According to the output, three variables (section, bed and pool) are insignificant because the p-value of them are larger than 0.05. The relationship between the selling price and variables should be: Y= -49.59+4.04X1+32.97X2+11.09X3+29.15X4+22.52X5+12.92X6-25.66X7+1.59X8 X1=lot size
X2=number of bathrooms
X3= number of other rooms
X4= number of stories
X5 =number of fireplaces
X6 = car garages
X7 =whether or not the lot is fenced
Q4: Based on the results of the estimation in step 1, answer the following questions: a. How do you interpret the intercept, the coefficient of lot size, beds and other variables? The intercept (-49.58856) is the expected mean value of Price Y when all variables X=0. And the coefficient means that if all other variables are fixed, when lot size change 1 unit, the price Y changes 4.05389 units. So do the others. b. What does the sign of the coefficient tell you?
The Fence has negative sign on the coefficient, which means that as the Section, Fence increase, the Price would decrease. And the others (Lot size, Bath, Other, Stories, Fireplaces, Cars, Age) have positive sign on the coefficient, which means that as these variables increase, the Price would increase. These three variables: Section, Bed, Pool are insignificant. c. Based on the relationship you have estimated, how do you determine if a variable such as lot size has a significant (statistically significant) impact on sales price? Base on the P-value. Assume the confident is 95%, P-value should be less than 0.05. Otherwise, the variables are insignificant. d. Are the signs and the magnitude of the coefficients consistent with intuition? – Why/why not?
There is a little bit difference.
Section of the town, pool and number of bedrooms should be significant variables in real world but they have no influence on Price according to the result. e. Does the model fit the data well – what criteria do you use to assess goodness of fit? Yes, the...