Multiple regression question - I have a general equation for change in price per square foot of different homes (rural, city, suburb, where suburb is the reference variable):

Change_in_?price_per_sqft = -47.14 + 0.0059(footage) + 11.9(number_of_baths) - 6.1(distance) +

51.7(City) + 32.4(Rural) - 0.011(footage)(City) - 0.005(footage)(Rural) -

22.0(number_of_baths)(City) - 9.0(number_of_baths)(Rural) + 7.6(distance)(City) +

6.2(distance)(Rural)

I figured out what that equation would be for City homes, specifically:

City = -47.14 + 0.0059(footage) + 11.9(number_of_baths) - 6.1(distance) + 51.7 - 0.011(footage) + 22.0(number_of_baths) + 7.6(distance) Change in price/sq. ft., City=4.56 + 0.0048(footage) + 33.9(number_of_baths) + 1.5(distance)

A question had us calculate what the predicted change in price per square foot would be given specific values (Number_of_baths = 2 bathrooms, distance = 5 miles, footage = 2000 square feet), which I calculated as: Change in price/sq. ft. = 4.56 + 0.0048(2000) + 33.9(2) + 1.5(5) = ~$89.46

I am stumped on the follow up question:

What is the approximate prediction interval corresponding to the previous question? Show work.

I assume it wants us to do a 95% confidence interval or something, but I'm not sure. I am attaching an output I have from JMP, the statistics tool we're using. Hopefully that helps and massive thanks in advance for any help.

Response change_in_price_per_saft 4 Whole Model Parameter Estimates Term Estimate Std Error t Ratio Prob>|t| Lower 95% Upper 95% number_of_baths*location[Rural] 1.3369372 2.983884 0.45 0.6550 -4.577639 7.2515134 distance*location[City] 2.9978947 1.888381 1.59 0.1153 -0.745205 6.7409941 distance*location[Rural] 1.5805681 1.284993 1.23 0.2214 -0.966512 4.1276478 Effect Tests Sum of Source Nparm DF Squares F Ratio Prob > F footage 26.6016 0.1685 0.6822 number_of_baths 73.8418 0.4678 0.4954 distance 1 244.9298 1.5518 0.2156 location 2 1669.2810 5.2881 0.0064* footage*location 2 526.8644 1.6690 0.1932 number_of_baths*location 2 2108.2961 6.6789 0.0018* NN distance*location 2 955.6709 3.0275 0.0526 Indicator Function Parameterization Term Estimate Std Error t Ratio Prob> |t| Lower 95% Upper 95% Intercept -47.1351 9.183115 -5.13 <.0001 footage number_of_baths distance location>