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 (https://bit.ly/2z8fXg1); 💻 For the free Practice Dataset: (https://bit.ly/2rOfgEJ) 👍🏼Best Statistics & R Programming Tutorials: ( https://goo.gl/4vDQzT )

►► Like to support us? You can Donate (https://bit.ly/2CWxnP2), Share our Videos, Leave us a Comment, Give us a Like or Write us a Review! Either way, We Thank You!

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


🤳🏽Follow MarinStatsLectures

Subscribe: https://goo.gl/4vDQzT
website: https://statslectures.com
Facebook:https://goo.gl/qYQavS
Twitter:https://goo.gl/393AQG
Instagram: https://goo.gl/fdPiDn

Our Team:
Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC.
Producer and Creative Manager: Ladan Hamadani (B.Sc., BA., MPH)

These videos are created by #marinstatslectures to support some courses at The University of British Columbia (UBC) (#IntroductoryStatistics and #RVideoTutorials for Health Science Research), although we make all videos available to the everyone everywhere for free.

Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!

Видео Checking Linear Regression Assumptions in R | R Tutorial 5.2 | MarinStatsLectures канала MarinStatsLectures-R Programming & Statistics