Multiple Linear Regression in R | R Tutorial 5.3 | MarinStatsLectures

In this R video lecture you will learn to use "lm", "summary", "cor", "confint" functions among others. You will also learn to use "plot" function for producing residual and QQ plots in R.

◼︎ Table of Content:

0:00:07 Multiple Linear Regression Model
0:00:32 How to fit a linear model in R? using the "lm" function
0:00:36 How to access the help menu in R for multiple linear regression
0:01:06 How to fit a linear regression model in R with two explanatory or X variables
0:01:19 How to produce and interpret the summary of linear regression model fit in R
0:03:16 How to calculate Pearson's correlation between the two variables with R
0:03:26 How to interpret the collinearity between two variables in R
0:03:49 How to create a confidence interval for the model coefficients in R? using the "confint" function
0:03:57 How to interpret the confidence interval for our model's coefficients in R
0:04:13 How to fit a linear model using all of the X variables in R
0:04:27 how to check the linear regression model assumptions in R? by examining plots of the residuals or errors using the "plot(model)" function

►► Watch More:

This video is a tutorial for programming in R Statistical Software for beginners, using RStudio.

Follow MarinStatsLectures

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!

[amitchandak]

Your videos are very insightful and easy to follow. I'm paying $$$ for R course in college, rather I could have learned by following your videos and save my money!

[pschoi]

I have struggled so much in learning r. Your tutorials made learning easy. Thank you!

[AbheeBrahmnalkar]

This entire series was just LOVELY! Thank you for providing clarity on interpreting various attributes of lm model

[Fletacarling]

these videos have literally saved my life for every homework assignment!! Cheers from a linguistics grad student

[guannanzou]

The good part for this video is that it gives very specific details and steps in how to make a model and build up pictures in R. On the other hand, this video seems too complex about some simple parts. It discusses not very much about the background information. But as a learning guide, it seems very useful and efficient for those who just starts get used to the model in R programming.

[shakuntalasharma]

I have gone through all tutorials. Very clear, well organized. Very helpful.

[KJ-jqfq]

I successfully tuned linear model with a rmse 8 while watching this video. Thank you!

[bernardraath]

Mike, that help function you showed at start is so helpful as a beginner. Thank you.

[yzpezhd]

it's very clear, understandable, beautiful explanation. From Japan.

[indzz]

Thanks for these videos! Saved me when I was doing my essay that required data analysis🙌

[DHamsterdam]

I've done most tutorials so far. They are clear and well made. Compliments!

[mustafanasiri]

Very useful tutorial and a wonderful, clear and concise video. What if our estimate value in a multi regression model is negative, while p-value still being significant? How do you interpret a negative estimate value for one of the independent variables? Could you please explain a bit?

[ZakharovInvest]

You are just awesome!! I wish I had you as my econometrics professor at school! I am falling in love with econometrics and R language

[huovfiy]

Hello Martin, Thanks a lot for your vlogs. They helped me a lot to get a grip on "R". Since I am new to "R", I have a basic question. I have to forecast the number of smart navigation system (Explained Variable) for several countries up to 2020. My explanatory variables are the number of cars and GDP per head in each given country. My explained variables are Austria_SNS, Belgium_SNS, Canada_SNS etc. with 3 data points for each country (2013, 2014 and 2015). My explanatory variables are Austria_Cars, Belgium_Cars, Canada_Cars, Austria_GDP, Belgium_GDP, Canada_GDP etc with data from 2000 up to 2020. I would like to run the same model for all individual countries in one go replacing the name of countries with a string (something like X_SNS= X_Cars+X_GDP where X= Austria, Belgium, Canada etc). Thanks for your time in advance, Qmars

[saulflores]

Thank you! Much clearer than my professor.

[jiangyanyi]

Thank you so much for the great videos. I do not understand what it means when the p value is not significant in the example where age and height are in the model. But later there is a high value of correlation between age and height. Thank you.

[elenourchi]

Hello there, great video! I have a question regarding when to omit a variable. On what grounds do we omit an explanatory variable for a multiple linear regression model? Also, if there is an explanatory variable that is a categorical factor will there be 2 equations for the model? Thanks for your help!

[oacho]

Hi, thank you so much! So nice of you to share your understanding of stat with the public. I was running some model to explain metabolic rate as a function of 3 variables and their interactions. When i look at the summary table those variables appear not significant. Instead when i look at the ANOVA table those variables become significant. Is this normal or am I missing something? I would like to hear your perspective on this. Thanks!

[somahousein]

Thank you for your great videos! I am looking for your video where you mean centre height and age. I am not sure if I missed it but I have watched all the videos up to 5.12 and was not able to hear where you mention it.

[yueqiu]

Thanks!!! It really helps my statistic final project!!