From the 2016 General Social Survey, when we cross-classify political ideology (with 1 being most liberal and 7 being most conservative) by political party affili- ation for subjects of ages 18–27, we get: Report the prediction equation and interpret the direction of the estimated effect. Construct the 95% Wald confidence interval for the effect of political ideology. Interpret and compare to the profile likelihood interval shown. Conduct the Wald test for the effect of x. Report the test statistic, P-value, and interpret.

MATLAB: An Introduction with Applications
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From the 2016 General Social Survey, when we cross-classify political ideology (with 1 being most liberal and 7 being most conservative) by political party affili- ation for subjects of ages 18–27, we get:

  1. Report the prediction equation and interpret the direction of the estimated effect.

  2. Construct the 95% Wald confidence interval for the effect of political ideology. Interpret and compare to the profile likelihood interval shown.

  3. Conduct the Wald test for the effect of x. Report the test statistic, P-value, and interpret.

answer 1,2,3 please

1
2
3
4
6
7
Democrat
5
18
19
25
7
7
Republican
1
3
1
11
10
11
1
When we use R to model the effect of political ideology on the probability of being
a Democrat, we get the results:
> y <- c(5,18,19,25,7,7,2); n <- c(6,21,20,36,17,18,3)
> x <- c(1,2,3,4,5,6,7)
> fit <- glm (y/n ~ x, family=binomial (link=logit), weights=n)
> summary (fit)
Estimate Std. Error
z value Pr(>|z|)
(Intercept)
3.1870
0.7002
4.552
5.33e-06
-0.5901
0.1564
-3.772 0.000162
Null deviance: 24.7983
on 6
degrees of freedom
Residual deviance:
7.7894
on 5
degrees of freedom
Number of Fisher Scoring iterations: 4
> confint (fit)
>
2.5 %
97.5 %
(Intercept)
1.90180
4.66484
-0.91587
-0.29832
Transcribed Image Text:1 2 3 4 6 7 Democrat 5 18 19 25 7 7 Republican 1 3 1 11 10 11 1 When we use R to model the effect of political ideology on the probability of being a Democrat, we get the results: > y <- c(5,18,19,25,7,7,2); n <- c(6,21,20,36,17,18,3) > x <- c(1,2,3,4,5,6,7) > fit <- glm (y/n ~ x, family=binomial (link=logit), weights=n) > summary (fit) Estimate Std. Error z value Pr(>|z|) (Intercept) 3.1870 0.7002 4.552 5.33e-06 -0.5901 0.1564 -3.772 0.000162 Null deviance: 24.7983 on 6 degrees of freedom Residual deviance: 7.7894 on 5 degrees of freedom Number of Fisher Scoring iterations: 4 > confint (fit) > 2.5 % 97.5 % (Intercept) 1.90180 4.66484 -0.91587 -0.29832
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