title

Basic Analytical Techniques | Data Science With R Tutorial

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Basic Analytical Techniques Using R tools. After completing this course you will be able to:
1. Get a basic introduction to R
2. Understand exploration of data
3. Explore data using R
4. Visualize data using R
5. Understand diagnostic analytics
6. Implementing diagnostic analytics using R
7. Understand these concepts with the help of case studies
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âžˇď¸Ź About Caltech Post Graduate Program In Data Science
This Post Graduation in Data Science leverages the superiority of Caltech's academic eminence. The Data Science program covers critical Data Science topics like Python programming, R programming, Machine Learning, Deep Learning, and Data Visualization tools through an interactive learning model with live sessions by global practitioners and practical labs.
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- Descriptive Statistics
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- Supervised and Unsupervised Learning
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{'title': 'Basic Analytical Techniques | Data Science With R Tutorial', 'heatmap': [{'end': 811.448, 'start': 729.915, 'weight': 0.715}, {'end': 999.413, 'start': 931.741, 'weight': 1}, {'end': 1664.936, 'start': 1591.336, 'weight': 0.846}, {'end': 1930.102, 'start': 1858.473, 'weight': 0.71}, {'end': 3462.565, 'start': 3322.364, 'weight': 0.874}], 'summary': "This tutorial on data science with r covers r tools, programming, data exploration, visualization, and statistical analysis techniques. it includes examples such as calculating pearson's correlation coefficient, anova analysis, and statistical tests using real-world datasets, emphasizing key concepts, and providing practical insights.", 'chapters': [{'end': 258.536, 'segs': [{'end': 35.915, 'src': 'embed', 'start': 0.169, 'weight': 2, 'content': [{'end': 8.414, 'text': 'Hello and welcome to Lesson 3 of the Business Analytics Foundation with R Tools course offered by SimpliLearn.', 'start': 0.169, 'duration': 8.245}, {'end': 17.279, 'text': 'After completing this course, you will be able to get a basic introduction to R.', 'start': 9.895, 'duration': 7.384}, {'end': 21.061, 'text': 'understand exploration of data.', 'start': 17.279, 'duration': 3.782}, {'end': 23.983, 'text': 'explore data using R.', 'start': 21.061, 'duration': 2.922}, {'end': 25.603, 'text': 'visualize data using R.', 'start': 23.983, 'duration': 1.62}, {'end': 29.674, 'text': 'Understand diagnostic analytics.', 'start': 27.293, 'duration': 2.381}, {'end': 35.915, 'text': 'Implement diagnostics analytics using R.', 'start': 31.674, 'duration': 4.241}], 'summary': 'Learn r for data exploration, visualization, and diagnostics in business analytics.', 'duration': 35.746, 'max_score': 0.169, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c169.jpg'}, {'end': 135.249, 'src': 'embed', 'start': 70.152, 'weight': 0, 'content': [{'end': 75.315, 'text': 'It is mainly used in the fields of data mining and statistical analysis.', 'start': 70.152, 'duration': 5.163}, {'end': 86.04, 'text': 'Out of its various applications, it includes time series analysis, linear modeling, and nonlinear modeling.', 'start': 77.196, 'duration': 8.844}, {'end': 94.938, 'text': 'The main advantage of using R over other such tools for data mining is its active community,', 'start': 86.994, 'duration': 7.944}, {'end': 99.941, 'text': 'the built-in packages and the package contributions by the members of the community.', 'start': 94.938, 'duration': 5.003}, {'end': 106.885, 'text': 'Another reason for its popularity is that R needs very little programming knowledge.', 'start': 101.762, 'duration': 5.123}, {'end': 112.428, 'text': 'You can download R from its official website.', 'start': 109.406, 'duration': 3.022}, {'end': 124.117, 'text': 'r-project.org The website has instructions on how to download and install R and the basic machine requirements.', 'start': 113.585, 'duration': 10.532}, {'end': 131.164, 'text': 'RStudio is an IDE for programming in R that is freely available.', 'start': 125.358, 'duration': 5.806}, {'end': 135.249, 'text': 'It is completely optional for you to download RStudio.', 'start': 132.145, 'duration': 3.104}], 'summary': 'R is popular for data mining due to its active community, built-in packages, and low programming knowledge requirement.', 'duration': 65.097, 'max_score': 70.152, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c70152.jpg'}, {'end': 194.979, 'src': 'embed', 'start': 165.157, 'weight': 5, 'content': [{'end': 171.299, 'text': "In the next slide, let's start with a very basic introduction to the commands in R.", 'start': 165.157, 'duration': 6.142}, {'end': 183.382, 'text': 'Before getting into the specifics and statistical analysis in R, listed here are a few important commands that would be used throughout the course.', 'start': 171.299, 'duration': 12.083}, {'end': 190.884, 'text': 'To use a particular package, the install.packages function is used.', 'start': 185.262, 'duration': 5.622}, {'end': 194.979, 'text': 'This installation needs to be done only once.', 'start': 192.418, 'duration': 2.561}], 'summary': 'Intro to r commands: learn essential commands & use install.packages function for package installation.', 'duration': 29.822, 'max_score': 165.157, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c165157.jpg'}], 'start': 0.169, 'title': 'R tools and programming', 'summary': 'Covers the basics of r, data exploration, visualization, and diagnostic analytics, emphasizing the advantages of r for data mining due to its active community and built-in packages. it also introduces r programming, emphasizing its popularity, minimal programming knowledge required, and instructions on downloading and installation, along with basic commands and functionalities of r.', 'chapters': [{'end': 99.941, 'start': 0.169, 'title': 'Business analytics foundation with r tools', 'summary': 'Covers the basics of r, data exploration, visualization, and diagnostic analytics, emphasizing the advantages of r for data mining due to its active community and built-in packages.', 'duration': 99.772, 'highlights': ['R is a freely available programming language for statistical computations and graphics, widely used in data mining and statistical analysis, including time series analysis, linear modeling, and nonlinear modeling.', 'The main advantage of using R for data mining is its active community and the availability of built-in packages and package contributions by the members of the community.', 'Completing the course provides a basic introduction to R, understanding of data exploration, visualization, and diagnostic analytics using R tools.']}, {'end': 258.536, 'start': 101.762, 'title': 'Introduction to r programming', 'summary': 'Introduces r programming, emphasizing its popularity, minimal programming knowledge required, and instructions on downloading and installation, along with basic commands and functionalities of r.', 'duration': 156.774, 'highlights': ['R requires very little programming knowledge, making it popular among users.', 'Instructions on downloading and installing R and its basic machine requirements are available on the official website r-project.org.', 'RStudio is a freely available IDE for programming in R, and it is optional to download.', 'The chapter emphasizes using the R command line prompt for programming, with the option to use RStudio, and encourages utilizing community forums for support and exploration.', 'Important commands in R, such as install.packages, library, data, read, write, getwd, and setwd, are introduced for package installation, loading functions, data usage, file operations, and directory management.']}], 'duration': 258.367, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c169.jpg', 'highlights': ['R is a freely available programming language for statistical computations and graphics, widely used in data mining and statistical analysis, including time series analysis, linear modeling, and nonlinear modeling.', 'The main advantage of using R for data mining is its active community and the availability of built-in packages and package contributions by the members of the community.', 'Completing the course provides a basic introduction to R, understanding of data exploration, visualization, and diagnostic analytics using R tools.', 'R requires very little programming knowledge, making it popular among users.', 'Instructions on downloading and installing R and its basic machine requirements are available on the official website r-project.org.', 'Important commands in R, such as install.packages, library, data, read, write, getwd, and setwd, are introduced for package installation, loading functions, data usage, file operations, and directory management.']}, {'end': 754.906, 'segs': [{'end': 323.222, 'src': 'embed', 'start': 259.877, 'weight': 2, 'content': [{'end': 273.022, 'text': 'For example, read.csv of c colon front slash r tutorials front slash sample data dot csv.', 'start': 259.877, 'duration': 13.145}, {'end': 279.125, 'text': 'Note that r uses the forward slash for specifying directories.', 'start': 274.563, 'duration': 4.562}, {'end': 284.809, 'text': 'The assignment operator in R is different from the equal to operator.', 'start': 280.728, 'duration': 4.081}, {'end': 291.59, 'text': 'To assign a value to a variable in R, use the lesser than dash symbol.', 'start': 285.289, 'duration': 6.301}, {'end': 301.033, 'text': 'For help on any function in R, type question mark followed by the function name to open the R help text.', 'start': 293.651, 'duration': 7.382}, {'end': 307.194, 'text': 'If you are using R, the help text will open on the default browser.', 'start': 302.733, 'duration': 4.461}, {'end': 313.458, 'text': 'In the next slide, we will look at the basic exploration using R.', 'start': 308.976, 'duration': 4.482}, {'end': 323.222, 'text': "Before getting into the functions and commands for data exploration, let's look at how data is stored in R.", 'start': 313.458, 'duration': 9.764}], 'summary': 'R uses forward slash for directories. assignment in r uses <- symbol. use ?function_name for help in r.', 'duration': 63.345, 'max_score': 259.877, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c259877.jpg'}, {'end': 381.049, 'src': 'embed', 'start': 355.315, 'weight': 0, 'content': [{'end': 362.219, 'text': 'The IRIS data set is a very popular, commonly used data set introduced by Sir Donald Fisher.', 'start': 355.315, 'duration': 6.904}, {'end': 375.326, 'text': 'The data contains 150 entries belonging to three different species and includes features such as sepal length and width, and petal length and width.', 'start': 363.598, 'duration': 11.728}, {'end': 381.049, 'text': 'The three species have 50 entries each, as shown in the data frame.', 'start': 376.046, 'duration': 5.003}], 'summary': 'Iris data set has 150 entries, 3 species with 50 entries each.', 'duration': 25.734, 'max_score': 355.315, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c355315.jpg'}, {'end': 475.735, 'src': 'embed', 'start': 443.109, 'weight': 1, 'content': [{'end': 450.745, 'text': 'The number of rows is an optional argument, and the default number of rows is 6.', 'start': 443.109, 'duration': 7.636}, {'end': 454.506, 'text': 'To view the last few records, the TAIL function is used.', 'start': 450.745, 'duration': 3.761}, {'end': 457.568, 'text': 'The syntax is similar to the HEAD function.', 'start': 455.267, 'duration': 2.301}, {'end': 469.452, 'text': 'TAIL In the next slide, we will look at commands to view the dimensions of data.', 'start': 458.408, 'duration': 11.044}, {'end': 475.735, 'text': 'Listed here are a few commands to view the dimensions of a data set.', 'start': 471.073, 'duration': 4.662}], 'summary': 'Default number of rows is 6. tail function to view last records.', 'duration': 32.626, 'max_score': 443.109, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c443109.jpg'}], 'start': 259.877, 'title': 'R basics and data exploration', 'summary': 'Covers r basics, file reading, directory specification, variable assignment, accessing help on functions, and an introduction to data storage. it also introduces data frames in r, focusing on the iris dataset with 150 entries and explaining commands to view data.', 'chapters': [{'end': 323.222, 'start': 259.877, 'title': 'R tutorial: basics and data exploration', 'summary': 'Discusses r basics including file reading, directory specification, variable assignment, and accessing help on functions, as well as an introduction to data storage in r.', 'duration': 63.345, 'highlights': ['The assignment operator in R is different from the equal to operator. To assign a value to a variable in R, use the lesser than dash symbol.', 'For help on any function in R, type question mark followed by the function name to open the R help text. If you are using R, the help text will open on the default browser.', 'Note that R uses the forward slash for specifying directories.', "Before getting into the functions and commands for data exploration, let's look at how data is stored in R."]}, {'end': 754.906, 'start': 323.222, 'title': 'Viewing and exploring data in r', 'summary': 'Introduces data frames in r, focuses on the popular iris dataset with 150 entries and features such as sepal and petal dimensions, and explains commands to view data, including dimensions, attributes, columnar, and row-wise data.', 'duration': 431.684, 'highlights': ['The IRIS dataset contains 150 entries belonging to three different species, each with 50 entries, and includes features such as sepal and petal dimensions. The IRIS dataset is a popular dataset with 150 entries belonging to three different species and includes features such as sepal length and width, and petal length and width.', 'Commands to view data in R include functions to view the top and last few records, as well as to view the dimensions and attributes of the dataset. Commands to view data in R include functions to view the top and last few records, as well as to view the dimensions and attributes of the dataset.', 'The class command is used to display the data type of the dataset and its columns, and viewing columnar data can be done using different notations, such as Dataset$COLUMNNAME or Dataset[, column name/number]. The class command is used to display the data type of the dataset and its columns, and viewing columnar data can be done using different notations, such as Dataset$COLUMNNAME or Dataset[, column name/number].']}], 'duration': 495.029, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c259877.jpg', 'highlights': ['The IRIS dataset contains 150 entries belonging to three different species, each with 50 entries, and includes features such as sepal and petal dimensions.', 'Commands to view data in R include functions to view the top and last few records, as well as to view the dimensions and attributes of the dataset.', 'The assignment operator in R is different from the equal to operator. To assign a value to a variable in R, use the lesser than dash symbol.', 'For help on any function in R, type question mark followed by the function name to open the R help text.']}, {'end': 2280.837, 'segs': [{'end': 821.756, 'src': 'embed', 'start': 788.168, 'weight': 0, 'content': [{'end': 792.251, 'text': 'Data of IRIS loads the IRIS data set onto the workspace.', 'start': 788.168, 'duration': 4.083}, {'end': 797.056, 'text': 'Step 5.', 'start': 795.495, 'duration': 1.561}, {'end': 800.819, 'text': 'Now we shall do some basic analysis on R.', 'start': 797.056, 'duration': 3.763}, {'end': 811.448, 'text': 'Just to note that before doing any analysis, it is safe to save the dataset onto the workspace that we try to use by using the attach function.', 'start': 800.819, 'duration': 10.629}, {'end': 816.632, 'text': 'So attach of iris will save the dataset onto the workspace.', 'start': 812.409, 'duration': 4.223}, {'end': 821.756, 'text': 'Step 6.', 'start': 820.135, 'duration': 1.621}], 'summary': 'Iris dataset loaded for basic analysis on r.', 'duration': 33.588, 'max_score': 788.168, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c788168.jpg'}, {'end': 1015.182, 'src': 'heatmap', 'start': 931.741, 'weight': 2, 'content': [{'end': 940.703, 'text': 'The next function that we are going to look into is the read function in R, along with the type of files that can be read.', 'start': 931.741, 'duration': 8.962}, {'end': 946.684, 'text': 'Step 1.', 'start': 943.583, 'duration': 3.101}, {'end': 956.986, 'text': 'Read.csv helps us to load a CSV file, the path for which we need to pass as argument to this function.', 'start': 946.684, 'duration': 10.302}, {'end': 966.843, 'text': 'We load the dataset stored in the file into a user variable and then check the structure using the head function.', 'start': 958.578, 'duration': 8.265}, {'end': 971.867, 'text': 'Step 2.', 'start': 969.925, 'duration': 1.942}, {'end': 983.094, 'text': 'We can even make a data frame of this data by using the data.frame function and passing the red.csv as an argument,', 'start': 971.867, 'duration': 11.227}, {'end': 987.957, 'text': 'or we can even pass the user variable that stores the data as an argument.', 'start': 983.094, 'duration': 4.863}, {'end': 993.429, 'text': 'Step 3.', 'start': 991.468, 'duration': 1.961}, {'end': 999.413, 'text': 'Just like the head function, we have the tail function that shows the last six records of a dataset.', 'start': 993.429, 'duration': 5.984}, {'end': 1003.795, 'text': 'Tail of the dataset returns those records.', 'start': 1000.733, 'duration': 3.062}, {'end': 1008.978, 'text': 'Step 4.', 'start': 1007.157, 'duration': 1.821}, {'end': 1015.182, 'text': 'We can display as many records by passing the required number as an argument after the dataset name.', 'start': 1008.978, 'duration': 6.204}], 'summary': 'In r, the read function can load csv files using read.csv and display dataset structure using head and tail functions.', 'duration': 146.541, 'max_score': 931.741, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c931741.jpg'}, {'end': 1214.909, 'src': 'embed', 'start': 1188.424, 'weight': 6, 'content': [{'end': 1195.11, 'text': 'The summary function is a generic function in R that displays summaries of data or models.', 'start': 1188.424, 'duration': 6.686}, {'end': 1197.982, 'text': 'as we will see in later chapters.', 'start': 1196.081, 'duration': 1.901}, {'end': 1202.083, 'text': 'The syntax is summary of data set.', 'start': 1198.802, 'duration': 3.281}, {'end': 1204.765, 'text': 'As shown in the screenshot.', 'start': 1202.844, 'duration': 1.921}, {'end': 1214.909, 'text': 'the summary function displays the minimum value, maximum value mean, median and first and third qualities of every numeric data.', 'start': 1204.765, 'duration': 10.144}], 'summary': 'The summary function in r displays min, max, mean, median, and quartiles of numeric data.', 'duration': 26.485, 'max_score': 1188.424, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c1188424.jpg'}, {'end': 1300.334, 'src': 'embed', 'start': 1271.53, 'weight': 7, 'content': [{'end': 1275.995, 'text': 'The summary function displays all the summary statistics for a particular data.', 'start': 1271.53, 'duration': 4.465}, {'end': 1281.141, 'text': 'Here you can see a list of commands to display individual summary statistics.', 'start': 1276.616, 'duration': 4.525}, {'end': 1288.248, 'text': 'The argument for each function is the column name for which the statistics is to be obtained.', 'start': 1282.302, 'duration': 5.946}, {'end': 1292.914, 'text': 'The commands are min to get the minimum value.', 'start': 1288.909, 'duration': 4.005}, {'end': 1296.933, 'text': 'Max to get the maximum value.', 'start': 1294.412, 'duration': 2.521}, {'end': 1300.334, 'text': 'Range to get the range of the data.', 'start': 1297.493, 'duration': 2.841}], 'summary': 'Summary function displays stats for data; uses min, max, range commands.', 'duration': 28.804, 'max_score': 1271.53, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c1271530.jpg'}, {'end': 1581.782, 'src': 'embed', 'start': 1555.399, 'weight': 8, 'content': [{'end': 1561.565, 'text': 'Aggregating the data set is one of the few important data manipulation functions in R.', 'start': 1555.399, 'duration': 6.166}, {'end': 1570.412, 'text': 'Parameters that need to be passed into the aggregate function are formula for the aggregation, data set used, and the type of aggregation.', 'start': 1561.565, 'duration': 8.847}, {'end': 1575.417, 'text': 'That is, a mean aggregation or a sum aggregation.', 'start': 1571.513, 'duration': 3.904}, {'end': 1581.782, 'text': 'Aggregation in R can be done using the aggregate function in two ways.', 'start': 1577.378, 'duration': 4.404}], 'summary': 'Aggregating data in r using aggregate function with formula, data set, and type of aggregation.', 'duration': 26.383, 'max_score': 1555.399, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c1555399.jpg'}, {'end': 1664.936, 'src': 'heatmap', 'start': 1591.336, 'weight': 0.846, 'content': [{'end': 1595.719, 'text': 'In the first way, we do it across the dataset by a particular column.', 'start': 1591.336, 'duration': 4.383}, {'end': 1600.764, 'text': 'Like the IRIS dataset, we can aggregate across the dataset by species.', 'start': 1596.72, 'duration': 4.044}, {'end': 1605.357, 'text': 'Step 2.', 'start': 1603.996, 'duration': 1.361}, {'end': 1613.122, 'text': 'For this scenario, we enter the formula as dot, tilde, followed by the column name, which is species in our case.', 'start': 1605.357, 'duration': 7.765}, {'end': 1622.948, 'text': 'The enter data equals iris for specifying the data set, and last give the type of aggregation, that is mean or sum.', 'start': 1614.543, 'duration': 8.405}, {'end': 1627.865, 'text': 'Step 3.', 'start': 1626.505, 'duration': 1.36}, {'end': 1634.908, 'text': 'In the second way only the formula differs such that the aggregation is not done across the data set,', 'start': 1627.865, 'duration': 7.043}, {'end': 1638.969, 'text': 'but only on the variables or columns used in the example.', 'start': 1634.908, 'duration': 4.061}, {'end': 1650.172, 'text': 'With this, we come to the end of the basic operations that can be performed in R, right from data viewing to data manipulation to basic statistics.', 'start': 1640.369, 'duration': 9.803}, {'end': 1654.394, 'text': "Next, we'll focus on the visual aspects of R.", 'start': 1651.133, 'duration': 3.261}, {'end': 1664.936, 'text': 'In the next slide, we will look at the functions to visualize data in R.', 'start': 1656.368, 'duration': 8.568}], 'summary': 'Aggregating data in r by species using mean or sum, and moving on to visual aspects.', 'duration': 73.6, 'max_score': 1591.336, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c1591336.jpg'}, {'end': 1838.389, 'src': 'embed', 'start': 1792.827, 'weight': 9, 'content': [{'end': 1799.271, 'text': 'The plot function can be used to create scatter plots of one variable against another.', 'start': 1792.827, 'duration': 6.444}, {'end': 1804.294, 'text': 'For example, let us plot sepal length against species.', 'start': 1800.39, 'duration': 3.904}, {'end': 1810.201, 'text': 'We will use a few optional attributes of the plot function.', 'start': 1804.975, 'duration': 5.226}, {'end': 1812.823, 'text': 'to specify the title of the plot.', 'start': 1810.821, 'duration': 2.002}, {'end': 1815.066, 'text': 'the x-axis label.', 'start': 1812.844, 'duration': 2.222}, {'end': 1817.208, 'text': 'the y-axis label.', 'start': 1815.627, 'duration': 1.581}, {'end': 1838.389, 'text': 'The function would now be plot of iris$sepal.length comma iris$species comma main equals irisdata.', 'start': 1823.066, 'duration': 15.323}], 'summary': 'Using plot function to create scatter plots of sepal length against species with optional attributes: title, x-axis label, y-axis label', 'duration': 45.562, 'max_score': 1792.827, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c1792827.jpg'}, {'end': 1930.102, 'src': 'heatmap', 'start': 1858.473, 'weight': 0.71, 'content': [{'end': 1860.993, 'text': 'Next, we will look at pie charts.', 'start': 1858.473, 'duration': 2.52}, {'end': 1871.255, 'text': 'Pie charts are the simplest form of visualizing the numerical proportion of the different classes through the sectors of the circle.', 'start': 1862.594, 'duration': 8.661}, {'end': 1876.276, 'text': 'The pie function is used to create pie charts in R.', 'start': 1872.375, 'duration': 3.901}, {'end': 1886.298, 'text': 'The table function is used to create a frequency table, and then the pi function is called to create a chart of the table.', 'start': 1877.691, 'duration': 8.607}, {'end': 1893.424, 'text': 'The main attribute, as mentioned before, is used to specify the title for the chart.', 'start': 1887.559, 'duration': 5.865}, {'end': 1899.009, 'text': 'Here is an example chart showing the different species of iris data.', 'start': 1895.226, 'duration': 3.783}, {'end': 1904.594, 'text': 'The circle is divided into three equal sectors for the three species.', 'start': 1900.53, 'duration': 4.064}, {'end': 1909.438, 'text': 'In the next slide, we will look at bar charts.', 'start': 1906.437, 'duration': 3.001}, {'end': 1919.182, 'text': "Bar plots are used to depict values in vertical manner, with the height being equivalent to the value that's being shown.", 'start': 1911.099, 'duration': 8.083}, {'end': 1926.546, 'text': 'For this example, we will use another built-in dataset, U.S. Personal Expenditure.', 'start': 1920.583, 'duration': 5.963}, {'end': 1930.102, 'text': 'the data set is displayed in the table.', 'start': 1927.54, 'duration': 2.562}], 'summary': 'Pie charts visualize proportion of classes; bar plots depict values in vertical manner.', 'duration': 71.629, 'max_score': 1858.473, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c1858473.jpg'}, {'end': 1899.009, 'src': 'embed', 'start': 1872.375, 'weight': 10, 'content': [{'end': 1876.276, 'text': 'The pie function is used to create pie charts in R.', 'start': 1872.375, 'duration': 3.901}, {'end': 1886.298, 'text': 'The table function is used to create a frequency table, and then the pi function is called to create a chart of the table.', 'start': 1877.691, 'duration': 8.607}, {'end': 1893.424, 'text': 'The main attribute, as mentioned before, is used to specify the title for the chart.', 'start': 1887.559, 'duration': 5.865}, {'end': 1899.009, 'text': 'Here is an example chart showing the different species of iris data.', 'start': 1895.226, 'duration': 3.783}], 'summary': 'R pie function creates pie charts, table function for frequency tables, main attribute used for chart title.', 'duration': 26.634, 'max_score': 1872.375, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c1872375.jpg'}, {'end': 1995.491, 'src': 'embed', 'start': 1964.351, 'weight': 11, 'content': [{'end': 1967.513, 'text': 'In the next slide, we will create box plots.', 'start': 1964.351, 'duration': 3.162}, {'end': 1973.477, 'text': 'Box plots are used to show numerical data with their quartile ranges.', 'start': 1969.214, 'duration': 4.263}, {'end': 1979.981, 'text': 'Also called a box whisker plot, the boxes show the interquartile region.', 'start': 1974.458, 'duration': 5.523}, {'end': 1983.223, 'text': 'with the middle line equal to the median.', 'start': 1980.882, 'duration': 2.341}, {'end': 1987.406, 'text': 'The whiskers show the lower and upper quartiles.', 'start': 1984.824, 'duration': 2.582}, {'end': 1990.688, 'text': 'And the points show the outliers.', 'start': 1988.707, 'duration': 1.981}, {'end': 1995.491, 'text': 'The box plots are very useful in detecting the outliers.', 'start': 1991.569, 'duration': 3.922}], 'summary': 'Creating box plots to show numerical data with quartile ranges and outliers, useful for outlier detection.', 'duration': 31.14, 'max_score': 1964.351, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c1964351.jpg'}, {'end': 2059.001, 'src': 'embed', 'start': 2027.362, 'weight': 12, 'content': [{'end': 2031.465, 'text': 'You can see that there is an outlier in the Virginica species.', 'start': 2027.362, 'duration': 4.103}, {'end': 2036.77, 'text': 'In the next slide, we will look at histograms.', 'start': 2033.267, 'duration': 3.503}, {'end': 2042.948, 'text': 'Histograms are used to depict frequency distribution data.', 'start': 2038.985, 'duration': 3.963}, {'end': 2050.333, 'text': 'R has a default islands data set that is best suited to create histograms.', 'start': 2044.51, 'duration': 5.823}, {'end': 2059.001, 'text': 'Once you are done with this slide, you are encouraged to try creating a histogram using the islands data.', 'start': 2052.036, 'duration': 6.965}], 'summary': "An outlier is observed in the virginica species; histograms depict frequency distribution data, with r's islands data set best suited for this.", 'duration': 31.639, 'max_score': 2027.362, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c2027362.jpg'}, {'end': 2161.619, 'src': 'embed', 'start': 2127.447, 'weight': 13, 'content': [{'end': 2133.989, 'text': 'Basic data visualization in R can be done using the plot function.', 'start': 2127.447, 'duration': 6.542}, {'end': 2137.431, 'text': 'Step 1.', 'start': 2135.99, 'duration': 1.441}, {'end': 2146.574, 'text': 'When we do a plot of the iris data set, a separate window pops up showing a scatter plot across each and every variable of the data set.', 'start': 2137.431, 'duration': 9.143}, {'end': 2151.333, 'text': 'Step 2.', 'start': 2149.952, 'duration': 1.381}, {'end': 2161.619, 'text': 'If we want a plot of any particular variable or set of variables, then we pass the variable or column names as argument to the plot function.', 'start': 2151.333, 'duration': 10.286}], 'summary': 'Basic data visualization in r using plot function for iris dataset.', 'duration': 34.172, 'max_score': 2127.447, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c2127447.jpg'}], 'start': 754.906, 'title': 'R data analysis and visualization', 'summary': 'Covers loading the iris data set, basic analysis, managing workspace, reading and manipulating data, summary functions, aggregation, and visualization techniques in r, including scatter plots, pie charts, bar plots, box plots, and histograms.', 'chapters': [{'end': 906.084, 'start': 754.906, 'title': 'Loading and analyzing data in r', 'summary': 'Covers the process of loading the iris data set, conducting basic analysis in r, and managing the workspace, including loading, checking the structure, and clearing the screen.', 'duration': 151.178, 'highlights': ['Loading the IRIS data set using the DATA function is a crucial step in the process, providing the foundation for the subsequent analysis.', 'Using the attach function to save the dataset onto the workspace ensures easy access and manipulation during analysis.', 'Clearing the screen with the Control-L keyboard shortcut is a convenient way to manage the workspace and maintain a clear view of the data.']}, {'end': 1160.983, 'start': 906.084, 'title': 'R data analysis basics', 'summary': 'Covers functions to check and change the working directory, reading and manipulating data in r, including loading csv files, creating data frames, viewing subsets of the data, and accessing column and row data.', 'duration': 254.899, 'highlights': ['The function getwd returns the current working directory, while setwd is used to change the working directory by passing the new directory as an argument within double quotes.', 'Using read.csv allows loading a CSV file, and the head function helps in checking the structure of the dataset, while the data.frame function can be used to create a data frame from the loaded data or a user variable.', 'The tail function shows the last six records of a dataset, and additional records can be displayed by passing the required number as an argument after the dataset name.', 'The dim function returns the total number of rows and columns in the dataset, ncall function returns the total number of columns, nrow function returns the total number of rows, and the names function returns the titles of all the columns in the dataset.', 'Column data can be viewed in three different ways: by calling the column name after the dataset name using the dollar sign, as a matrix attribute of the dataset within square brackets, or similarly within the square brackets but with the column name in double quotes.']}, {'end': 1962.59, 'start': 1160.983, 'title': 'R data summary & visualization', 'summary': 'Covers the summary function in r, which displays minimum, maximum, mean, median, and frequency distribution for numeric and categorical data, and also explores the aggregation function and visualization techniques including scatter plots, pie charts, and bar plots.', 'duration': 801.607, 'highlights': ["The summary function in R displays minimum, maximum, mean, median, and frequency distribution for numeric and categorical data. The summary function in R displays minimum, maximum, mean, median, and frequency distribution for numeric and categorical data. For example, 'Table of Iris$Species' displays a frequency distribution of the three different classes.", 'Commands like min, max, range, mean, median, IQR, SD, and var can be used to obtain individual summary statistics for a specific column in the dataset. Commands like min, max, range, mean, median, IQR, SD, and var can be used to obtain individual summary statistics for a specific column in the dataset.', 'The aggregate function in R is used to group and summarize data, with options for mean or sum aggregation. The aggregate function in R is used to group and summarize data, with options for mean or sum aggregation. It can be used to aggregate across the dataset by a particular column, or only on the variables or columns used in the example.', 'The plot function in R is used to create scatter plots, with the ability to plot a variety of graphs on different types of data. The plot function in R is used to create scatter plots, with the ability to plot a variety of graphs on different types of data. It can also be used to create other types of plots such as pairwise data plots and pie charts.', 'Pie charts and bar plots can be created in R using the pie and BarPlot functions, respectively, to visualize numerical proportions and categorical data. Pie charts and bar plots can be created in R using the pie and BarPlot functions, respectively, to visualize numerical proportions and categorical data.']}, {'end': 2280.837, 'start': 1964.351, 'title': 'R data visualization', 'summary': 'Introduces box plots, histograms, and basic data visualization in r, demonstrating how to create them using functions like boxplot, hist, and plot, with examples and attributes for each plot type.', 'duration': 316.486, 'highlights': ['Box plots are used to show numerical data with quartile ranges, and in R, they can be created using the boxplot function, allowing for the detection of outliers.', 'Histograms are used to depict frequency distribution data, and in R, they can be created using the hist function, where the data is put into buckets and the histograms are created.', 'Basic data visualization in R can be done using the plot function, which can generate scatter plots, pie charts, and barplot charts using different variables and data sets.', 'The boxplot function in R requires passing a formula specifying the variables to be plotted, along with the data, to generate the required box plot.', 'The hist function in R requires passing the variable that needs to be plotted to generate a histogram plot.']}], 'duration': 1525.931, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c754906.jpg', 'highlights': ['Loading the IRIS data set using the DATA function is crucial for subsequent analysis.', 'Using the attach function ensures easy access and manipulation of the dataset.', 'Clearing the screen with the Control-L keyboard shortcut helps manage the workspace.', 'The function getwd returns the current working directory, while setwd is used to change it.', 'Using read.csv allows loading a CSV file, and the head function helps in checking the structure of the dataset.', 'The tail function shows the last six records of a dataset, and additional records can be displayed.', 'The summary function in R displays minimum, maximum, mean, median, and frequency distribution for numeric and categorical data.', 'Commands like min, max, range, mean, median, IQR, SD, and var can be used to obtain individual summary statistics for a specific column.', 'The aggregate function in R is used to group and summarize data, with options for mean or sum aggregation.', 'The plot function in R is used to create scatter plots and other types of graphs on different types of data.', 'Pie charts and bar plots can be created in R using the pie and BarPlot functions, respectively.', 'Box plots are used to show numerical data with quartile ranges, and in R, they can be created using the boxplot function.', 'Histograms are used to depict frequency distribution data, and in R, they can be created using the hist function.', 'Basic data visualization in R can be done using the plot function, which can generate scatter plots, pie charts, and barplot charts.']}, {'end': 3633.482, 'segs': [{'end': 2384.029, 'src': 'embed', 'start': 2281.457, 'weight': 0, 'content': [{'end': 2294.319, 'text': 'We can fine-tune this plot by passing the main chart title x-axis titled and y-axis titled using the main xlab and ylab arguments in the plot function.', 'start': 2281.457, 'duration': 12.862}, {'end': 2297.9, 'text': 'And this can be done in case of all other plots.', 'start': 2295.059, 'duration': 2.841}, {'end': 2308.04, 'text': 'In the next slide, we will look at a case study to understand what we have learned so far.', 'start': 2302.495, 'duration': 5.545}, {'end': 2318.309, 'text': "Let's now take a banking-related data set as an example for doing data exploration.", 'start': 2312.003, 'duration': 6.306}, {'end': 2327.897, 'text': 'Officer David from the sales department has been asked to do a study on the overall performance of a bank by analyzing the bank loans.', 'start': 2319.169, 'duration': 8.728}, {'end': 2340.314, 'text': 'As part of the investigation, David has asked his colleague to categorize the loans as seasoned or bad loans with respect to age category.', 'start': 2329.565, 'duration': 10.749}, {'end': 2344.397, 'text': 'The following data categories have been captured.', 'start': 2341.495, 'duration': 2.902}, {'end': 2359.844, 'text': 'Age group, total number of loans, number of good loans, number of bad loans, percentage of good loans, percentage of bad loans.', 'start': 2345.819, 'duration': 14.025}, {'end': 2364.845, 'text': 'In the next slide, we will have a look at the data.', 'start': 2361.585, 'duration': 3.26}, {'end': 2369.926, 'text': 'Here you can see a table of the bank loan data.', 'start': 2364.865, 'duration': 5.061}, {'end': 2379.148, 'text': 'As mentioned in the previous slide, the number of good loans and bad loans has been tabulated as per the age category.', 'start': 2370.786, 'duration': 8.362}, {'end': 2384.029, 'text': 'There are 15 categories of age in the table.', 'start': 2380.268, 'duration': 3.761}], 'summary': 'Data exploration of banking-related data, analyzing performance of bank loans, with 15 age categories.', 'duration': 102.572, 'max_score': 2281.457, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c2281457.jpg'}, {'end': 2785.663, 'src': 'embed', 'start': 2744.784, 'weight': 5, 'content': [{'end': 2749.688, 'text': 'You can see that Toyota Corolla has the maximum mileage in the given set of cars.', 'start': 2744.784, 'duration': 4.904}, {'end': 2754.852, 'text': 'Step 10.', 'start': 2753.211, 'duration': 1.641}, {'end': 2760.897, 'text': 'Similarly, the row with the minimum value can be found using the which.min function.', 'start': 2754.852, 'duration': 6.045}, {'end': 2765.805, 'text': 'Let us find the lightest car in the given set of cars.', 'start': 2762.642, 'duration': 3.163}, {'end': 2774.634, 'text': 'Type mtcars of which dot min of mtcars dollar wt in a comma.', 'start': 2768.148, 'duration': 6.486}, {'end': 2780.38, 'text': 'We now know that the Lotus Europa is the lightest of the cars.', 'start': 2776.496, 'duration': 3.884}, {'end': 2785.663, 'text': 'Step 11.', 'start': 2783.964, 'duration': 1.699}], 'summary': 'Toyota corolla has the maximum mileage and lotus europa is the lightest car.', 'duration': 40.879, 'max_score': 2744.784, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c2744784.jpg'}, {'end': 2894.024, 'src': 'embed', 'start': 2842.369, 'weight': 3, 'content': [{'end': 2847.433, 'text': 'we can infer that the cars with three gears have a mileage of 16 on average.', 'start': 2842.369, 'duration': 5.064}, {'end': 2853.897, 'text': 'The cars with four gears are the best in terms of mileage, giving the highest average mileage.', 'start': 2848.413, 'duration': 5.484}, {'end': 2862.504, 'text': 'The aggregate function can similarly be used to find the average speed of manual and automatic transmission vehicles and so on.', 'start': 2854.938, 'duration': 7.566}, {'end': 2872.752, 'text': 'Following mathematical exploration techniques, we will now visualize data and try to support the inferences made through the earlier commands.', 'start': 2864.09, 'duration': 8.662}, {'end': 2877.553, 'text': 'Step 1.', 'start': 2876.113, 'duration': 1.44}, {'end': 2883.234, 'text': 'Let us start with a scatter plot that includes all the factors in the data set being plotted against each other.', 'start': 2877.553, 'duration': 5.681}, {'end': 2885.995, 'text': 'Type plot and hit Enter.', 'start': 2884.074, 'duration': 1.921}, {'end': 2894.024, 'text': 'The new window shows a plot with all the variables plotted against every other variable in the data set.', 'start': 2888.459, 'duration': 5.565}], 'summary': 'Cars with four gears have the highest average mileage at 16, using aggregate function and visualization techniques for analysis.', 'duration': 51.655, 'max_score': 2842.369, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c2842369.jpg'}, {'end': 3034.39, 'src': 'embed', 'start': 3006.712, 'weight': 7, 'content': [{'end': 3013.375, 'text': 'We have the AM variable, which has two values, 0 for automatic and 1 for manual transmissions.', 'start': 3006.712, 'duration': 6.663}, {'end': 3019.345, 'text': 'The summary statistic will show us the number of automatic and manual cars.', 'start': 3015.083, 'duration': 4.262}, {'end': 3025.407, 'text': 'But if we have thousands of data, it will be difficult to obtain a ratio of the two categories.', 'start': 3020.005, 'duration': 5.402}, {'end': 3032.029, 'text': 'You can graphically view the ratio using the pie charts, as seen in the previous lessons.', 'start': 3026.627, 'duration': 5.402}, {'end': 3034.39, 'text': 'We will use the pie function.', 'start': 3032.83, 'duration': 1.56}], 'summary': 'Analyzing am variable to show automatic and manual car ratio using pie charts.', 'duration': 27.678, 'max_score': 3006.712, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c3006712.jpg'}, {'end': 3177.215, 'src': 'embed', 'start': 3151.125, 'weight': 8, 'content': [{'end': 3163.575, 'text': 'The formula for boxplot will hence be HP tilde SIL and mention data equals empty cars and pass them as arguments for the boxplot function.', 'start': 3151.125, 'duration': 12.45}, {'end': 3169.48, 'text': 'You can see that the horsepower increases as the number of cylinders increase.', 'start': 3165.256, 'duration': 4.224}, {'end': 3177.215, 'text': 'For cars with eight cylinders, there is a lot of variation as can be seen from the wider box and whiskers for the plot.', 'start': 3170.572, 'duration': 6.643}], 'summary': 'Boxplot shows horsepower increase with cylinder count, especially for eight-cylinder cars.', 'duration': 26.09, 'max_score': 3151.125, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c3151125.jpg'}, {'end': 3244.282, 'src': 'embed', 'start': 3210.753, 'weight': 9, 'content': [{'end': 3220.516, 'text': 'To know this, use table of empty cars dollar gear to create a table of the counts, and pass this as an argument to the bar plot function.', 'start': 3210.753, 'duration': 9.763}, {'end': 3232.935, 'text': 'From the plot, we can find that maximum number of cars, that is 15 cars, have 3 gears, and 12 cars have 4 gears, and 5 cars have 5 gears.', 'start': 3221.889, 'duration': 11.046}, {'end': 3237.378, 'text': 'Similarly, this can be done for the number of cylinders.', 'start': 3234.116, 'duration': 3.262}, {'end': 3244.282, 'text': 'Type barplot of table of empty cars $SIL to view the plot.', 'start': 3238.018, 'duration': 6.264}], 'summary': 'Using bar plot, 15 cars have 3 gears, 12 cars have 4 gears, and 5 cars have 5 gears.', 'duration': 33.529, 'max_score': 3210.753, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c3210753.jpg'}, {'end': 3310.017, 'src': 'embed', 'start': 3275.93, 'weight': 10, 'content': [{'end': 3284.797, 'text': 'The histogram divides the horsepower into buckets of 50 units, starting at 50 and ending at 350, thus having six bars.', 'start': 3275.93, 'duration': 8.867}, {'end': 3297.787, 'text': 'We can see that most cars fall into the 100 to 150 horsepower range, and only two cars have horsepower of above 250.', 'start': 3285.478, 'duration': 12.309}, {'end': 3301.59, 'text': 'This concludes the case study on descriptive statistics using R.', 'start': 3297.787, 'duration': 3.803}, {'end': 3310.017, 'text': 'In the next slide, we will look at the function to test the correlation between two variables.', 'start': 3302.972, 'duration': 7.045}], 'summary': 'Histogram shows most cars have 100-150 horsepower; only 2 cars have over 250.', 'duration': 34.087, 'max_score': 3275.93, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c3275930.jpg'}, {'end': 3462.565, 'src': 'heatmap', 'start': 3322.364, 'weight': 0.874, 'content': [{'end': 3327.808, 'text': 'For example, is there a correlation between the height of parents and their offspring?', 'start': 3322.364, 'duration': 5.444}, {'end': 3336.346, 'text': 'In the example given, we try to find if there is a correlation between the sepal length and width of a flower.', 'start': 3329.423, 'duration': 6.923}, {'end': 3345.089, 'text': 'In R, correlation can be calculated using the core.test function.', 'start': 3338.807, 'duration': 6.282}, {'end': 3351.292, 'text': "By default, the function calculates the Pearson's correlation coefficient.", 'start': 3345.95, 'duration': 5.342}, {'end': 3355.894, 'text': "Let's look at how to interpret the results.", 'start': 3353.513, 'duration': 2.381}, {'end': 3362.039, 'text': 'The output shows the correlation method used and the data.', 'start': 3357.537, 'duration': 4.502}, {'end': 3374.303, 'text': 'The important output for this test is the p-value, which is calculated using the t-statistics and degrees of freedom.', 'start': 3364.3, 'duration': 10.003}, {'end': 3386.128, 'text': 'As seen in earlier chapters, if the p-value is less than 0.05, we can conclude that the null hypothesis is rejected.', 'start': 3376.044, 'duration': 10.084}, {'end': 3390.987, 'text': 'That is, there is no correlation between the two variables.', 'start': 3387.386, 'duration': 3.601}, {'end': 3400.35, 'text': 'The correlation coefficient is given as 0.1175698.', 'start': 3392.368, 'duration': 7.982}, {'end': 3406.993, 'text': 'This means there might be a negative correlation between the two variables.', 'start': 3400.35, 'duration': 6.643}, {'end': 3413.935, 'text': 'But since the p-value is quite high, we conclude that the result is not significant.', 'start': 3408.353, 'duration': 5.582}, {'end': 3418.016, 'text': 'That is, the correlation is almost zero.', 'start': 3414.994, 'duration': 3.022}, {'end': 3427.364, 'text': 'Next, we will look at a video to understand correlation using R.', 'start': 3420.258, 'duration': 7.106}, {'end': 3437.712, 'text': 'In this video, we perform a correlation test on two variables on a data set to ascertain the significance of the relation in values of the variables.', 'start': 3427.364, 'duration': 10.348}, {'end': 3445.554, 'text': 'We will use the anorexia data set from the mass package for the correlation.', 'start': 3439.81, 'duration': 5.744}, {'end': 3452.338, 'text': 'Load the package by typing library of mass.', 'start': 3448.916, 'duration': 3.422}, {'end': 3455.14, 'text': 'Specify mass in capital letters.', 'start': 3452.858, 'duration': 2.282}, {'end': 3462.565, 'text': 'We will use two variables of the data set to perform the test.', 'start': 3458.762, 'duration': 3.803}], 'summary': 'Correlation analysis in r with an example using sepal measurements, finding a non-significant correlation coefficient of 0.1175698.', 'duration': 140.201, 'max_score': 3322.364, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c3322364.jpg'}, {'end': 3589.632, 'src': 'embed', 'start': 3560.443, 'weight': 11, 'content': [{'end': 3567.169, 'text': 'Here, sales represents the value of total sales that happens in the ice cream company each day.', 'start': 3560.443, 'duration': 6.726}, {'end': 3576.026, 'text': 'and temperature represents the temperature that is being recorded for the corresponding day, measured in Celsius.', 'start': 3568.823, 'duration': 7.203}, {'end': 3586.05, 'text': 'We will be using correlation to check whether there is any relationship between sales and temperature for this company,', 'start': 3577.687, 'duration': 8.363}, {'end': 3589.632, 'text': 'and the following video will walk you through the process.', 'start': 3586.05, 'duration': 3.582}], 'summary': 'Sales and temperature correlation analysis for ice cream company.', 'duration': 29.189, 'max_score': 3560.443, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c3560443.jpg'}], 'start': 2281.457, 'title': 'R data analysis and visualization', 'summary': 'Discusses analyzing bank loan data with 15 age categories and techniques for data analysis in r, including exploring data frame attributes, summarizing statistics, and visualizing data using various techniques like scatter plots, pie charts, and correlation tests.', 'chapters': [{'end': 2412.502, 'start': 2281.457, 'title': 'Analyzing bank loan data', 'summary': "Discusses fine-tuning plot features and presents a case study involving officer david analyzing a bank's loan performance, including categorizing loans and examining loan data with 15 age categories.", 'duration': 131.045, 'highlights': ["Officer David conducts a study on a bank's overall performance by categorizing loans into seasoned or bad loans based on age category, capturing data such as total number of loans, number of good and bad loans, and their percentages.", 'The chapter covers fine-tuning plot features by passing main chart titles for x-axis and y-axis using xlab and ylab arguments in the plot function for all other plots.', 'A table of bank loan data is presented, including 15 age categories and the corresponding number of good and bad loans, along with the percentage of good and bad loans for each category.']}, {'end': 2862.504, 'start': 2413.123, 'title': 'R data analysis techniques', 'summary': 'Covers techniques for data analysis in r, including exploring data frame attributes, summarizing statistics, and finding maximum and minimum values for variables, providing insights into the dataset.', 'duration': 449.381, 'highlights': ['The aggregate function can be used to find the average mileage of cars with three gears, which is 16 on average, and the cars with four gears have the highest average mileage. Using the aggregate function to aggregate the MPG variable by the variable gear, the output shows that cars with three gears have an average mileage of 16, while cars with four gears have the highest average mileage.', 'The maximum miles per gallon is 33.90, and the average is 20.09, with 19 automatic and 13 manual cars out of 32 in the data set. The summary statistics show that the maximum miles per gallon is 33.90, the average is 20.09, and for the AM column, 19 cars are automatic and 13 are manual out of 32 cars in the data set.', 'The lightest car in the dataset is the Lotus Europa, found using the which.min function. Using the which.min function with the mtcars$wt variable, it is found that the Lotus Europa is the lightest car in the dataset.']}, {'end': 3633.482, 'start': 2864.09, 'title': 'Visualizing data in r', 'summary': 'Explains the visualization techniques in r, including scatter plots, pie charts, bar plots, box plots, histograms, and correlation tests, to infer relationships and insights from the data set.', 'duration': 769.392, 'highlights': ['The scatter plot visualizes all variables in the data set plotted against each other, aiding in making inferences and identifying outliers. The scatter plot visualizes all variables in the data set plotted against each other, aiding in making inferences and identifying outliers.', 'The pie chart visually represents the ratio of automatic and manual cars, showing that almost 60% are automatic cars in the given data set. The pie chart visually represents the ratio of automatic and manual cars, showing that almost 60% are automatic cars in the given data set.', 'The box plot illustrates the relationship between horsepower and the number of cylinders, showing variations and identifying outliers. The box plot illustrates the relationship between horsepower and the number of cylinders, showing variations and identifying outliers.', 'The bar plot displays the count of cars grouped by the number of gears, providing insights into the distribution of cars based on the number of gears. The bar plot displays the count of cars grouped by the number of gears, providing insights into the distribution of cars based on the number of gears.', 'A histogram plot for the horsepower of cars shows the distribution of horsepower, with most cars falling into the 100 to 150 horsepower range. A histogram plot for the horsepower of cars shows the distribution of horsepower, with most cars falling into the 100 to 150 horsepower range.', 'A correlation test is performed to determine the significance of the relationship between two variables, with a specific case study using sales and temperature data from an ice cream company. A correlation test is performed to determine the significance of the relationship between two variables, with a specific case study using sales and temperature data from an ice cream company.']}], 'duration': 1352.025, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c2281457.jpg', 'highlights': ['Officer David categorizes bank loans into seasoned or bad loans based on age category, capturing total number of loans, good and bad loans, and their percentages.', 'Fine-tuning plot features using xlab and ylab arguments in the plot function for all other plots.', 'Presenting a table of bank loan data including 15 age categories and corresponding number of good and bad loans, along with the percentage of good and bad loans for each category.', 'Using the aggregate function to find the average mileage of cars with three gears (16 on average) and the highest average mileage for cars with four gears.', 'Summary statistics show maximum miles per gallon (33.90), average (20.09), and distribution of automatic and manual cars in the dataset.', 'Identifying the lightest car in the dataset using the which.min function with the mtcars$wt variable.', 'Visualizing all variables in the data set with a scatter plot to aid in making inferences and identifying outliers.', 'Using a pie chart to visually represent the ratio of automatic and manual cars in the given data set (almost 60% automatic).', 'Illustrating the relationship between horsepower and the number of cylinders using a box plot to show variations and identify outliers.', 'Displaying the count of cars grouped by the number of gears with a bar plot to provide insights into the distribution of cars based on the number of gears.', 'Creating a histogram plot for the horsepower of cars to show the distribution, with most cars falling into the 100 to 150 horsepower range.', 'Performing a correlation test to determine the significance of the relationship between two variables, with a specific case study using sales and temperature data from an ice cream company.']}, {'end': 4170.596, 'segs': [{'end': 3741.609, 'src': 'embed', 'start': 3675.616, 'weight': 0, 'content': [{'end': 3677.597, 'text': 'The correlation result is displayed.', 'start': 3675.616, 'duration': 1.981}, {'end': 3683.56, 'text': "It can be inferred that the Pearson's product moment correlation test has been performed.", 'start': 3678.257, 'duration': 5.303}, {'end': 3688.919, 'text': 'The test can be changed by using the method attribute.', 'start': 3685.575, 'duration': 3.344}, {'end': 3693.023, 'text': 'The t-value for these two variables is calculated as 6.6333.', 'start': 3689.72, 'duration': 3.303}, {'end': 3708.527, 'text': 'The degrees of freedom is 5, and the p-value is given as 0.001173.', 'start': 3693.023, 'duration': 15.504}, {'end': 3716.192, 'text': 'The basic alternative hypothesis is displayed and the 95% confidence interval is calculated and displayed.', 'start': 3708.527, 'duration': 7.665}, {'end': 3729.261, 'text': "Pearson's correlation coefficient, RHO, is displayed in the last line and equals 0.9476071.", 'start': 3717.653, 'duration': 11.608}, {'end': 3737.206, 'text': 'From the earlier lessons, we know that a positive sign in the correlation coefficient implies they both are positively correlated.', 'start': 3729.261, 'duration': 7.945}, {'end': 3741.609, 'text': 'That is, an increase in temperature leads to an increase in sales.', 'start': 3737.886, 'duration': 3.723}], 'summary': "Pearson's correlation coefficient is 0.9476, indicating a strong positive correlation between temperature and sales.", 'duration': 65.993, 'max_score': 3675.616, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c3675616.jpg'}, {'end': 3887.757, 'src': 'embed', 'start': 3819.154, 'weight': 3, 'content': [{'end': 3831.065, 'text': 'It can be seen that the p-value, that is the last column, is lesser than 2e minus 16, which is the least positive number represented by R.', 'start': 3819.154, 'duration': 11.911}, {'end': 3838.62, 'text': 'Since p-value is almost zero, we can reject the null hypothesis,', 'start': 3832.836, 'duration': 5.784}, {'end': 3844.464, 'text': 'and thus the inference is that there are differences in results on using different insect sprays.', 'start': 3838.62, 'duration': 5.844}, {'end': 3852.89, 'text': 'Next, we will look at a video that shows how to perform ANOVA in R.', 'start': 3846.186, 'duration': 6.704}, {'end': 3857.794, 'text': 'Now, let us perform ANOVA in R using the insect spray dataset.', 'start': 3852.89, 'duration': 4.904}, {'end': 3866.869, 'text': 'For ANOVA, a model needs to be fitted where we have to decide on a target variable and one or more independent variables.', 'start': 3859.366, 'duration': 7.503}, {'end': 3871.871, 'text': 'Step 1.', 'start': 3870.19, 'duration': 1.681}, {'end': 3878.334, 'text': "In our case, let's take COUNT as the target variable and SPRAY as the independent variable.", 'start': 3871.871, 'duration': 6.463}, {'end': 3882.215, 'text': 'Step 2.', 'start': 3880.855, 'duration': 1.36}, {'end': 3887.757, 'text': 'For fitting the model, use the AOV function and save it in a user-defined model.', 'start': 3882.215, 'duration': 5.542}], 'summary': 'Anova in r shows significant difference in results using insect sprays.', 'duration': 68.603, 'max_score': 3819.154, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c3819154.jpg'}, {'end': 3991.124, 'src': 'embed', 'start': 3961.315, 'weight': 4, 'content': [{'end': 3965.597, 'text': '27 volunteers were selected and they were split into three random groups.', 'start': 3961.315, 'duration': 4.282}, {'end': 3970.9, 'text': 'Nine members were assigned randomly to each of the medications.', 'start': 3966.798, 'duration': 4.102}, {'end': 3982.298, 'text': 'The athletes were instructed to take the medication while experiencing body pain and then report the pain on a scale of 1 to 10,,', 'start': 3972.332, 'duration': 9.966}, {'end': 3984.76, 'text': '10 being too much of pain.', 'start': 3982.298, 'duration': 2.462}, {'end': 3991.124, 'text': 'Here the table shows the pain rating for each athlete across each medication.', 'start': 3985.76, 'duration': 5.364}], 'summary': '27 volunteers split into 3 groups, rated pain on a scale of 1 to 10 for different medications', 'duration': 29.809, 'max_score': 3961.315, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c3961315.jpg'}], 'start': 3634.923, 'title': "Calculating pearson's correlation coefficient and understanding anova in r", 'summary': "Explains the process of calculating the pearson's correlation coefficient, revealing a correlation of 0.9476 between sales and temperature. it also discusses the application of anova in r, showcasing a strong association between variables and providing a case study of testing three formulations of a body pain medication for athletes.", 'chapters': [{'end': 3741.609, 'start': 3634.923, 'title': "Calculating pearson's correlation coefficient", 'summary': "Explains the process of calculating the pearson's correlation coefficient, determining a correlation of 0.9476 between sales and temperature, and the implications of this strong positive correlation on the variables.", 'duration': 106.686, 'highlights': ["The Pearson's correlation coefficient, RHO, is calculated as 0.9476071, indicating a strong positive correlation between sales and temperature.", 'The t-value for these two variables is calculated as 6.6333, with 5 degrees of freedom, and a p-value of 0.001173, signifying a significant correlation.', 'An increase in temperature leads to an increase in sales, as indicated by the positive correlation coefficient.']}, {'end': 4170.596, 'start': 3743.19, 'title': 'Understanding anova in r', 'summary': 'Discusses the application of anova in r, showing the strong association between variables, using the insect sprays dataset to reject the null hypothesis, and providing a case study of testing three formulations of a body pain medication for athletes.', 'duration': 427.406, 'highlights': ['The p-value is almost zero (less than 2e-16), indicating a rejection of the null hypothesis and demonstrating differences in results on using different insect sprays.', 'The chapter provides a case study of testing three formulations of a body pain medication for athletes, with 27 volunteers split into three groups and instructed to report pain on a scale of 1 to 10, leading to the use of ANOVA to test significant differences between means of all three medications.', 'The chapter introduces ANOVA in R, demonstrating the analysis of variance to compare means between different groups, using the insect sprays dataset and providing step-by-step instructions for performing ANOVA in R with the dataset.']}], 'duration': 535.673, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c3634923.jpg', 'highlights': ["The Pearson's correlation coefficient, RHO, is calculated as 0.9476071, indicating a strong positive correlation between sales and temperature.", 'The t-value for these two variables is calculated as 6.6333, with 5 degrees of freedom, and a p-value of 0.001173, signifying a significant correlation.', 'An increase in temperature leads to an increase in sales, as indicated by the positive correlation coefficient.', 'The p-value is almost zero (less than 2e-16), indicating a rejection of the null hypothesis and demonstrating differences in results on using different insect sprays.', 'The chapter provides a case study of testing three formulations of a body pain medication for athletes, with 27 volunteers split into three groups and instructed to report pain on a scale of 1 to 10, leading to the use of ANOVA to test significant differences between means of all three medications.', 'The chapter introduces ANOVA in R, demonstrating the analysis of variance to compare means between different groups, using the insect sprays dataset and providing step-by-step instructions for performing ANOVA in R with the dataset.']}, {'end': 5222.071, 'segs': [{'end': 4273.986, 'src': 'embed', 'start': 4238.988, 'weight': 1, 'content': [{'end': 4249.431, 'text': 'Type the command plot of PainRating tilde Types comma Data equals BodyPainRating and hit Enter.', 'start': 4238.988, 'duration': 10.443}, {'end': 4254.272, 'text': 'The box plot of the ratings is displayed in a new window.', 'start': 4250.771, 'duration': 3.501}, {'end': 4259.733, 'text': 'Note that there is an outlier on the Medication 2 rating entries.', 'start': 4255.472, 'duration': 4.261}, {'end': 4266.535, 'text': 'It can also be noted that the median for Medications 2 and 3 are the same.', 'start': 4262.094, 'duration': 4.441}, {'end': 4273.986, 'text': 'and the median for medication 1 is significantly lower than the other two.', 'start': 4269.284, 'duration': 4.702}], 'summary': 'Box plot of painrating shows outlier on medication 2, with median for medications 2 and 3 being the same, and significantly lower median for medication 1.', 'duration': 34.998, 'max_score': 4238.988, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c4238988.jpg'}, {'end': 4442.912, 'src': 'embed', 'start': 4342.535, 'weight': 0, 'content': [{'end': 4351.439, 'text': 'Since the p-value is significantly lower than the standard value of 0.05, we can infer that the null hypothesis needs to be rejected.', 'start': 4342.535, 'duration': 8.904}, {'end': 4360.682, 'text': 'In the context of this study, we can conclude that different medication formulations have different impacts on the pain rating for athletes.', 'start': 4352.299, 'duration': 8.383}, {'end': 4370.947, 'text': 'In the next slide, we will look at the square test is used to calculate the goodness of a particular fit.', 'start': 4364.044, 'duration': 6.903}, {'end': 4378.45, 'text': 'It compares the observed values against expected values obtained from a null hypothesis.', 'start': 4371.847, 'duration': 6.603}, {'end': 4383.727, 'text': 'Here we try to test the sepal length for goodness of fit.', 'start': 4379.625, 'duration': 4.102}, {'end': 4389.29, 'text': 'The low p-value suggests we can reject the null hypothesis.', 'start': 4384.668, 'duration': 4.622}, {'end': 4397.094, 'text': 'We will now look at a video to understand how to perform a chi-squared test in R.', 'start': 4391.171, 'duration': 5.923}, {'end': 4401.056, 'text': 'Now we go about doing the chi-square test.', 'start': 4397.094, 'duration': 3.962}, {'end': 4407.76, 'text': 'For this test we need a categorical data set and R has that kind of data pre-installed in it.', 'start': 4401.577, 'duration': 6.183}, {'end': 4411.98, 'text': 'The data set is called HairEyeColor.', 'start': 4409.158, 'duration': 2.822}, {'end': 4416.564, 'text': 'Step 1.', 'start': 4415.383, 'duration': 1.181}, {'end': 4419.086, 'text': 'Call that data set using the data function.', 'start': 4416.564, 'duration': 2.522}, {'end': 4425.672, 'text': 'Run the data, data of HairEyeColor, and it becomes active in the workspace.', 'start': 4419.987, 'duration': 5.685}, {'end': 4430.395, 'text': 'Step 2.', 'start': 4429.275, 'duration': 1.12}, {'end': 4438.122, 'text': 'To handle the data more efficiently, save it in a user-defined variable, say, MyData, and put it in a data frame.', 'start': 4430.395, 'duration': 7.727}, {'end': 4442.912, 'text': 'Step 3.', 'start': 4441.471, 'duration': 1.441}], 'summary': "P-value < 0.05, reject null hypothesis; different medication formulations impact athletes' pain ratings; chi-squared test in r using haireyecolor dataset.", 'duration': 100.377, 'max_score': 4342.535, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c4342535.jpg'}, {'end': 4586.615, 'src': 'embed', 'start': 4544.332, 'weight': 4, 'content': [{'end': 4549.115, 'text': 'For example, marks obtained by a student before and after a training.', 'start': 4544.332, 'duration': 4.783}, {'end': 4555.18, 'text': 'To illustrate this, the anorexia data set from the MASS package is used.', 'start': 4550.016, 'duration': 5.164}, {'end': 4561.262, 'text': 'The data contains pre-treatment and post-treatment weights of patients.', 'start': 4556.138, 'duration': 5.124}, {'end': 4578.931, 'text': 'To implement it in R, type t.test The first two attributes are the features to be compared.', 'start': 4562.383, 'duration': 16.548}, {'end': 4586.615, 'text': 'And the attribute paired, equals true, specifies that it is pairwise t-test.', 'start': 4579.912, 'duration': 6.703}], 'summary': 'Using r, a pairwise t-test is performed on pre- and post-treatment weights from the anorexia dataset to compare student performance.', 'duration': 42.283, 'max_score': 4544.332, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c4544332.jpg'}, {'end': 4693.748, 'src': 'embed', 'start': 4621.383, 'weight': 5, 'content': [{'end': 4627.945, 'text': 'Here we implement a t-test on the sepal length and sepal width of the iris data set.', 'start': 4621.383, 'duration': 6.562}, {'end': 4633.027, 'text': 'From the result, it can be seen that the p-value is almost zero.', 'start': 4628.766, 'duration': 4.261}, {'end': 4639.097, 'text': 'And hence, the null hypothesis that there is no relation can be rejected.', 'start': 4634.153, 'duration': 4.944}, {'end': 4648.884, 'text': 'Now, we will look at a video that shows how to perform t-tests using R.', 'start': 4641.519, 'duration': 7.365}, {'end': 4653.087, 'text': 'Next, we try to see how to do t-tests in R.', 'start': 4648.884, 'duration': 4.203}, {'end': 4658.972, 'text': 'Starting off with the one-sample t-test, use the anorexia dataset for this purpose.', 'start': 4653.087, 'duration': 5.885}, {'end': 4663.793, 'text': 'Step 1.', 'start': 4662.512, 'duration': 1.281}, {'end': 4669.217, 'text': 'Choose any variable of the data set to perform the test, say pre-WT.', 'start': 4663.793, 'duration': 5.424}, {'end': 4673.72, 'text': 'Step 2.', 'start': 4672.5, 'duration': 1.22}, {'end': 4684.148, 'text': 'Run the command t.test of pre-WT and pass the parameter mu equals 3 separated by a comma.', 'start': 4673.72, 'duration': 10.428}, {'end': 4688.832, 'text': 'The mu parameter corresponds to the null hypothesis.', 'start': 4685.049, 'duration': 3.783}, {'end': 4693.748, 'text': 'Step 3.', 'start': 4692.327, 'duration': 1.421}], 'summary': 'T-test on iris data rejects null hypothesis, p-value almost zero.', 'duration': 72.365, 'max_score': 4621.383, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c4621383.jpg'}, {'end': 5086.139, 'src': 'embed', 'start': 5059.655, 'weight': 7, 'content': [{'end': 5067.999, 'text': 'We see that the apartments have a minimum of zero bedrooms, which might correspond to studio apartments, and a maximum of seven bedrooms.', 'start': 5059.655, 'duration': 8.344}, {'end': 5074.45, 'text': 'The median value is 3, specifying that apartments with 3 bedrooms are the most common.', 'start': 5069.226, 'duration': 5.224}, {'end': 5081.976, 'text': 'The first quartile, as we know, is the middle value between the smallest number and the median of the data set.', 'start': 5075.411, 'duration': 6.565}, {'end': 5086.139, 'text': 'For bedrooms, this value is 3.', 'start': 5083.197, 'duration': 2.942}], 'summary': 'Apartments range from studio (0 bedrooms) to 7 bedrooms, with 3-bedroom units being the most common.', 'duration': 26.484, 'max_score': 5059.655, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c5059655.jpg'}, {'end': 5161.799, 'src': 'embed', 'start': 5115.757, 'weight': 9, 'content': [{'end': 5122.684, 'text': "if we observe the summary data, we will find that material and level don't have the usual statistics,", 'start': 5115.757, 'duration': 6.927}, {'end': 5127.124, 'text': 'like the quartiles mean or median value given for them.', 'start': 5122.684, 'duration': 4.44}, {'end': 5132.928, 'text': 'They are instead classified into their types and the number of occurrences of each type.', 'start': 5127.945, 'duration': 4.983}, {'end': 5136.811, 'text': 'We can see that most houses are brick houses.', 'start': 5134.309, 'duration': 2.502}, {'end': 5142.135, 'text': 'Having the highest count and aluminum wood houses are the least common.', 'start': 5137.632, 'duration': 4.503}, {'end': 5148.179, 'text': 'From the level variable, we can see that a majority of the houses are two-storied.', 'start': 5143.816, 'duration': 4.363}, {'end': 5155.297, 'text': 'There are only three ranches and seven split-level apartments, and the rest are all two-storied.', 'start': 5149.394, 'duration': 5.903}, {'end': 5161.799, 'text': 'So constructing a two-storied apartment would be a very safe bet in this neighborhood.', 'start': 5157.017, 'duration': 4.782}], 'summary': 'Most houses are brick, majority are two-storied, making it a safe bet to construct a two-storied apartment in this neighborhood.', 'duration': 46.042, 'max_score': 5115.757, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c5115757.jpg'}, {'end': 5222.071, 'src': 'embed', 'start': 5193.428, 'weight': 10, 'content': [{'end': 5204.036, 'text': 'R has a package named pastix that can be installed by using the install.packages function and included in the current working session by the library function.', 'start': 5193.428, 'duration': 10.608}, {'end': 5213.164, 'text': 'We use the stat.desk function to obtain a comprehensive statistical summary of the data.', 'start': 5206.318, 'duration': 6.846}, {'end': 5218.508, 'text': 'So we type stat.desk of the data frame.', 'start': 5214.104, 'duration': 4.404}, {'end': 5222.071, 'text': 'That is my data in this case study.', 'start': 5219.609, 'duration': 2.462}], 'summary': "R's pastix package can be installed using install.packages and included in the current session using library. the stat.desk function provides a comprehensive statistical summary of the data.", 'duration': 28.643, 'max_score': 5193.428, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c5193428.jpg'}], 'start': 4170.598, 'title': 'Analyzing statistical tests and residential apartment construction data in r', 'summary': 'Delves into anova, chi-square test with f-statistic value of 11.91 and p-value of 0.003, handling categorical data, paired t-tests, independent t-tests, and one-sample t-test in r. additionally, it covers analyzing residential apartment construction data including importing the data, descriptive statistics, and obtaining a comprehensive statistical summary focusing on variables like bedrooms count, material, and level.', 'chapters': [{'end': 4401.056, 'start': 4170.598, 'title': 'Anova and chi-square test in r', 'summary': 'Covers creating a data frame, visualizing data using a box plot, performing anova analysis with f-statistic value of 11.91 and p-value of 0.003, and understanding chi-square test with a focus on rejecting null hypothesis due to low p-value.', 'duration': 230.458, 'highlights': ['The F-statistic value is 11.91 with a p-value of 0.003, leading to rejection of the null hypothesis and conclusion that different medication formulations have different impacts on the pain rating for athletes. The F-statistic value of 11.91 and p-value of 0.003 indicate a significant difference in medication impacts on pain ratings, leading to rejection of the null hypothesis and supporting the conclusion for athletes.', 'Visualization of data is done using a box plot, revealing an outlier on Medication 2 rating entries and a significant difference in medians among different medications. The box plot visualization highlights an outlier on Medication 2 rating entries and significant differences in medians among different medications, providing valuable insights into the data distribution.', 'The chi-square test is discussed in relation to rejecting the null hypothesis due to a low p-value, emphasizing its application in testing the goodness of fit. The discussion on the chi-square test emphasizes the importance of rejecting the null hypothesis due to a low p-value and its application in testing the goodness of fit, providing an overview of its significance in statistical analysis.']}, {'end': 4883.907, 'start': 4401.577, 'title': 'Performing statistical tests in r', 'summary': 'Covers handling categorical data in r, performing chi-square test, paired t-tests, independent t-tests, and one-sample t-test, with examples and results, along with practical demonstrations.', 'duration': 482.33, 'highlights': ['Handling categorical data in R, saving in a data frame, and running chi-square test with examples and results.', 'Performing paired t-tests and interpreting the results, including an example with pre-treatment and post-treatment weights of patients.', 'Demonstrating independent t-tests with an example of comparing sepal length and sepal width of the iris dataset, and interpreting the results.', 'Step-by-step explanation of one-sample t-test using the anorexia dataset, including parameters, output, and interpretation.']}, {'end': 5222.071, 'start': 4883.907, 'title': 'Analyzing residential apartment construction data', 'summary': 'Discusses the process of analyzing residential apartment construction data including importing the data, descriptive statistics, and obtaining comprehensive statistical summary, with a focus on variables like bedrooms count, material, and level.', 'duration': 338.164, 'highlights': ['The median value for the bedrooms count variable is 3, indicating that apartments with 3 bedrooms are the most common. The median value for the bedrooms count variable is 3, specifying that apartments with 3 bedrooms are the most common.', 'The mean value for the bedrooms count variable is almost three, showing that the apartments in this locality generally have three bedrooms. The mean value for the bedrooms count variable is almost three, indicating that the apartments in this locality generally have three bedrooms.', 'The majority of the houses are two-storied, making it a safe bet for construction in this neighborhood. The majority of the houses are two-storied, making it a safe bet for construction in this neighborhood.', 'The installation of the pastix package and use of the stat.desk function is recommended to obtain comprehensive statistical summary of the data. The installation of the pastix package and use of the stat.desk function is recommended to obtain comprehensive statistical summary of the data.', 'Brick houses are the most common, while aluminum wood houses are the least common. Brick houses are the most common, while aluminum wood houses are the least common.']}], 'duration': 1051.473, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c4170598.jpg', 'highlights': ['The F-statistic value is 11.91 with a p-value of 0.003, leading to rejection of the null hypothesis and conclusion that different medication formulations have different impacts on the pain rating for athletes.', 'Visualization of data is done using a box plot, revealing an outlier on Medication 2 rating entries and a significant difference in medians among different medications.', 'The chi-square test is discussed in relation to rejecting the null hypothesis due to a low p-value, emphasizing its application in testing the goodness of fit.', 'Handling categorical data in R, saving in a data frame, and running chi-square test with examples and results.', 'Performing paired t-tests and interpreting the results, including an example with pre-treatment and post-treatment weights of patients.', 'Demonstrating independent t-tests with an example of comparing sepal length and sepal width of the iris dataset, and interpreting the results.', 'Step-by-step explanation of one-sample t-test using the anorexia dataset, including parameters, output, and interpretation.', 'The median value for the bedrooms count variable is 3, indicating that apartments with 3 bedrooms are the most common.', 'The mean value for the bedrooms count variable is almost three, showing that the apartments in this locality generally have three bedrooms.', 'The majority of the houses are two-storied, making it a safe bet for construction in this neighborhood.', 'The installation of the pastix package and use of the stat.desk function is recommended to obtain comprehensive statistical summary of the data.', 'Brick houses are the most common, while aluminum wood houses are the least common.']}, {'end': 5759.868, 'segs': [{'end': 5345.84, 'src': 'embed', 'start': 5317.283, 'weight': 3, 'content': [{'end': 5320.446, 'text': 'Next factor is sum and the value displayed is 73.', 'start': 5317.283, 'duration': 3.163}, {'end': 5322.267, 'text': 'So in this locality there are 73 garages.', 'start': 5320.446, 'duration': 1.821}, {'end': 5330.715, 'text': 'The next is median, and its value is 1 for garage count.', 'start': 5326.874, 'duration': 3.841}, {'end': 5333.876, 'text': 'That is, most apartments have one garage.', 'start': 5331.095, 'duration': 2.781}, {'end': 5337.257, 'text': 'We have a mean value of 1.28, however.', 'start': 5334.516, 'duration': 2.741}, {'end': 5342.619, 'text': 'We cannot take the literal interpretation, since the number of garages must be a whole number.', 'start': 5337.857, 'duration': 4.762}, {'end': 5345.84, 'text': 'The next factor is standard error of mean.', 'start': 5343.259, 'duration': 2.581}], 'summary': 'Locality has 73 garages, median garage count is 1, mean is 1.28.', 'duration': 28.557, 'max_score': 5317.283, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c5317283.jpg'}, {'end': 5518.065, 'src': 'embed', 'start': 5461.485, 'weight': 1, 'content': [{'end': 5464.726, 'text': 'And the last factor we can see is coefficient of variance.', 'start': 5461.485, 'duration': 3.241}, {'end': 5469.33, 'text': 'This shows the percentage of variation from the mean.', 'start': 5466.069, 'duration': 3.261}, {'end': 5474.711, 'text': 'For the selling price, it gives a value of 0.37.', 'start': 5469.89, 'duration': 4.821}, {'end': 5479.012, 'text': 'That is, there is a 37% variation of values around the mean.', 'start': 5474.711, 'duration': 4.301}, {'end': 5484.293, 'text': 'Let us view the selling price data in a histogram.', 'start': 5481.493, 'duration': 2.8}, {'end': 5487.314, 'text': 'Type hist of selling price.', 'start': 5485.133, 'duration': 2.181}, {'end': 5495.356, 'text': 'From the results, we see that there are buckets of 10 to 90 million, increasing by 10 million each.', 'start': 5488.554, 'duration': 6.802}, {'end': 5505.271, 'text': 'Most of the houses are sold in the $20 to $40 million range and there are no houses sold in the $60 to $80 million range.', 'start': 5496.661, 'duration': 8.61}, {'end': 5509.615, 'text': 'There are two apartments sold above $80 million.', 'start': 5507.273, 'duration': 2.342}, {'end': 5513.86, 'text': 'We are interested in knowing what is unique about these apartments.', 'start': 5510.376, 'duration': 3.484}, {'end': 5518.065, 'text': 'To view just these two records, we will subset the data.', 'start': 5514.661, 'duration': 3.404}], 'summary': 'Selling price data has a coefficient of variance of 0.37, with most houses sold in the $20 to $40 million range and no sales in the $60 to $80 million range. two apartments were sold above $80 million.', 'duration': 56.58, 'max_score': 5461.485, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c5461485.jpg'}, {'end': 5658.954, 'src': 'embed', 'start': 5609.63, 'weight': 0, 'content': [{'end': 5615.232, 'text': 'so apartments and people in this locality are willing to pay more for apartments with five bedrooms.', 'start': 5609.63, 'duration': 5.602}, {'end': 5622.055, 'text': 'We should also remember that both apartments that sold highest had five bedrooms,', 'start': 5617.273, 'duration': 4.782}, {'end': 5627.617, 'text': 'and thus the average results might not be the right statistic due to the presence of outliers.', 'start': 5622.055, 'duration': 5.562}, {'end': 5635.32, 'text': 'Let us do a similar check on the selling price according to the garage count.', 'start': 5630.918, 'duration': 4.402}, {'end': 5642.69, 'text': 'There is a huge difference between the average selling prices of apartments with no garages and two garages.', 'start': 5636.769, 'duration': 5.921}, {'end': 5647.951, 'text': 'So adding a garage might be beneficial in increasing the selling price.', 'start': 5643.751, 'duration': 4.2}, {'end': 5658.954, 'text': 'The next thing that we would be doing with the data set is to see which factor is actually affecting the selling price of the apartments.', 'start': 5651.232, 'duration': 7.722}], 'summary': 'Apartments with five bedrooms command higher prices. garages also increase selling price.', 'duration': 49.324, 'max_score': 5609.63, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c5609630.jpg'}], 'start': 5223.072, 'title': 'Garage and selling price analysis', 'summary': 'Provides a statistical description of garage count in a locality, with 57 values, 6 nulls, and a range of 0 to 2. it also analyzes the selling price variation, with a mean of 34 million, variance of 164.38, and a coefficient of variance of 0.37. additionally, the analysis of apartment selling prices reveals a range of $20 to $40 million, notable absence in the $60 to $80 million range, and correlation between factors affecting apartment selling prices.', 'chapters': [{'end': 5487.314, 'start': 5223.072, 'title': 'Statistical analysis of garage count and selling price', 'summary': 'Provides a statistical description of garage count in a locality, with 57 values, 6 nulls, and a range of 0 to 2, and analyzes the selling price variation, with a mean of 34 million, variance of 164.38, and a coefficient of variance of 0.37.', 'duration': 264.242, 'highlights': ['The statistical description for garage count includes 57 values, 6 nulls, and a range of 0 to 2, indicating most apartments have one garage, with a mean value of 1.28. The number of values, nulls, and range for garage count are detailed, showing a mean value of 1.28.', "The analysis of selling price reveals a mean of 34 million, a variance of 164.38, and a coefficient of variance of 0.37, indicating a 37% variation around the mean. The selling price's mean, variance, and coefficient of variance are highlighted, indicating a 37% variation around the mean."]}, {'end': 5759.868, 'start': 5488.554, 'title': 'Apartment selling price analysis', 'summary': 'Analyzes apartment selling prices, finding that most apartments are sold in the $20 to $40 million range, with a notable absence of sales in the $60 to $80 million range. apartments with five bedrooms have the highest selling price, and adding a garage may increase the selling price. correlation analysis is used to determine the factors affecting apartment selling prices.', 'duration': 271.314, 'highlights': ['Apartments with five bedrooms have the highest selling price. Apartments with five bedrooms have the highest average selling price, indicating a preference for larger apartments.', 'Adding a garage may be beneficial in increasing the selling price. There is a significant difference in the average selling prices of apartments with no garages and those with two garages, suggesting that adding a garage may increase the selling price.', 'Most apartments are sold in the $20 to $40 million range. The majority of apartments are sold in the $20 to $40 million range, indicating the most common price range for sales.', 'No houses are sold in the $60 to $80 million range. There are no houses sold in the $60 to $80 million range, indicating a gap in the market for sales within this price range.', 'Correlation analysis is used to determine the factors affecting apartment selling prices. Correlation analysis is being used to identify the variables that affect the selling price of apartments, including factors such as house material, number of rooms, and living space per square foot.']}], 'duration': 536.796, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c5223072.jpg', 'highlights': ['Apartments with five bedrooms have the highest selling price.', 'Most apartments are sold in the $20 to $40 million range.', 'Adding a garage may be beneficial in increasing the selling price.', 'The statistical description for garage count includes 57 values, 6 nulls, and a range of 0 to 2, indicating most apartments have one garage, with a mean value of 1.28.', 'The analysis of selling price reveals a mean of 34 million, a variance of 164.38, and a coefficient of variance of 0.37, indicating a 37% variation around the mean.']}, {'end': 6641.728, 'segs': [{'end': 5847.575, 'src': 'embed', 'start': 5791.411, 'weight': 0, 'content': [{'end': 5800.517, 'text': 'The living space per square feet variable is a very close second, having a correlation of 0.789 with the selling price.', 'start': 5791.411, 'duration': 9.106}, {'end': 5814.639, 'text': 'So the selling price is strongly correlated with the local price and the living space of the apartment, as suggested by the correlation of almost 80%.', 'start': 5801.858, 'duration': 12.781}, {'end': 5825.344, 'text': 'we can see that age in years has a negative correlation with the selling price with a value of minus 0.21.', 'start': 5814.639, 'duration': 10.705}, {'end': 5833.247, 'text': 'Thought the correlation coefficient is quite low, we can see that an increase in the age for apartments tend to decrease its market value.', 'start': 5825.344, 'duration': 7.903}, {'end': 5840.889, 'text': 'Performing correlation for all the variables against all the other variables is more advantageous,', 'start': 5834.824, 'duration': 6.065}, {'end': 5847.575, 'text': 'as it compares all the variables and helps us in getting insights which might not be visible by just looking at the data.', 'start': 5840.889, 'duration': 6.686}], 'summary': 'Selling price strongly correlates with local price and living space (0.789), while age has a negative correlation (-0.21).', 'duration': 56.164, 'max_score': 5791.411, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c5791411.jpg'}, {'end': 6112.599, 'src': 'embed', 'start': 6082.993, 'weight': 2, 'content': [{'end': 6090.038, 'text': 'We can see that two-story and ranch apartments have a high variance whereas split level prices vary very little.', 'start': 6082.993, 'duration': 7.045}, {'end': 6094.461, 'text': 'Also for two-story there are two outliers in the data.', 'start': 6091.058, 'duration': 3.403}, {'end': 6100.016, 'text': 'A similar ANOVA can be done with material and selling price too.', 'start': 6096.215, 'duration': 3.801}, {'end': 6112.599, 'text': 'So type fit equals AOV of selling price tilde material comma data equals my data and we can see the ANOVA results for material.', 'start': 6100.796, 'duration': 11.803}], 'summary': 'Two-story and ranch apartments show high variance, while split level prices vary little. two-story has two outliers. anova can be done with material and selling price.', 'duration': 29.606, 'max_score': 6082.993, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c6082993.jpg'}, {'end': 6199.395, 'src': 'embed', 'start': 6170.834, 'weight': 5, 'content': [{'end': 6180.24, 'text': 'Here we have performed a comprehensive descriptive statistics on the data and some inferential statistics depending on the data we have at hand.', 'start': 6170.834, 'duration': 9.406}, {'end': 6190.727, 'text': 'It is very important to note that the type of tests and the statistics that we use depend entirely on the business need and the type of data we have.', 'start': 6180.58, 'duration': 10.147}, {'end': 6199.395, 'text': 'It is common in analytics to be sidelined from the purpose of the research or analysis by applying various tests on the given data.', 'start': 6191.829, 'duration': 7.566}], 'summary': 'Descriptive and inferential statistics were performed on the data, emphasizing the importance of tailoring tests to business needs and data type.', 'duration': 28.561, 'max_score': 6170.834, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c6170834.jpg'}, {'end': 6338.847, 'src': 'embed', 'start': 6310.263, 'weight': 3, 'content': [{'end': 6316.085, 'text': 'Since our data adheres to all the above assumptions, we can continue with the significance test.', 'start': 6310.263, 'duration': 5.822}, {'end': 6322.129, 'text': 'The researcher wants to know if the use of the drug decreases the tumor size.', 'start': 6317.284, 'duration': 4.845}, {'end': 6327.175, 'text': 'Hence, it corresponds to a one-tailed hypothesis test.', 'start': 6323.211, 'duration': 3.964}, {'end': 6338.847, 'text': 'Remember that two-sided hypothesis tests would be used when the research question is whether there is a difference in tumor size as the result of the drug.', 'start': 6328.236, 'duration': 10.611}], 'summary': 'Data adheres to assumptions, one-tailed hypothesis test to check if drug decreases tumor size.', 'duration': 28.584, 'max_score': 6310.263, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c6310263.jpg'}, {'end': 6592.687, 'src': 'embed', 'start': 6564.808, 'weight': 4, 'content': [{'end': 6573.838, 'text': 'Thus, we can conclude that the drug has an effect of the size on tumor in mice, and the tumor size reduces as an effect of the drug.', 'start': 6564.808, 'duration': 9.03}, {'end': 6580.956, 'text': 'With this, we complete the concepts of basic analytic techniques.', 'start': 6577.233, 'duration': 3.723}, {'end': 6586.942, 'text': 'Now, we will look at a business case to illustrate the concepts that we have learned in this lesson.', 'start': 6581.617, 'duration': 5.325}, {'end': 6592.687, 'text': 'Let us quickly summarize what we have learned in this lesson.', 'start': 6589.244, 'duration': 3.443}], 'summary': 'Drug reduces tumor size in mice, concluding basic analytic techniques.', 'duration': 27.879, 'max_score': 6564.808, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c6564808.jpg'}], 'start': 5761.161, 'title': 'Analyzing statistical data', 'summary': 'Covers correlation analysis highlighting strong positive correlations of local price and living space with selling price at 0.79 and 0.789 respectively, negative correlation of age at -0.21, and anova analysis revealing high p-values for level and material variables. additionally, it discusses the application of t-tests in analyzing the drug effect on tumor size, concluding a significant reduction with a value of 0.05.', 'chapters': [{'end': 6167.606, 'start': 5761.161, 'title': 'Correlation and anova analysis', 'summary': 'Covers the correlation between various variables, with the local price and living space showing strong positive correlations with the selling price at 0.79 and 0.789 respectively, while the age in years has a negative correlation of -0.21. additionally, it delves into anova analysis, revealing high p-values for both level and material variables, indicating the inability to reject the null hypothesis.', 'duration': 406.445, 'highlights': ['The local price and living space variables show strong positive correlations with the selling price at 0.79 and 0.789 respectively. This indicates a strong relationship between these variables and the selling price, providing valuable insights for decision-making.', 'The age in years has a negative correlation of -0.21 with the selling price. This suggests that an increase in the age of apartments tends to decrease their market value, providing important implications for property valuation.', 'The ANOVA analysis reveals high p-values for both level and material variables, indicating the inability to reject the null hypothesis. This signifies that there is not enough evidence to conclude a significant difference in means between the groups, providing crucial insights into the impact of these categorical variables on selling prices.']}, {'end': 6641.728, 'start': 6170.834, 'title': 'Analyzing drug effect on tumor growth', 'summary': 'Discusses the process of using t-tests to analyze the effect of a drug on tumor size, demonstrating its application in a business case, with a conclusion that the drug reduces tumor size with a significance value of 0.05.', 'duration': 470.894, 'highlights': ['The drug reduces the tumor size in mice with a significance value of 0.05 The t-test resulted in a p-value of 0.02927, allowing the rejection of the null hypothesis and concluding that the drug has an effect on reducing tumor size.', 'Describing the process of using t-tests to analyze the effect of a drug on tumor size The chapter provides a step-by-step demonstration of performing a t-test to analyze the effect of a drug on tumor size, showcasing the practical application of statistical analysis in a business context.', 'The importance of using appropriate statistical tests based on business needs and data type Emphasizes the importance of choosing the right statistical tests based on the business need and the type of data available, highlighting the significance of aligning statistical analysis with specific objectives.']}], 'duration': 880.567, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/rqrrTfy-z-c/pics/rqrrTfy-z-c5761161.jpg', 'highlights': ['Strong positive correlations of local price and living space with selling price at 0.79 and 0.789 respectively provide valuable insights for decision-making.', 'Negative correlation of age at -0.21 suggests implications for property valuation.', 'ANOVA analysis reveals high p-values for level and material variables, providing crucial insights into their impact on selling prices.', 'The drug reduces tumor size with a significance value of 0.05, allowing the rejection of the null hypothesis.', 'Step-by-step demonstration of performing a t-test to analyze the effect of a drug on tumor size showcases practical application of statistical analysis in a business context.', 'Emphasizes the importance of choosing the right statistical tests based on the business need and the type of data available.']}], 'highlights': ['R is a freely available programming language for statistical computations and graphics, widely used in data mining and statistical analysis, including time series analysis, linear modeling, and nonlinear modeling.', 'The main advantage of using R for data mining is its active community and the availability of built-in packages and package contributions by the members of the community.', 'Completing the course provides a basic introduction to R, understanding of data exploration, visualization, and diagnostic analytics using R tools.', "The Pearson's correlation coefficient, RHO, is calculated as 0.9476071, indicating a strong positive correlation between sales and temperature.", 'The t-value for these two variables is calculated as 6.6333, with 5 degrees of freedom, and a p-value of 0.001173, signifying a significant correlation.', 'The F-statistic value is 11.91 with a p-value of 0.003, leading to rejection of the null hypothesis and conclusion that different medication formulations have different impacts on the pain rating for athletes.', 'Strong positive correlations of local price and living space with selling price at 0.79 and 0.789 respectively provide valuable insights for decision-making.', 'Negative correlation of age at -0.21 suggests implications for property valuation.', 'ANOVA analysis reveals high p-values for level and material variables, providing crucial insights into their impact on selling prices.', 'The drug reduces tumor size with a significance value of 0.05, allowing the rejection of the null hypothesis.']}