The excel data file named “Family-Residences Data” (posted in the content area u
ID: 2948678 • Letter: T
Question
The excel data file named “Family-Residences Data” (posted in the content area under Week IX) presents the sale price y (thousands), square footage (x1), number of rooms (x2), number of bedrooms (x3), and age (x4) for each of 63 single-family residences sold in Oxford, Ohio. Use any software of your choice to conduct a multiple regression analysis for this data set. Use the result of this analysis to answer the questions below.
1. Write a regression model that relates the dependent variable to the independent variables.
2. Interpret the error term in this model. What does it represent?
3. Identify the least squares point estimates of b0, b1, b2, b3, and b4 from your software output. Approximate these to four decimal places when necessary.
4. Write a multiple regression equation that relates sale price to square footage, number of rooms, number of bedrooms, and age.
5. Does the model explain a substantial portion of the variability in sale prices? Explain.
6. Do the signs and magnitudes of the estimated coefficients appear to be reasonable? Explain.
7. Write the multiple regression hypotheses to be tested.
8. Use F test to test the adequacy of the model with a = .05. Interpret the result of this test.
9. Use the p-value from your software output to test the importance of each of the independent variables x1, x2, x3, and x4 at a= .05. Which variables are not important? Explain.
10. Use the residential sales estimated equation to predict sales price of a residence that has 1700 square feet, seven rooms, and three bedrooms and is 15 years old.
Sales price, y (thousands) Square feet, x1 Rooms, x2 Bedrooms, x3 Age, x4 53.5 1008 5 2 35 49 1290 6 3 36 50.5 860 8 2 36 49.9 912 5 3 41 52 1204 6 3 40 55 1204 5 3 10 80.5 1764 8 4 64 86 1600 7 3 19 69 1255 5 3 16 149 3600 10 5 17 46 864 5 3 37 38 720 4 2 41 49.5 1008 6 3 35 103 1950 8 3 52 152.5 2086 7 3 12 85 2011 9 4 76 60 1465 6 3 102 58.5 1232 5 2 69 101 1736 7 3 67 79.4 1296 6 3 11 125 1996 7 3 9 87.9 1874 5 2 14 80 1580 5 3 11 94 1920 5 3 14 74 1430 9 3 16 69 1486 6 3 27 63 1008 5 2 35 67.5 1282 5 3 20 35 1134 5 2 74 142.5 2400 9 4 15 92.2 1701 5 3 15 56 1020 6 3 16 63 1053 5 2 24 60 1728 6 3 26 34 416 3 1 42 52 1040 5 2 9 75 1496 6 3 30 93 1936 8 4 39 60 1904 7 4 32 73 1080 5 2 24 71 1768 8 4 74 83 1503 6 3 14 90 1736 7 3 16 83 1695 6 3 12 115 2186 8 4 12 50 888 5 2 34 55.2 1120 6 3 29 61 1400 5 3 33 147 2165 7 3 2 210 2353 8 4 15 60 1536 6 3 36 100 1972 8 3 37 44.5 1120 5 3 27 55 1664 7 3 79 53.4 925 5 3 20 65 1288 5 3 2 73 1400 5 3 2 40 1376 6 3 103 141 2038 12 4 62 68 1572 6 3 29 139 1545 6 3 9 140 1993 6 3 4 55 1130 5 2 21Explanation / Answer
Sol:
install analysis tool pack excel.
Go to data>data analysis>Regression>Select y as sales price and remaining variables as x.you get the below output:
The rgression eq is
sale price=10.4041+0.049911*squarefeet+6.292436*rooms-11.0008bedrroms-0.43301*age
Solution2:
Error term is standad error of estimate
=18.95903
Solution3:
he least squares point estimates of b0, b1, b2, b3, and b4 from output are:
b0=10.4041
b1=0.0499
b2=6.2924
b3=-11.0008
b4=-0.4330
Solution4:
sale price=10.4041+0.049911*squarefeet+6.292436*rooms-11.0008bedrroms-0.43301*age
SUMMARY OUTPUT Regression Statistics Multiple R 0.851737 R Square 0.725456 Adjusted R Square 0.706522 Standard Error 18.95903 Observations 63 ANOVA df SS MS F Significance F Regression 4 55088.34 13772.08 38.31488 1.16E-15 Residual 58 20847.8 359.4448 Total 62 75936.13 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 10.4041 11.49694 0.904945 0.369238 -12.6095 33.41773 Square feet, x1 0.049911 0.008103 6.159486 7.43E-08 0.033691 0.066131 Rooms, x2 6.292436 2.527656 2.489436 0.015684 1.232783 11.35209 Bedrooms, x3 -11.0008 5.867606 -1.87483 0.065854 -22.7461 0.744511 Age, x4 -0.43301 0.109692 -3.94755 0.000216 -0.65258 -0.21344Related Questions
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