Build a Housing Multiple Linear Regression Model Using R

The Multiple Linear Regression model (MLR) captures the relationship between multiple attributes of a data set (called predictors) and an attribute of the same data set (called target). Using an MLR model, we can then predict the unknown value of the target attribute given the known values of its predictors.

We will use R Studio and R to build an MLR for a housing data set Download housing data set.

Critical Thinking Assignment, complete these steps:

Step 1:

• Read the "housing.csv" file and assign it into a data frame called "housing". Hint: use read.csv() function with header = FALSE. For example, housing <- read.csv("C:\Users\yourname\Documents\CSUGlobal\MIS500\Week 2\housing.csv", header = FALSE).
• Your R code should be well commented and well organized to explain what the code does.
• Take screenshots to demonstrate successful completion of your work. The screenshot should show your well-commented R code and the results you obtained. The screenshot should have the current date and time.

Step 2:

• Display summary statistics for all attributes (columns) of the housing data set.
• Your R code should be well commented and well organized to explain what the code does.
• Take screenshots to demonstrate successful completion of your work. The screenshot should show your well-commented R code and the statistics test results you obtained. The screenshot should have the current date and time.

Step 3:

• Display the correlation numbers among the variables in the housing data set at the column positions of 1, 6, 11, and 14.
• Your R code should be well commented and well organized to explain what the code does.
• Take screenshots to demonstrate successful completion of your work. The screenshot should show your well-commented R code and the statistics test results you obtained. The screenshot should have the current date and time.

Step 4:

• Display a scatter matrix of the attributes of the housing data set at the column positions of 1, 6, 11, and 14.
• Your R code should be well commented and well organized to explain what the code does.
• Take screenshots to demonstrate successful completion of your work. The screenshot should show your well-commented R code and the results of the visual test you obtained. The screenshot should have the current date and time.

Step 5:

• Build an MLR model using the attribute of the housing data set at position 14 as the target attribute and the attributes at positions 1, 6, and 11 as the predictor.
• Your R code should be well commented and well organized to explain what the code does.
• Take screenshots to demonstrate successful completion of your work. The screenshot should show your well-commented R code and the statistics test results you obtained. The screenshot should have the current date and time.

Step 6:

• Display a summary of the performance of your MLR model.
• Your R code should be well commented and well organized to explain what the code does.
• Take screenshots to demonstrate successful completion of your work. The screenshot should show your R code and the statistics test results you obtained. The screenshot should have the current date and time.

Step 7:

Summarize your work on this assignment as follows:

• Interpret the results you obtained from your work. Interpretation of the statistics test results including visual test results should be relevant and accurate.