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# Hello, I have a question about logistic regression.

In a survey study, the age of women, desire of having more children (1= yes and 0=No) and contraceptive use (1=yes and 0=No) information were collected. The researchers want to investigate whether there is any evidence that age and desire of having more children are associated with the contraceptive use. They analyze the data using SAS's LOGISTIC procedure and observe the results shown in the Table given below. Please let me know how to solve the following questions based on the info given.

1)  What are the dependent variable and the independent variables? And what are the Omnibus Null and Alternative hypotheses.

2)  Report the test statistic and P-value that should be used to test the Omnibus Null hypothesis (i.e. "Global Null" per SAS). What can we conclude about the Omnibus Null hypothesis?

3)  Report the odds ratios and 95% confidence intervals for all two independent variables. Based on those confidence intervals, which of the independent variables are significant predictors of contraceptive use and which ones are not significant predictors? Why?

4)  Report p-values of all two independent variables using "Analysis of Maximum Likelihood Estimates" table in the SAS output. Based on the p-values, which of the independent variables are significant predictors of contraceptive use and which ones are not significant predictors? Why? The reasoning should be based on comparison to Q3.

5)    What do these findings mean from public health perspective?

Table 2: Output for Part Two

Analysis of Maximum Likelihood Estimates
Standard
Wald
Parameter OF Estimate
Error Chi - Square Pr &gt; China
Intercept
3 8265
1.9197
3. 9732
0. 0462
More Child
20684
0 8 861
5. 4 488
0 0196
age
0 0959
0 0601
2 5470
0. 1 105
Odds Ratio Estimates
95% Wald
Effect
Point Estimate Confidence Limits
More Child
7.912 1 . 393 44 932
age
1.101 0.978
1238

Logistic Regression
The LOGISTIC Procedure
Model Information
Data Set
WORK CONTRACEPTIVE
Response Variable
CONTA
Number of Response Levels 2
Model
binary logit
Optimization Technique
Fisher's scoring
Number of Observations Used 32
Response Profile
Ordered
Total
Value CONTP Frequency
10
18
7
14
Probability modeled is CONTP = 0

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