Part 1

HousePrices data set is a cross-sectional data set on house prices and other features, e.g., number of bedroom, of houses in Windsor, Ontario. The data were gathered during the summer of 1987.

Use the HousePrices data to perform the following tests using Linear Regression settings:

i.Construct a summary stat for all the variables in the HousePrices data.

ii.What is the percentage of houses in the data with Driveway, Gas-Heat and Air-conditioning present? (Hint: find the mean after creating dummy variables with driveway, gasheat, and aircon variables respectively).

iii.Construct a linear regression model to test whether number of bedrooms influence house prices. Provide a summary of the linear regression model using summary() function.

iv.Construct a multiple linear regression model by including all variables as predictors of house prices (response variable) and observe the effect on the house prices. Provide a summary of the regression model using summary() function.

Variable description of HousePrices data:A data frame containing 546 observations on 12 variables.

price: Sale price of a house.

lotsize: Lot size of a property in square feet.

bedrooms: Number of bedrooms.

bathrooms: Number of full bathrooms.

stories: Number of stories excluding basement.

driveway: Factor. Does the house have a driveway?

recreation: Factor. Does the house have a recreational room?

fullbase: Factor. Does the house have a full finished basement?

gasheat: Factor. Does the house use gas for hot water heating?

aircon: Factor. Is there central air conditioning?

garage: Number of garage places.

prefer: Factor. Is the house located in the preferred neighborhood of the city?

Part 2:

Use the Credit data to perform the following tests using Linear Regression settings:

A.Perform the following steps:

i.Attach the Credit data to the R environment.

ii.Observe the number of rows in the Credit data. Observe the dimension of the Credit data.

iii.Provide a summary stat for the variables in Credit data.

iv.What is the percentage of Student and Female in the Credit data?

B.Construct a linear regression model as follows:

Response variable: Credit Card Balance

Predictors: Credit Rating, Student, Credit Rating * Student (interaction terms)

Provide a summary of the model using summary() function.