In: Statistics and Probability
Use the “home” data to build a regression model that predicts market as a function of square feet. Is the coefficient for square feet significant at a .05 level?
Home Market Value | ||
House Age | Square Feet | Market Value |
33 | 1,812 | $90,000.00 |
32 | 1,914 | $104,400.00 |
32 | 1,842 | $93,300.00 |
33 | 1,812 | $91,000.00 |
32 | 1,836 | $101,900.00 |
33 | 2,028 | $108,500.00 |
32 | 1,732 | $87,600.00 |
33 | 1,850 | $96,000.00 |
32 | 1,791 | $89,200.00 |
33 | 1,666 | $88,400.00 |
32 | 1,852 | $100,800.00 |
32 | 1,620 | $96,700.00 |
32 | 1,692 | $87,500.00 |
32 | 2,372 | $114,000.00 |
32 | 2,372 | $113,200.00 |
33 | 1,666 | $87,500.00 |
32 | 2,123 | $116,100.00 |
32 | 1,620 | $94,700.00 |
32 | 1,731 | $86,400.00 |
32 | 1,666 | $87,100.00 |
28 | 1,520 | $83,400.00 |
27 | 1,484 | $79,800.00 |
28 | 1,588 | $81,500.00 |
28 | 1,598 | $87,100.00 |
28 | 1,484 | $82,600.00 |
28 | 1,484 | $78,800.00 |
28 | 1,520 | $87,600.00 |
27 | 1,701 | $94,200.00 |
28 | 1,484 | $82,000.00 |
28 | 1,468 | $88,100.00 |
28 | 1,520 | $88,100.00 |
27 | 1,520 | $88,600.00 |
27 | 1,484 | $76,600.00 |
28 | 1,520 | $84,400.00 |
27 | 1,668 | $90,900.00 |
28 | 1,588 | $81,000.00 |
28 | 1,784 | $91,300.00 |
27 | 1,484 | $81,300.00 |
27 | 1,520 | $100,700.00 |
28 | 1,520 | $87,200.00 |
27 | 1,684 | $96,700.00 |
27 | 1,581 | $120,700.00 |
Use the “home” data to build a regression model that predicts market as a function of square feet. Is the coefficient for square feet significant at a .05 level?
Excel Addon Megastat used.
Menu used: correlation/Regression ---- Regression Analysis.
The regression line is Market Value=32,673.220+35.036* square foot
t =35.036/5.167 = 6.78, P=0.0000 which is < 0.05 level of significance. Ho is rejected.
There is enough evidence to conclude that coefficient for square feet significant at a .05 level.
Regression Analysis |
|||||||
r² |
0.535 |
n |
42 |
||||
r |
0.731 |
k |
1 |
||||
Std. Error of Estimate |
7287.723 |
Dep. Var. |
Market Value |
||||
Regression output |
confidence interval |
||||||
variables |
coefficients |
std. error |
t (df=40) |
p-value |
95% lower |
95% upper |
|
Intercept |
a = |
32,673.220 |
|||||
Square Feet |
b = |
35.036 |
5.167 |
6.780 |
0.0000 |
24.593 |
45.480 |
ANOVA table |
|||||||
Source |
SS |
df |
MS |
F |
p-value |
||
Regression |
2,441,633,668.922 |
1 |
2,441,633,668.922 |
45.97 |
0.0000 |
||
Residual |
2,124,436,092.983 |
40 |
53,110,902.325 |
||||
Total |
4,566,069,761.905 |
41 |