# Dummy independent

Research question: Does gender affect smoking habits?

**Hypothesis:** A higher proportion of men smoke than women.

fre cgtsmke.

RECODE cgtsmke (1 2=1)(3 thru 5=0) into cgtsmke_dummy.

VARIABLE LABELS cgtsmke_dummy ‘Smokes or not’.

VALUE LABELS cgtsmke_dummy 0’No’ 1’Yes’.

fre cgtsmke cgtsmke_dummy.

fre gndr.

RECODE gndr (1=1)(2=0) into gndr_dummy.

VARIABLE LABELS gndr_dummy ‘Gender dummy’.

VALUE LABELS gndr_dummy 1’Male’ 0’Female’.

LOGISTIC REGRESSION cgtsmke_dummy WITH gndr_dummy.

**b0**: the log odds for category 1 of the dependent variable when the independent variable’s category is 0

b0: the log odds of smoking among women is -1.247.

**b1**: how much larger or smaller the log odds become as the independent variable increases by 1 unit.

b1: The log odds of smoking are -1.247 for females, but it increases by 0.784 if the respondent is male. The effect of gender on smoking is significant on the 5% significance level. In other words: the log odds of smoking among men are 0,784 times as high as among women.

**b0+b1**: the log odds for category 1 of the dependent variable when the independent variable’s category is 1.

b0+b1: the log odds of smoking among men is -0,463. (b0+b1*X=-1,247+0,784*1=-0,463)

**Exp(B) for b1**: is the odds ratio. -> category 1 … times than … category 0.

The odds of smoking are 2.191 times higher for males than for females.

The odds of smoking are (2.191-1)*100=119.1 % higher for males than for females.

**Exp(B) for b0: **the odds of smoking among women are 0,287.

**Conclusion**: The results support our hypothesis, stating that “A higher proportion of men smoke than women.” Why? Because the p-value of b1 is higher than 0,05 and the value of the Exp(b1) shows that the proportion of smokers among men is higher than among women.

**Other examples (dummy independent)**

1. smoking-gender

2. voting-domicil

3. paid job-gender

4. safety-gender

5. voting close-to-country

6. voting interest-in-politics

7. voting trust-in-politicians

8. voting gender

9. discrimination-nationality gender

10. Satisfaction-with-economy paid-job