I have no time to study for my finals and do this at the same time. Looking for someone to help me finish this. Will donate all my fg to you
pm or post if interested
i need it done soon

The Excel file contains the following variables:

Price: home price (measured in $)
Lot.Size: size of lot (measured in acres)
Age: age of home (in years)
Land.Value: value of land (measured in $)
Living.Area: size of home (measured in 100 ft2)
Bedrooms: number of bedrooms
Bathrooms: number of bathrooms
Rooms: number of rooms
Fireplace: categorical variable (“Yes” if home has fireplace; “No” otherwise)
Waterfront: categorical variable (“Yes” if the property is on a waterfront; “No” otherwise)
New.Construction: categorical variable (“Yes” if the home is a new construction; “No” otherwise)
Central.Air: categorical variable (“Yes” if home has central air conditioning; “No” otherwise)

Part I
Researchers would like to build a regression model to help predict the price of a house. They have information on the total living area in the house, the number bedrooms, and the age of the home. The regression model becomes:

Price= β_0+β_1 Living.Area+β_2 Bedrooms+β_3 Age+ε

Run the regression as indicated above.
Write down the null hypothesis of a test of the overall significance of the regression.
Interpret the results of the overall significance of the test in the context of the problem.
Carry out a t-test for the slope coefficient on bedrooms. Interpret the results in the context of the problem.
Interpret the slope on bedrooms in the context of the problem.

Part II
Researchers would like to see if a difference in price exists between homes located on a waterfront and those that are not (after controlling for the other variables in the model). Using the variable Waterfront, create a dummy variable in Excel called NW where:

NW = 1 if home is not located on a waterfront
NW = 0 otherwise

The regression model becomes:

Price= β_0+β_1 Living.Area+β_2 Bedrooms+β_3 Age+α_0 NW+ε

Estimate the regression above.
Carry out a t-test for the coefficient on the dummy variable NW. Interpret the results in the context of the problem.
Interpret the coefficient on the dummy variable NW in the context of the problem.

Part III
Researchers would like to see if the relationship between the living area and the price of the house differs between homes located on a waterfront and those that are not. Create a dummy variable interaction term in Excel using the dummy variable NW interacted with living area:

NW*Living.Area

The regression model becomes:

Price= β_0+β_1 Living.Area+β_2 Bedrooms+β_3 Age+α_0 NW+α_1 NW*Living.Area+ε

Carry out a t-test for the coefficient on the dummy interaction term. Interpret the results in the context of the problem.

This post was edited by iCK on Dec 13 2016 04:59pm