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Descriptive statistics - Lecture 4 - Data analysis using R
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An example of a data API for Social Sciences. Lecture 4 contains the Answers (L3) and goes into Descriptive Statistics and how you can compute them with R. What are central tendencies? (mean, median, mode), Dispersion Measurements (range, quantiles, variance, standard deviation, outliers) and Shape Parameters / indicators (skewness, kurtosis) ?
Chapters:
00:00:00 - Sound check and introduction
00:05:49 - Overview of the lecture
00:10:58 - Answers to Assignments Lecture 3
01:13:00 - Break 1: Fox gifs
01:19:25 - Answers to Assignments Lecture 3 (Continued)
02:06:07 - Break 2: Wombat gifs
02:12:40 - Start of lecture 4
02:13:18 - Data APIs for Social Sciences
02:35:59 - Univariate versus Bivariate statistical analysis
02:38:04 - Central tendencies: Different types of means
02:45:00 - Central tendencies: Median
02:46:07 - Central tendencies: Mode
02:46:50 - Dispersion measures: Range
02:47:52 - Dispersion measures: Quantiles
02:49:33 - Dispersion measures: Variance & Standard Deviation
02:52:24 - Outliers and Data Winsorizing
02:57:27 - Shape measures: Skewness
02:58:35 - Shape measures: Kurtosis (Meso, Lepto, Platy)
03:00:00 - Normality in statistical tests
03:01:30 - Shapiro-Wilk normality testing in R
03:02:33 - Plots for univariate statistics
03:11:00 - Graphical parameters in R
03:12:03 - How to create a plot in R
03:14:30 - Multiple plots in a single R window
03:16:17 - Apply and subset functions in R
03:18:52 - Next week, Questions & outro
#rlanguage #programmingforstudents #lectureseries #statistics #assignments #coding #livestream #socialscience #lectures #playlist #education #educationalvideos #msc #phd #english #statisticsfordatascience #academia #academicyoutube #rpackages #answers #distributions #skewness #kurtosis #statistics #univariate #winsorizing #dispersion #normality #socialscience #api #outliers #plotting #descriptivestatistics
Chapters:
00:00:00 - Sound check and introduction
00:05:49 - Overview of the lecture
00:10:58 - Answers to Assignments Lecture 3
01:13:00 - Break 1: Fox gifs
01:19:25 - Answers to Assignments Lecture 3 (Continued)
02:06:07 - Break 2: Wombat gifs
02:12:40 - Start of lecture 4
02:13:18 - Data APIs for Social Sciences
02:35:59 - Univariate versus Bivariate statistical analysis
02:38:04 - Central tendencies: Different types of means
02:45:00 - Central tendencies: Median
02:46:07 - Central tendencies: Mode
02:46:50 - Dispersion measures: Range
02:47:52 - Dispersion measures: Quantiles
02:49:33 - Dispersion measures: Variance & Standard Deviation
02:52:24 - Outliers and Data Winsorizing
02:57:27 - Shape measures: Skewness
02:58:35 - Shape measures: Kurtosis (Meso, Lepto, Platy)
03:00:00 - Normality in statistical tests
03:01:30 - Shapiro-Wilk normality testing in R
03:02:33 - Plots for univariate statistics
03:11:00 - Graphical parameters in R
03:12:03 - How to create a plot in R
03:14:30 - Multiple plots in a single R window
03:16:17 - Apply and subset functions in R
03:18:52 - Next week, Questions & outro
#rlanguage #programmingforstudents #lectureseries #statistics #assignments #coding #livestream #socialscience #lectures #playlist #education #educationalvideos #msc #phd #english #statisticsfordatascience #academia #academicyoutube #rpackages #answers #distributions #skewness #kurtosis #statistics #univariate #winsorizing #dispersion #normality #socialscience #api #outliers #plotting #descriptivestatistics
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