c. Calculate an approximate 95% confidence interval for the exponentiated coefficient of Police. Write a sentence that interprets this interval in context of the problem.
c. Calculate an approximate 95% confidence interval for the exponentiated coefficient of Police. Write a sentence that interprets this interval in context of the problem.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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
Transcribed Image Text:c. Calculate an approximate 95% confidence interval for the exponentiated coefficient of Police.
Write a sentence that interprets this interval in context of the problem.
d. Compute the expected count of homicides in Chicago (Chicago covariate values are at the start
of the problem).
![The Federal Bureau of Investigation (FBI), as part of its internal security mission,
serves as a database for all the reported crimes committed in the United States. One question
that might be of interest is does having more police officers reduce the number of homicides? To
investigate this claim, the 2013 data from the twenty largest municipal cities was supplied by the
FBI's Uniform Crime Report. Below are two observations from the dataset:
> head (cities)
City
Pop Homicides
Police
Density MedHome Mayor
1
New York
8175.133
335
4.2595
27016.30
5.5390
1
2
Chicago
2695.598
414
4.4309
11843.58
1.8310
The variables included in the study are:
Name of city
Population of city in 1,000s of people
Number of homicides in the year 2013
Number of police officers per 1,000 people
Number of people per square mile
Median home price in $100,000
1 if city has a "Strong Mayor" form of government; 0 if not
City
Рор
Homicides
Police
Density
MedHome
Мayor
Model: log(u;) = Bo + B1Police; + B2MedHome; + B3I(Mayor;) + log(Pop;)
mod1
glm (Homicides ~ Police + MedHome + Mayor, family=poisson, offset=log (Pop))
Coefficients:
Estimate Std. Error z value Pr(>]z|)
(Intercept) -2.19745
0.35530
-6.185 1.31e-05
Police
0.05424
0.11390
0.476 0.640352
i
MedHome
-0.28076
0.05630
-4.987 0.000134
Мayor
0.60000
0.31171
1.925 0.072219
(Dispersion parameter for poisson family taken to be 1)](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Ff2db349d-44e2-4dc0-ad50-be44fa6802da%2Ff1522eb7-55fa-4240-b96d-e85d6f29e489%2Fbj42f7g_processed.png&w=3840&q=75)
Transcribed Image Text:The Federal Bureau of Investigation (FBI), as part of its internal security mission,
serves as a database for all the reported crimes committed in the United States. One question
that might be of interest is does having more police officers reduce the number of homicides? To
investigate this claim, the 2013 data from the twenty largest municipal cities was supplied by the
FBI's Uniform Crime Report. Below are two observations from the dataset:
> head (cities)
City
Pop Homicides
Police
Density MedHome Mayor
1
New York
8175.133
335
4.2595
27016.30
5.5390
1
2
Chicago
2695.598
414
4.4309
11843.58
1.8310
The variables included in the study are:
Name of city
Population of city in 1,000s of people
Number of homicides in the year 2013
Number of police officers per 1,000 people
Number of people per square mile
Median home price in $100,000
1 if city has a "Strong Mayor" form of government; 0 if not
City
Рор
Homicides
Police
Density
MedHome
Мayor
Model: log(u;) = Bo + B1Police; + B2MedHome; + B3I(Mayor;) + log(Pop;)
mod1
glm (Homicides ~ Police + MedHome + Mayor, family=poisson, offset=log (Pop))
Coefficients:
Estimate Std. Error z value Pr(>]z|)
(Intercept) -2.19745
0.35530
-6.185 1.31e-05
Police
0.05424
0.11390
0.476 0.640352
i
MedHome
-0.28076
0.05630
-4.987 0.000134
Мayor
0.60000
0.31171
1.925 0.072219
(Dispersion parameter for poisson family taken to be 1)
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