Bivariate Analysis with R
The Series 4 of R video tutorials (Bivaraite analysis in R), will show you step by step and through working with examples how to conduct bivariate analysis in R (and also a bit of univariate analysis, specifically the one-sample t-test for a population mean in R). These video tutorials will discuss one sample t-test, two sample t-test for independent groups and paired t-test in R. Here we will also learn to perform Mann Whitney or Wilcoxon rank-sum test, Wilcoxon rank test, analysis of variance (ANOVA), chi-square test of independence, Fisher's exact test and cross tabulations with R programming langauge. You will also learn to use R software to calculate relative risks, odds ratios and risk difference, correlation and covariance and more.
Here are the R Video Tutorials:
(R video tutorial 4.1):One-Sample t Test in R: How to conduct the one-sample t-test and confidence interval for the mean of a single variable using R Programming Langauge
(R video tutorial 4.2): Two-Sample t Test in R How to conduct the independent two-sample t-test and confidence interval for the difference in means of two populations in R
(R video tutorial 4.3): Mann Whitney U aka Wilcoxon Rank-Sum Test in R: How to conduct the Wilcoxon Rank-Sum (aka Mann-Whitney U) test, a non-parametric alternative to the independent t-test in R
(R video tutorial 4.4): Bootstrap Hypothesis Testing in R: How to use R Programming Language to perform a bootstrap hypothesis testing with examples
(R video tutorial 4.5): Bootstrap Confidence Interval with R: How to Calculate Probabilities for Binomial Random Variables in R
(R video tutorial 4.6): Permutation Hypothesis Test in R: How to Conduct a Permutation Hypothesis Test in R programming Language Using RStudio, Step by Step with No Package.
(R video tutorial 4.7): Paired t Test in R: How to conduct the matched paired t-test in R to compare the means of two paired or dependent populations
(R video tutorial 4.8): Wilcoxon Signed Rank Test in R: How to conduct the Wilcoxon Signed Rank test with R a non-parametric alternative to the paired t-test
(R video tutorial 4.9): ANOVA, Multiple Comparisons & Kurskal Wallis in R: How to conduct one way Analysis of Variance (ANOVA) in R, ANOVA Pairwise Comparison in R, (Multiple Comparisons in R), and Kruskal Wallis one-way ANOVA in R
(R video tutorial 4.10): Chi-Square Test, Fishers Exact Test, and Cross Tabulations in R: How to conduct the Chi-square test of independence and Fisher's Exact test, as well as produce cross tabulations in R.
(R video tutorial 4.11): Calculate Odds Ratio and Relative Risk in R : how to calculate the relative risk, odds ratio and risk difference (also known as attributable risk) using the epiR package in R (These are numeric summaries for analyzing 2x2 tables, also known as cross-tabluations)
(R video tutorial 4.12): Correlation and Covariance in R: How to calculate Pearson's, Spearman's rank and Kendall's rank correlations in R, calculate covariance, create confidence intervals and hypothesis tests, and produce pairwise plots using R programming language
These videos can be used by beginners in R and anyone intrested in statistic with R and datascience with R. No prior knowledge of R programming is needed, just a passion to learn! But we highly recommend watching the Series 1 of R video tutorials first!
Make sure to use the free R datasets provided for you to get some hand on experience while watching the tutorials!
