Logistic Regression – Age, Motorcycles & ED
Logistic Regression – Age, Motorcycles & ED
Dependent Variable: Presence/Absence of Erectile Dysfunction
Independent Variables: Age (20-29,…,50-59), Motorcycle Riding (Yes/No)
[pic]
Overall X=266+161=427 “Successes” and n=752+234=986 subjects [pic]
[pic]
Model 0:
Note: For model 0, we can compute: [pic]
[pic]
[pic]
Model 1:
[pic]
[pic]
LR Test for Age Effect. H0: βAge=0 HA: βAge≠0 TS: _________________ RR: ______
Wald Test for Age Effect. H0: βAge=0 HA: βAge≠0 TS: _______________ RR: _______
Model 2:
[pic]
[pic]
LR Test for MR Effect. H0: βMR=0 HA: βMR ≠0 TS: _________________ RR: ______
Wald Test for MR Effect. H0: βMR=0 HA: βMR≠0 TS: _______________ RR: _______
(These Control for Age)
Model 3:
[pic] [pic]
LR Test for Age*MR Interaction H0: βAM=0 HA: βAM ≠0 TS: ___________ RR: ______
Wald Test for Age*MR Interaction. H0:βAM=0 HA: βAM≠0 TS: __________ RR: _______
................
................
In order to avoid copyright disputes, this page is only a partial summary.
To fulfill the demand for quickly locating and searching documents.
It is intelligent file search solution for home and business.
Related searches
- logistic regression for longitudinal data
- multivariable logistic regression analysis
- univariable logistic regression model
- multivariable logistic regression model
- binary logistic regression analysis
- binary logistic regression equation
- binary logistic regression formula
- binary logistic regression 101
- binary logistic regression pdf
- multinomial logistic regression assumptions
- multinomial logistic regression stata
- multinomial logistic regression in sas