Using the regression model from the previous problem, what is the interpretation of the slope coefficient associated with Age?  Assume Salary is measured in $1,000's.

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Using the regression model from the previous problem, what is the interpretation of the slope coefficient associated with Age?  Assume Salary is measured in $1,000's.

SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R Square
0.389395911
0.151629176
0.107747926
Standard Error
12.21174238
Observations
62
ANOVA
df
SS
MS
Significance F
Regression
1545.896129
515.2987096
3.455443432
0.022109834
Residual
58
8649.345807
149.1266518
Total
61
10195.24194
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
93.51214154
25.87757288
3.613636487
0.000632425
41.71255147
145.3117316
41.71255147
145.3117316
Age
-0.238577414
1.093820589
-0.218113845
0.828106007 -2.428097211
1.950942383
-2.428097211
1.950942383
GPA
-12.79023531
3.997425287
-3.199618353
0.002232046
-20.7919514 -4.788519224
-20.7919514
-4.788519224
Gender num
1.32088149
3.130832692
0.421894627
0.674661647
-4.94616105
7.587924029
-4.94616105
7.587924029
Transcribed Image Text:SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square 0.389395911 0.151629176 0.107747926 Standard Error 12.21174238 Observations 62 ANOVA df SS MS Significance F Regression 1545.896129 515.2987096 3.455443432 0.022109834 Residual 58 8649.345807 149.1266518 Total 61 10195.24194 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 93.51214154 25.87757288 3.613636487 0.000632425 41.71255147 145.3117316 41.71255147 145.3117316 Age -0.238577414 1.093820589 -0.218113845 0.828106007 -2.428097211 1.950942383 -2.428097211 1.950942383 GPA -12.79023531 3.997425287 -3.199618353 0.002232046 -20.7919514 -4.788519224 -20.7919514 -4.788519224 Gender num 1.32088149 3.130832692 0.421894627 0.674661647 -4.94616105 7.587924029 -4.94616105 7.587924029
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