rguides

Tutorial series

R for Statistics

6 tutorials — follow in order for the best learning path.

  1. Descriptive Statistics in R

    Learn how to calculate descriptive statistics in R, mean, median, mode, variance, standard deviation, quartiles, and more.

  2. Hypothesis Testing in R

    Learn how to perform hypothesis tests in R including t-tests, chi-square tests, and ANOVA. Covers p-values, significance levels, and interpreting results.

  3. Linear Regression in R

    Learn simple and multiple linear regression in R with lm(), including diagnostics, interpretation, predictions and visualization.

  4. Logistic Regression in R

    Learn how to build, interpret, and evaluate logistic regression models in R using the glm() function. Covers odds ratios, predictions, and model diagnostics.

  5. ANOVA in R, Learn how to perform Analysis of Variance (ANOVA)

    Learn how to perform Analysis of Variance (ANOVA) in R, including one-way ANOVA, two-way ANOVA, and post-hoc tests with practical examples.

  6. Regression Models in R

    Learn to fit, interpret, and diagnose linear and generalized linear regression models in R using lm() and glm().