This course on Bivariate Analysis in R aims to teach learners how to conduct bivariate and univariate analysis using R statistical software. The course covers topics such as paired and independent t-tests, analysis of variance (ANOVA), chi-square tests, relative risks, odds ratios, correlations, covariance, and simple linear regression in R. The teaching method involves video tutorials, and the intended audience includes individuals interested in learning data science and statistics using R programming language.
Overview
Syllabus
One-Sample t Test & Confidence Interval in R with Example | R Tutorial 4.1| MarinStatsLectures.
Two-Sample t Test in R (Independent Groups) with Example | R Tutorial 4.2 | MarinStatsLectures.
Mann Whitney U / Wilcoxon Rank-Sum Test in R | R Tutorial 4.3 | MarinStatsLectures.
Bootstrap Hypothesis Testing in R with Example | R Video Tutorial 4.4 | MarinStatsLecutres.
Bootstrap Confidence Interval with R | R Video Tutorial 4.5 | MarinStatsLectures.
Permutation Hypothesis Test in R with Examples | R Tutorial 4.6 | MarinStatsLectures.
Paired t-Test in R with Examples | R Tutorial 4.7 | MarinStatsLectures.
Wilcoxon Signed Rank Test in R with Example | R Tutorial 4.8 | MarinStatsLectures.
ANOVA, ANOVA Multiple Comparisons & Kruskal Wallis in R | R Tutorial 4.9 | MarinStatsLectures|.
Chi-Square Test, Fisher’s Exact Test, & Cross Tabulations in R | R Tutorial 4.10| MarinStatsLectures.
Odds Ratio, Relative Risk & Risk Difference with R | R Tutorial 4.11| MarinStatsLectures.
Correlations and Covariance in R with Example | R Tutorial 4.12 | MarinStatsLectures.
Simple Linear Regression in R | R Tutorial 5.1 | MarinStatsLectures.
Checking Linear Regression Assumptions in R | R Tutorial 5.2 | MarinStatsLectures.
Taught by
MarinStatsLectures-R Programming & Statistics
