Regression Statistics Multiple R 0.874642413 R Square Adjusted R Square Standard Error 0.764999351 0.744564512 62.97881926 Observations 51 ANOVA df Significance F Regression 593934.928 148483, 732 37.43603514 6.36772E-14 Residual 46 182451.2571 3966.331676 Total 50 776386.1851 Coefficients Standard Error 240.8002285 Upper 95% t Stat P-value Lower 95% Intercept 51.13392961 4.709206398 2.31605E-05 137.8729666 343.7274903 VEHICLE 3.042782981 1.582326221 1.922980824 0.06068593 -0.142274506 6.227840468 DIABETES 11.24212265 1.659066489 6.776173665 1.97533E-08 7.902595017 14.58165028 FLU 12.32304584 2.057012215 5.990749957 2.9897E-07 8. 182495007 16.46359668 HOMICIDE 1.362128456 2.113562817 0.644470297 0.522471501 -2.892252837 5.616509749

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ISBN:9781119256830
Author:Amos Gilat
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Chapter1: Starting With Matlab
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The US government is interested in understanding what predicts death rates.  They have a set of data that includes the number of deaths in each state, the number of deaths resulting from vehicle accidents (VEHICLE), the number of people dying from diabetes (DIABETES), the number of deaths related to the flu (FLU) and the number of homicide deaths (HOMICIDE).  Your run a regression to predict deaths and get the following output: 
Annually, the state of Hawaii has 11.2 vehicle accident deaths, 15.4 cases of diabetes-related deaths, 18.7 cases of flu-related deaths and 2.5 homicide-related deaths.  What is the predicted number of deaths for Hawaii? 

Regression Statistics
Multiple R
0.874642413
R Square
Adjusted R Square
Standard Error
0.764999351
0.744564512
62.97881926
Observations
51
ANOVA
df
Significance F
Regression
593934.928
148483, 732
37.43603514
6.36772E-14
Residual
46
182451.2571
3966.331676
Total
50
776386.1851
Coefficients Standard Error
240.8002285
Upper 95%
t Stat
P-value
Lower 95%
Intercept
51.13392961
4.709206398
2.31605E-05
137.8729666
343.7274903
VEHICLE
3.042782981
1.582326221
1.922980824
0.06068593
-0.142274506
6.227840468
DIABETES
11.24212265
1.659066489
6.776173665
1.97533E-08
7.902595017
14.58165028
FLU
12.32304584
2.057012215
5.990749957
2.9897E-07
8. 182495007
16.46359668
HOMICIDE
1.362128456
2.113562817
0.644470297
0.522471501
-2.892252837
5.616509749
Transcribed Image Text:Regression Statistics Multiple R 0.874642413 R Square Adjusted R Square Standard Error 0.764999351 0.744564512 62.97881926 Observations 51 ANOVA df Significance F Regression 593934.928 148483, 732 37.43603514 6.36772E-14 Residual 46 182451.2571 3966.331676 Total 50 776386.1851 Coefficients Standard Error 240.8002285 Upper 95% t Stat P-value Lower 95% Intercept 51.13392961 4.709206398 2.31605E-05 137.8729666 343.7274903 VEHICLE 3.042782981 1.582326221 1.922980824 0.06068593 -0.142274506 6.227840468 DIABETES 11.24212265 1.659066489 6.776173665 1.97533E-08 7.902595017 14.58165028 FLU 12.32304584 2.057012215 5.990749957 2.9897E-07 8. 182495007 16.46359668 HOMICIDE 1.362128456 2.113562817 0.644470297 0.522471501 -2.892252837 5.616509749
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