Learn how to train an MLR model, interpret its coefficients and perform a stepwise regression. The aim of this article to illustrate how to fit a multiple linear regression model in the R statistical programming language and interpret the coefficients.
The aim of this article to illustrate how to fit a multiple linear regression model in the R statistical programming language and interpret the coefficients. Here, we are going to use the Salary dataset for demonstration.
The 2008–09 nine-month academic salary for Assistant Professors, Associate Professors and Professors in a college in the U.S. The data were collected as part of the on-going effort of the college’s administration to monitor salary differences between male and female faculty members [1].
The data frame includes 397 observations and 6 variables.
rank (I1): a factor with levels AssocProf, AsstProf, Prof
discipline (I2): a factor with levels A (“theoretical” departments) or B (“applied” departments).
yrs.since.phd (I3): years since PhD.
yrs.service (I4): years of service.
sex (I5): a factor with levels Female and Male
salary (D): nine-month salary, in dollars.
Where** I: Independent variable; **D: Dependent/Outcome variable
The first step is to start installing and loading R libraries
## use install.packages( ) function for installation
library(tidyverse) ## data loading, manipulation and plotting
library(carData) ## Salary dataset
library(broom) ## tidy model output
Print dataset details
Let’s print different inbuilt datasets offered by the carData package.
data(package = "carData")
machine-learning coefficient stepwise interpretation regression
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