Simple Linear Regression in R Programming | Fitting Linear Model in R
This video is about simple linear regression in R. The simple linear regression model is used to predict the outcome of response variable or dependent variable on the basis of one explanatory variable or independent variable. lm () function is used to fit a linear model in R. The model can be saved with any object name. The values for intercept or alpha and beta or regression coefficient can be obtained by printing out the model.
The summary function can be used to get the summary of a linear regression model. With summary function, you can get values of minimum, maximum, first quantile of residuals. Similiary, standard errors of intercept and regression coefficient, p values and t statistic can also be obtained using the summary of model in r. It also gives values of coefficient of determination or r squared and adjusted r squared.
Rscript/codes used in this tutorial
https://drive.google.com/file/d/1ujWI...
To download Data used in this video
https://drive.google.com/file/d/1_XSF...
To learn basics of R Programming
• Basics of R Programing
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