variables. price: Sale price of a house. lotsize: Lot size of a property in square feet....

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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. A data frame containing 546 observations on 12 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: (40 points) Use the Credit data to perform the following tests using Linear Regression settings: A. Perform the following steps: (20 points) i. Attach the Credit data to the R environment. (5 points) ii. Observe the number of rows in the Credit data. Observe the dimension of the Credit data. (5 points) Provide a summary stat for the variables in Credit data. (5 points) What is the percentage of Student in the Credit data? What is the percentage of Female in the Credit data? (5 points) iii. iv. B. Construct a linear regression model as follows: (20 points) Response variable: Credit Card Balance Predictors: Credit Rating, Student, Credit Rating Student (interaction terms) Provide a summary of the model using summary() function. The online quiz (Q5 to Q8) will be related to the following concepts. You do not have to respond to the following questions in the R program: i. What is the effect of Student on Credit Card Balance? Explain the coefficient in terms of how Student status effect changes in Credit Card Balance. Explain the significance level of the coefficient of Student. 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. A data frame containing 546 observations on 12 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: (40 points) Use the Credit data to perform the following tests using Linear Regression settings: A. Perform the following steps: (20 points) i. Attach the Credit data to the R environment. (5 points) ii. Observe the number of rows in the Credit data. Observe the dimension of the Credit data. (5 points) Provide a summary stat for the variables in Credit data. (5 points) What is the percentage of Student in the Credit data? What is the percentage of Female in the Credit data? (5 points) iii. iv. B. Construct a linear regression model as follows: (20 points) Response variable: Credit Card Balance Predictors: Credit Rating, Student, Credit Rating Student (interaction terms) Provide a summary of the model using summary() function. The online quiz (Q5 to Q8) will be related to the following concepts. You do not have to respond to the following questions in the R program: i. What is the effect of Student on Credit Card Balance? Explain the coefficient in terms of how Student status effect changes in Credit Card Balance. Explain the significance level of the coefficient of Student.

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