This course aims to teach learners how to fit linear regression models in R, interpret model output, check model assumptions, create categorical variables, use dummy variables, include factors in regression models, interpret interaction effects, and perform variable selection using the partial F-test. The teaching method involves practical tutorials in R. This course is intended for individuals interested in statistics, data analysis, and regression modeling 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
