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.

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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

**Data Representation and Analysis of Political Ideology and Party Affiliation**

The table below displays the distribution of individuals identifying as either Democrat or Republican across differing degrees of political ideology.

|        | 1 | 2  | 3  | 4  | 5 | 6 | 7 |
|--------|---|----|----|----|---|---|---|
| Democrat   | 5 | 18 | 19 | 25 | 7 | 7 | 2 |
| Republican | 1 | 3  | 1  | 11 | 10| 11| 1 |

### Statistical Model Using R

To model the effect of political ideology on the probability of being a Democrat, the following R code and results were generated:

```r
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)
```

**Summary Output:**

- **Estimates:**

  | Coefficient   | Estimate | Std. Error | z value | Pr(>|z|)  |
  |---------------|----------|------------|---------|-----------|
  | (Intercept)   | 3.1870   | 0.7002     | 4.552   | 5.33e-06  |
  | x             | -0.5901  | 0.1564     | -3.772  | 0.000162  |

- **Deviance Information:**
  - Null deviance: 24.7983 on 6 degrees of freedom
  - Residual deviance: 7.7894 on 5 degrees of freedom
  - Number of Fisher Scoring iterations: 4

- **Confidence Intervals (95%):**

  | Coefficient | 2.5 %   | 97.5 %  |
  |-------------|---------|---------|
  | (Intercept) | 1.90180 | 4.66484 |
  | x           | -0.91587| -0.29832|

This output provides insights into how political ideology, as an independent variable,
Transcribed Image Text:**Data Representation and Analysis of Political Ideology and Party Affiliation** The table below displays the distribution of individuals identifying as either Democrat or Republican across differing degrees of political ideology. | | 1 | 2 | 3 | 4 | 5 | 6 | 7 | |--------|---|----|----|----|---|---|---| | Democrat | 5 | 18 | 19 | 25 | 7 | 7 | 2 | | Republican | 1 | 3 | 1 | 11 | 10| 11| 1 | ### Statistical Model Using R To model the effect of political ideology on the probability of being a Democrat, the following R code and results were generated: ```r 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) ``` **Summary Output:** - **Estimates:** | Coefficient | Estimate | Std. Error | z value | Pr(>|z|) | |---------------|----------|------------|---------|-----------| | (Intercept) | 3.1870 | 0.7002 | 4.552 | 5.33e-06 | | x | -0.5901 | 0.1564 | -3.772 | 0.000162 | - **Deviance Information:** - Null deviance: 24.7983 on 6 degrees of freedom - Residual deviance: 7.7894 on 5 degrees of freedom - Number of Fisher Scoring iterations: 4 - **Confidence Intervals (95%):** | Coefficient | 2.5 % | 97.5 % | |-------------|---------|---------| | (Intercept) | 1.90180 | 4.66484 | | x | -0.91587| -0.29832| This output provides insights into how political ideology, as an independent variable,
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