In: Statistics and Probability
End of Section Problem 13.12
Your answer is partially correct. Try again. | |
Use the following data to develop a regression model to predict
y from x1 and x2. Comment on the output.
Develop a regression model to predict y from x1 only. Compare the
results of this model with those of the model using both
predictors.What might you conclude by examining the output from
both regression models?
y | x1 | x2 |
28 | 12.6 | 134 |
43 | 11.4 | 126 |
45 | 11.5 | 143 |
49 | 11.1 | 152 |
57 | 10.4 | 143 |
68 | 9.6 | 147 |
74 | 9.8 | 128 |
81 | 8.4 | 119 |
82 | 8.8 | 130 |
86 | 8.9 | 135 |
101 | 8.1 | 141 |
112 | 7.6 | 123 |
114 | 7.8 | 121 |
119 | 7.4 | 129 |
124 | 6.4 | 135 |
(Round all answers to 4 decimal places.)
The regression equation for y from x1 and x2 is:
y=
+(
) x1 +(
) x2
F=
with p=
tx1=
with p=
tx2=
with p=
The regression equation for y from x1 only is:
y=
+(
) x1
F=
with p=
Using Excel, go to Data select Data Analysis, choose Regression. Put y values in Y input range. Put x1 and x2 values in X input range for first model and put x1 values in X input range for second model.
x1 and x2 model
SUMMARY OUTPUT | ||||||
Regression Statistics | ||||||
Multiple R | 0.9814 | |||||
R Square | 0.9632 | |||||
Adjusted R Square | 0.9570 | |||||
Standard Error | 6.3334 | |||||
Observations | 15 | |||||
ANOVA | ||||||
df | SS | MS | F | Significance F | ||
Regression | 2 | 12586.3823 | 6293.1912 | 156.8882 | 0.0000 | |
Residual | 12 | 481.3510 | 40.1126 | |||
Total | 14 | 13067.7333 | ||||
Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
Intercept | 243.4408 | 23.0195 | 10.5754 | 0.0000 | 193.2856 | 293.5961 |
X1 | -16.6079 | 1.0312 | -16.1047 | 0.0000 | -18.8548 | -14.3610 |
X2 | -0.0732 | 0.1870 | -0.3915 | 0.7023 | -0.4806 | 0.3342 |
The regression equation for y from x1 and x2 is:
y = 243.4408 + (-16.6079)x1 +(-0.0732)x2
F = 156.8882
with p = 0.000
tx1 = -16.1047
with p = 0.000
tx2 = -0.3915
with p = 0.7023
x1 model
SUMMARY OUTPUT | |||||
Regression Statistics | |||||
Multiple R | 0.9812 | ||||
R Square | 0.9627 | ||||
Adjusted R Square | 0.9598 | ||||
Standard Error | 6.1237 | ||||
Observations | 15 | ||||
ANOVA | |||||
df | SS | MS | F | Significance F | |
Regression | 1 | 12580.2357 | 12580.2357 | 335.4746 | 0.0000 |
Residual | 13 | 487.4976 | 37.4998 | ||
Total | 14 | 13067.7333 | |||
Coefficients | Standard Error | t Stat | P-value | ||
Intercept | 235.1429 | 8.6775 | 27.0980 | 0.0000 | |
X1 | -16.7678 | 0.9155 | -18.3160 | 0.0000 |
The regression equation for y from x1 and x2 is:
y = 235.1429 + (-16.7678)x1
F = 335.4746
with p = 0.000