Regression Statistics Multiple R 0.427544459 R Square 0.182794265 Adjusted R Square 0.129498238 Standard Error 12.45749356 Observations 50 ANOVA Regression Residual Total df SS MS F Significance F 3 1596.799298 532.2664326 3.429791628 0.024596922 46 7138.700702 155.1891457 49 8735.5 Coefficients Standard Error Intercept 104.2460353 P-value 0.00064554 Lower 95% 46.93486971 Gender Numeric Age GPA 3.357643288 -0.518174564 -14.35589635 t Stat Upper 95% 28.47200105 3.661352608 161.5572009 46.93486971 161.5572009 3.578337563 0.938324915 0.352978384 -3.845176644 10.56046322 -3.845176644 10.56046322 1.161691886 -0.446051634 0.657652545 -2.85653905 1.820189921 -2.85653905 1.820189921 Lower 95.0% Upper 95.0% 4.563887699 -3.145541103 0.002904394 -23.54252581 -5.169266883 -23.54252581 -5.169266883
Regression Statistics Multiple R 0.427544459 R Square 0.182794265 Adjusted R Square 0.129498238 Standard Error 12.45749356 Observations 50 ANOVA Regression Residual Total df SS MS F Significance F 3 1596.799298 532.2664326 3.429791628 0.024596922 46 7138.700702 155.1891457 49 8735.5 Coefficients Standard Error Intercept 104.2460353 P-value 0.00064554 Lower 95% 46.93486971 Gender Numeric Age GPA 3.357643288 -0.518174564 -14.35589635 t Stat Upper 95% 28.47200105 3.661352608 161.5572009 46.93486971 161.5572009 3.578337563 0.938324915 0.352978384 -3.845176644 10.56046322 -3.845176644 10.56046322 1.161691886 -0.446051634 0.657652545 -2.85653905 1.820189921 -2.85653905 1.820189921 Lower 95.0% Upper 95.0% 4.563887699 -3.145541103 0.002904394 -23.54252581 -5.169266883 -23.54252581 -5.169266883
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
Using results from the previous regression model, discuss whether you would have confidence in this multiple regression model to predict expected salary based on GPA, age, and gender? Answers not supported by model statistics will be wrong

Transcribed Image Text:Regression Statistics
Multiple R
0.427544459
R Square
0.182794265
Adjusted R Square
0.129498238
Standard Error
12.45749356
Observations
50
ANOVA
Regression
Residual
Total
df
SS
MS
F
Significance F
3
1596.799298
532.2664326 3.429791628
0.024596922
46
7138.700702
155.1891457
49
8735.5
Coefficients
Standard Error
Intercept
104.2460353
P-value
0.00064554
Lower 95%
46.93486971
Gender Numeric
Age
GPA
3.357643288
-0.518174564
-14.35589635
t Stat
Upper 95%
28.47200105 3.661352608
161.5572009 46.93486971 161.5572009
3.578337563 0.938324915 0.352978384 -3.845176644 10.56046322 -3.845176644 10.56046322
1.161691886 -0.446051634 0.657652545 -2.85653905 1.820189921 -2.85653905 1.820189921
Lower 95.0% Upper 95.0%
4.563887699 -3.145541103 0.002904394 -23.54252581 -5.169266883 -23.54252581 -5.169266883
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