Step_by_step_R_intro_with_detailed_comments_using__

# Step by step R intro tutorial with detailed comments The content is from *An Introduction to Statistical Learning with Application* in R (ISLR2). You can download the ebook [here](https://hastie.su.domains/ISLR2/ISLRv2_website.pdf). * linear regression (Chapter 3) * how to use lm() function to do * simple linear regression * how to interpret information generated by summary() function | Quick take out [here](https://www.learnbymarketing.com/tutorials/linear-regression-in-r/) * multiple linear regression * interaction(s) * [What's the right way to add interaction term in lm()?](https://stats.stackexchange.com/questions/387801/r-whats-the-right-way-to-add-an-interaction-term-in-lm) * deal with qualitative variable(s) * non-linear regression (yes, lm() can!) * use confint() function to get confidence interval * use predict() function to predict value using model generated by lm() function * how to conduct diagnostic analysis * residuals v.s. fitted values (to check whether linear model fits well or not) * random, non-constant variance (heterosidasicity), and non-linear | Quick take out [here](https://stats.stackexchange.com/questions/76226/interpreting-the-residuals-vs-fitted-values-plot-for-verifying-the-assumptions) * Q-Q plot tells whether the majority of points are normally distributed or not * [Difference between Q-Q plot and P-P plot?](https://stats.stackexchange.com/questions/92141/pp-plots-vs-qq-plots/100383#100383) * leverage * collinearlity check using vif() funciton * write customize function