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Bivariate Analysis in R

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Overview

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This series teaches learners to perform bivariate statistical analyses in R, using examples to compare groups, assess associations, and model relationships. It covers parametric and nonparametric inference, resampling, confidence intervals, and simple linear regression.

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

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