3. Fit a logistic regression model using these variables. Use DRINK as the dependent variable and CASES and SEX as independent variables. Also include as an independent variable the appropriate interaction term. .* Some variable creation commands * Create 0/1 indicators of drinker and female gender generate drink01-. (294 missing values generated) replace drink01-1 if drink=-1 (234 real changes made) replace drink01=0 if drink--2 (60 real changes made) label define drinkf 0 "0=nondrinker" 1 "1=drinker" . label values drink01 drinkf • generate female-. (294 missing values generated) • replace female=0 if sex==1 (111 real changes made) • replace female=1 if sex==2 (183 real changes made) . label define sexf 0 "0=male" 1 "1=female" . label values female sexf .* Create a new variable called FEM_CASE that is the interaction of FEMALE and CASES • generate fem_case-female*cases * Use the command LOGISTIC if you want output to include ODDS RATIOS * Use the command LOGIT if you want the output to include BETAS and SEs * LOGISTIC OUTCOME PREDICTOR PREDICTOR etc.. . logit drink01 cases female fem_case Number of obs LR chi2 (3) 294 5.62 0.1318 0.0189 Logistic regression Prob > chi2 Log likelihood = -145.95772 Pseudo R2 drink01 | Coef. Std. Err. P>|z| [95% Conf. Interval] cases | -. 4405564 female | fem case | cons | .8413815 -0.52 0.601 -2.089634 1.208521 -.7743296 .3455196 -2.24 0.025 -1.451536 -.0971237 .9386327 .9578851 0.98 0.327 -.9387877 2.816053 1.826851 .2879632 6.34 0.000 1.262453 2.391248
3. Fit a logistic regression model using these variables. Use DRINK as the dependent variable and CASES and SEX as independent variables. Also include as an independent variable the appropriate interaction term. .* Some variable creation commands * Create 0/1 indicators of drinker and female gender generate drink01-. (294 missing values generated) replace drink01-1 if drink=-1 (234 real changes made) replace drink01=0 if drink--2 (60 real changes made) label define drinkf 0 "0=nondrinker" 1 "1=drinker" . label values drink01 drinkf • generate female-. (294 missing values generated) • replace female=0 if sex==1 (111 real changes made) • replace female=1 if sex==2 (183 real changes made) . label define sexf 0 "0=male" 1 "1=female" . label values female sexf .* Create a new variable called FEM_CASE that is the interaction of FEMALE and CASES • generate fem_case-female*cases * Use the command LOGISTIC if you want output to include ODDS RATIOS * Use the command LOGIT if you want the output to include BETAS and SEs * LOGISTIC OUTCOME PREDICTOR PREDICTOR etc.. . logit drink01 cases female fem_case Number of obs LR chi2 (3) 294 5.62 0.1318 0.0189 Logistic regression Prob > chi2 Log likelihood = -145.95772 Pseudo R2 drink01 | Coef. Std. Err. P>|z| [95% Conf. Interval] cases | -. 4405564 female | fem case | cons | .8413815 -0.52 0.601 -2.089634 1.208521 -.7743296 .3455196 -2.24 0.025 -1.451536 -.0971237 .9386327 .9578851 0.98 0.327 -.9387877 2.816053 1.826851 .2879632 6.34 0.000 1.262453 2.391248
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
Related questions
Question
![3. Fit a logistic regression model using these variables. Use DRINK as the dependent variable and CASES
and SEX as independent variables. Also include as an independent variable the appropriate interaction
term.
Some variable creation commands
.* Create 0/1 indicators of drinker and female gender
• generate drink01-.
(294 missing values generated)
. replace drink01=1 if drink--1
(234 real changes made)
. replace drink01=0 if drink==2
(60 real changes made)
• label define drinkf o "0=nondrinker" 1 "1-drinker"
label values drink01 drinkf
• generate female-.
(294
• replace female=0 if sex==1
(111 real changes made)
• replace female=1 if sex==2
(183 real changes made)
. label define sexf o "0=male" 1 "1=female"
• label values female sexf
values generated)
.* Create a new variable called FEM CASE that is the interaction of FEMALE and CASES
• generate fem_case=female*cases
* Use the command LOGISTIC if you want output to include ODDS RATIOS
* Use the command LOGIT if you want the output to include BETAS and SES
* LOGISTIC OUTCOME PREDICTOR PREDICTOR etc..
logit drink01 cases female fem_case
Logistic regression
Number of obs
294
LR chi2 (3)
Prob > chi2
5.62
0.1318
Log likelihood = -145.95772
Pseudo R2
0.0189
drink01 |
Coef.
Std. Err.
P>| z|
[95% Conf. Interval]
cases |
-.4405564
.8413815
-0.52
0.601
-2.089634
1.208521
female |
fem caseI
cons |
-.7743296
.3455196
-2.24
0.025
-1.451536
-.0971237
.9386327
.9578851
0.98
0.327
-.9387877
2.816053
1.826851
.2879632
6.34
0.000
1.262453
2.391248](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F5aabc011-aa34-4a44-ae78-8523442210ec%2Fa2b62f37-b974-4304-8949-eb1d0422be10%2Fai2uwh_processed.jpeg&w=3840&q=75)
Transcribed Image Text:3. Fit a logistic regression model using these variables. Use DRINK as the dependent variable and CASES
and SEX as independent variables. Also include as an independent variable the appropriate interaction
term.
Some variable creation commands
.* Create 0/1 indicators of drinker and female gender
• generate drink01-.
(294 missing values generated)
. replace drink01=1 if drink--1
(234 real changes made)
. replace drink01=0 if drink==2
(60 real changes made)
• label define drinkf o "0=nondrinker" 1 "1-drinker"
label values drink01 drinkf
• generate female-.
(294
• replace female=0 if sex==1
(111 real changes made)
• replace female=1 if sex==2
(183 real changes made)
. label define sexf o "0=male" 1 "1=female"
• label values female sexf
values generated)
.* Create a new variable called FEM CASE that is the interaction of FEMALE and CASES
• generate fem_case=female*cases
* Use the command LOGISTIC if you want output to include ODDS RATIOS
* Use the command LOGIT if you want the output to include BETAS and SES
* LOGISTIC OUTCOME PREDICTOR PREDICTOR etc..
logit drink01 cases female fem_case
Logistic regression
Number of obs
294
LR chi2 (3)
Prob > chi2
5.62
0.1318
Log likelihood = -145.95772
Pseudo R2
0.0189
drink01 |
Coef.
Std. Err.
P>| z|
[95% Conf. Interval]
cases |
-.4405564
.8413815
-0.52
0.601
-2.089634
1.208521
female |
fem caseI
cons |
-.7743296
.3455196
-2.24
0.025
-1.451536
-.0971237
.9386327
.9578851
0.98
0.327
-.9387877
2.816053
1.826851
.2879632
6.34
0.000
1.262453
2.391248
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