R Programming Tutorial for Linear Regression

Fitting of Simple Linear Regression

Load the data into R. The procedure for loading of data files has been shown in previous R tutorials. Here, we use a data set from a pre-installed package “fma”.

Loading Data set from a Package in R

To load data from the package in R, use the commands:
>data (package=”fma”)
This instantly presents a list of all data sets available within the package.

Now, to load the data set “advert”, use the command:
>data(advert)

Now print the data in R by:
>advert

How to Use R software for carrying out Linear Regression Analysis?

To fit the data into a simple linear regression model using R programming we use the following commands:

Define a variable name (here, model) and use the command:
>model <- lm (sals~advert, data=advert)

To view the intercept and slope of regression in R, print 
> model

To print the entire summary of the regression using R software, use the command:
>summary (model)

The following command can be used to find the residuals of individual observations:
>residuals (model)

How to Draw a Scatter Plot using R?

To draw a scatter plot between sales and advert we use the following command:
>plot (sales~advert, data= advert, main=” Sales and Advertising expenditure”, xlab=” Advertising expenditure”, ylab=” sales”)

main is used to define the title of the scatterplot while xlab and ylab are used to set X-axis and Y-axis labels.

How to display the Line of best Fit in Linear Regression scatter plot in R?

Display line of best fit by using the following the command:
>abline(model)

How to use R programming software for Data FORECAST?

Forecast data when adverting expenditure is 30. Use the following command:
>predict(model, newdata=data.frame(advert=30))

Sample Assignment on Linear Regression Modelling in R

Learn R programming for Statistical Analysis

• Introduction to R programming
• How to Load Data set in R
• Linear Regression Modelling in R program
• Time series Analysis using R program
• Time series Forecasting and Decomposition using R
• Forecasting Linear Trend using R