Master linear regression techniques in R through this comprehensive tutorial series. Learn to fit models, interpret outputs, check assumptions, handle categorical variables, analyze interactions, perform partial F-tests, and implement polynomial regression. Gain practical skills in using R for both simple and multiple linear regression, including creating dummy variables, changing reference categories, and assessing model validity through residual plots. Ideal for statistics students and researchers looking to enhance their data analysis capabilities using R.
Overview
Syllabus
Simple Linear Regression in R | R Tutorial 5.1 | MarinStatsLectures.
Checking Linear Regression Assumptions in R | R Tutorial 5.2 | MarinStatsLectures.
Multiple Linear Regression in R | R Tutorial 5.3 | MarinStatsLectures.
Changing Numeric Variable to Categorical in R | R Tutorial 5.4 | MarinStatsLectures.
Dummy Variables or Indicator Variables in R | R Tutorial 5.5 | MarinStatsLectures.
Change Reference (Baseline) Category in Regression with R | R Tutorial 5.6 | MarinStatsLectures.
Including Variables/ Factors in Regression with R, Part I | R Tutorial 5.7 | MarinStatsLectures.
Including Variables/ Factors in Regression with R, Part II | R Tutorial 5.8 | MarinStatsLectures.
Multiple Linear Regression with Interaction in R | R Tutorial 5.9 | MarinStatsLectures.
Interpreting Interaction in Linear Regression with R | R Tutorial 5.10 | MarinStatsLectures.
Partial F-Test for Variable Selection in Linear Regression | R Tutorial 5.11| MarinStatsLectures.
Polynomial Regression in R | R Tutorial 5.12 | MarinStatsLectures.
Taught by
MarinStatsLectures-R Programming & Statistics
