1. Which variables were significant in your model? (Use an alpha of 1% to draw your conclusions.)2. In the regression model you ran, the binary variables were defined as Female = 0, Male = 1. Using ONLY the information from your regression output, write the regression equation for a model with the OPPOSITE definition of the binary variables (Male = 0, Female = 1). SUMMARY OUTPUTRegression StatisticsMultiple R0.357056623R Square0.127489432Adjusted R Square0.122063371Standard Error5.662140623Observations810ANOVAdfSSMSFSignificance FRegression5 3766.35143753.270285723.4957624.4032E-22Residual804 25776.108532.05983643Total809 29542.4599CoefficientsStandard Errort StatP-valueLower 95%Upper 95% Lower 95.0% Upper 95.0%Intercept-0.015534766 0.42318834-0.036708871 0.97072625-0.8462192 0.81514964-0.8462192 0.81514964Gender (Male = 1)0.9858707430.40146992.455652934 0.01427351 0.19781787 1.77392362 0.19781787 1.77392362Exercise-0.00742009 0.00118168-6.279247486 5.5636E-10-0.0097396-0.0051005-0.0097396-0.0051005Fast food0.353544316 0.073355894.8195761491.72E-06 0.20955264 0.49753599 0.20955264 0.49753599Alcohol0.194136078 0.084986572.284314702 0.02261287 0.02731432 0.36095783 0.02731432 0.36095783Junk Food0.330639265 0.083984933.9368880068.9697E-05 0.16578365 0.49549488 0.16578365 0.49549488

Introduction: A variable in a regression model would be considered to be significant, if it's…

Answered: 1. Which variables were significant in…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Question

1. Which variables were significant in your model? (Use an alpha of 1% to draw your conclusions.)

2. In the regression model you ran, the binary variables were defined as Female = 0, Male = 1. Using ONLY the information from your regression output, write the regression equation for a model with the OPPOSITE definition of the binary variables (Male = 0, Female = 1).

SUMMARY OUTPUT
Regression Statistics
Multiple R
0.357056623
R Square
0.127489432
Adjusted R Square
0.122063371
Standard Error
5.662140623
Observations
810
ANOVA
df
SS
MS
F
Significance F
Regression
5 3766.35143
753.2702857
23.495762
4.4032E-22
Residual
804 25776.1085
32.05983643
Total
809 29542.4599
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95% Lower 95.0% Upper 95.0%
Intercept
-0.015534766 0.42318834
-0.036708871 0.97072625
-0.8462192 0.81514964
-0.8462192 0.81514964
Gender (Male = 1)
0.985870743
0.4014699
2.455652934 0.01427351 0.19781787 1.77392362 0.19781787 1.77392362
Exercise
-0.00742009 0.00118168
-6.279247486 5.5636E-10
-0.0097396
-0.0051005
-0.0097396
-0.0051005
Fast food
0.353544316 0.07335589
4.819576149
1.72E-06 0.20955264 0.49753599 0.20955264 0.49753599
Alcohol
0.194136078 0.08498657
2.284314702 0.02261287 0.02731432 0.36095783 0.02731432 0.36095783
Junk Food
0.330639265 0.08398493
3.936888006
8.9697E-05 0.16578365 0.49549488 0.16578365 0.49549488

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