Modelling Multiple Linear Regression Using R (research-oriented modelling

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.

Article Outline

• Dataset description
• Exploratory data analysis
• A simple linear regression model fitting
• Model interpretation
• MLR regression model fitting and interpretation
• Hypothesis testing
• Stepwise regression

Aim

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.

Dataset Description

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

Load Libraries

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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