Question

A realty company would like to develop a regression model to help set weekly rental rates for beach properties during the summer season. The independent variables for this model will be the size of the property in square feet, the number of bedrooms it has, the number of balthrooms it has, and its age. Use the accompanying data, which are from randomly selected rental properties, to complete parts a through d below EER Click the icon to view the data table. a. Check for the presence of multicollinearity. Find the variance inflation factor (VIF) for each independent variable. Independent Variable Bedrooms VIE x) x2) (X3) Age Bathrooms Square Feet (Round to one decimal place as needed.) (Xa) There presence of multicollinearity in the model, because of the VIFS is/are greater than 5.0 b. If multicollinearity is present, take the necessary steps to eliminate it What independent variables need to be eliminated from the model? Select all that apply A. x, (Bedrooms) B. X (Age) C. x (Bathrooms) 3 D. x (Square Feet) E. No variables need to be eliminated because there significant source of multicolinearity is no c. Perform a best subsets regression and choose the most appropriate model t for these data. Which independent variables should be included in the model? Select all that apply x, (Bedrooms) X (Age) x (Bathrooms) X, (Square Feet) d. Identify the regression equation for the model in part c. Let y be the predicted Rental (S) Select the correc answer boxes to complete your choice (Round to one decimal place as needed) hoice below and il in the t c -t( )-Ds-Ds O A. ya OB. Oc. --x ,- OD 9--()x D Bathrooms Square Feet 1,300 1,900 1,200 1,700 1,800 2,900 2,000 2,200 Rental (S) Bedrooms Age 875 900 900 1,090 1,175 1,250 13 5 15 14 1.5 13 1.5 12 1.5 1,400 1,400 1.500 1,600 1,700 1,800 1.900 25 25 7 25 1,600 1,800 2,000 2,700 2,300 2,500 2200 2,900 3,200 2,300 2,300 2500 2,800 3.000 7 25 25 25 25 25 35 12 8 21 2.000 2,000 2.200 2,300 2.500 2,600 3,000 6 10 4 6 45 13 4 10 5 25 4 4 4 15 4 3.200 5 6 11 3,500 10 2 4,000 4,500 5.000 7.000 1.475 3,500 2,000 3.000 3,400 2,100 1,800 2,700 3,000 2,400 11 4.5 10 45 5 55 1 5 15 5 6 1,900 2.250 3 5 5 19 25 2 2 2.300 2,525 2.700 2,700 2,800 2.900 3,000 3,000 3.400 3.600 11 2 2 25 4 10 3 8 1,600 1,700 1,900 3 5 2.5 4 1.5 3 4 12 15 14 4 2,200 1,800 2,000 2,100 2,500 3,000 2,700 3,400 3,100 2,700 1,800 3,100 3,200 3,700 3,000 2,400 3.800 2,700 3,500 3,500 2,900 3,100 1,400 1,700 1800 35 25 35 4 14 4 12 6 3,700 4,000 4.300 4,700 4 12 10 35 3 11 10 25 35 4,900 5,000 5,200 5,700 6,000 6,500 7,000 7,200 7,600 8,000 9,000 10,000 12,900 1,300 1,700 1,800 1.900 2.000 2,100 2.200 3 16 4.5 17 45 6 3 55 6 45 35 3.5 3.5 6 4 5 11 10 4 6 17 4 12 5 14 22 17 5 10 5 1.5 2 15 11 17 25 3 3 2 15 2 25 2,100 2,000 2.300 2700 3 11 10 6 12 232mmら 寸mm 2.200 12 2 4 2.5 3 2.5 2.5 3.5 2,700 2,500 2,500 2,800 2,800 2,400 2,400 2,700 3,200 3,400 2,200 2,800 3,100 2,900 3,500 2,100 2,600 3,100 3,600 2,900 3,000 2,500 3,100 3,100 3,600 2,600 Square Feet 2,400 2,800 2,900 3,000 3,200 3,400 3.500 3,600 3,700 4,000 4.100 4,250 4,500 4,500 4,500 4,800 5,000 5,400 5,770 6,000 7,800 8,000 9,000 10,500 12,000 Rental ($) 6 6 14 12 11 2.5 2 2.5 11 15 10 3 20 3 2.5 3 2.5 4.5 4.5 5.5 5.5 3.5 5.5 6 4.5 4 2 Bedrooms Age Bathrooms 241942872 444 444555 445 5545565 5455n64

Answer 1

Could you please provide me the raw data set in .csv or .xls format so that I can solve it properly, otherwise I am giving you the R code.

Y=c(...) #Rental

X1=c(...) #Bedrooms

X2=c(...) #Age

X3=c(...) #Bathrooms

X4=c(...) #Square Feet

reg=lm(Y~X1+X2+X3+X4)

library(car)

vif(reg)

Conclusion:

Multicollinearity is present for that variable for which the VIF>5.0. So, we eliminate that variable and re-fit the regression model.

for example, if VIF of X1 is greater than 5, then our new R-code will be like this

reg1=lm(Y~X2+X3+X4)