Checking Linear Regression Assumptions in R | R Tutorial 5.2 | MarinStatsLectures

Checking Linear Regression Assumptions in R: Learn how to check the linearity assumption, constant variance (homoscedasticity) and the assumption of normality for a regression model in R. To learn more about Linear Regression Concept and with R (bit.ly/2z8fXg1); 💻 For the free Practice Dataset: (bit.ly/2rOfgEJ) 👍🏼Best Statistics & R Programming Tutorials: ( goo.gl/4vDQzT )

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How to test linear regression assumptions in R?

In this R tutorial, we will first go over some of the concepts for linear regression like how to add a regression line, how to interpret the regression line (predicted or fitted Y value, the mean of Y given X), how to interpret the residuals or errors (the difference between observed Y value and the predicted or fitted Y value) and the assumptions when fitting a linear regression model.
Then we will discuss the regression diagnostic plots in R, the reason for making diagnostic plots, and how to produce these plots in R; You will learn to check the linearity assumption and constant variance (homoscedasticity) for a regression model with residual plots in R and test the assumption of normality in R with QQ (Quantile Quantile) plots. You will also learn to check the constant variance assumption for data with non-constant variance in R, produce and interpret residual plots, QQ plots, and scatterplots for data with non-constant variance, and produce and interpret residual plots, QQ plots, and scatterplots for data with non-linear relationship in R.


■ Table of Content:

0:00:29 Introducing the data used in this video
0:00:49 How to fit a Linear Regression Model in R?
0:01:03 how to produce the summary of the linear regression model in R?
0:01:15 How to add a regression line to the plot in R?
0:01:24 How to interpret the regression line?
0:01:43 How to interpret the residuals or errors?
0:01:53 where to find the Residual Standard Error (Standard Deviation of Residuals) in R
0:02:14 What are the assumptions when fitting a linear regression model and how to check these assumptions
0:03:01 What are the built-in regression diagnostic plots in R and how to produce them
0:03:24 How to use Residual Plot for testing linear regression assumptions in R
0:03:50 How to use QQ-Plot in R to test linear regression assumptions
0:04:33 How to produce multiple plots on one screen in R
0:05:00 How to check constant variance assumption for data with non-constant variance in R
0:05:12 How to produce and interpret a Scatterplot and regression line for data with non-constant variance
0:05:40 How to produce and interpret the Residual plot for data with non-constant variance in R
0:06:02 How to produce and interpret the QQ plot for data with non-constant variance in R
0:06:12 How to produce and interpret a Scatterplot with regression line for data with non-linear relationship in R
0:06:40 How to produce and interpret the Residual plot for a data with non-linear relationship in R
0:06:52 How to produce and interpret the QQ plot for a data with non-linear relationship in R
0:07:02 what is the reason for making diagnostic plots


►► Watch More:
►Linear Regression Concept and Linear Regression with R Series: bit.ly/2z8fXg1
►Simple Linear Regression Concept youtu.be/vblX9JVpHE8
►Nonlinearity in Linear Regression youtu.be/tOzwEv0PoZk
► R Squared of Coefficient of Determination youtu.be/GI8ohuIGjJA
► Linear Regression in R Complete Series bit.ly/1iytAtm
► Intro to Statistics Course: bit.ly/2SQOxDH
►Data Science with R bit.ly/1A1Pixc


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Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC.
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