## Another Logistic Regression

Logistic Regression

Let's test a model to predict voting for Trump in 2016.

First, some recodes:

Vote4Trump<-ifelse(V162034a==2,1,0) #This will be our DV

Gender<-'NA'; Gender[V161342==1]<-1; Gender[V161342==2]<-0

V161310r<-'NA'; V161310r[V161310a>=0]<-0; V161310r[V161310a==1]<-1

Now, the model:

summary(glm(Vote4Trump~Gender+V161310r+V161126,family=binomial))

exp(coef(glm(Vote4Trump~Gender+V161310r+V161126, family=binomial)))

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## Introduction to Logistic Regression

Using the 2016 ANES, build a model to predict political identity (to make it comparable to the model shown in class, recode political identity to Republican/Not Republican). Post your code below your interpretation so that we can see your recoding.

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## Introduction to the Linear Model (Multivariate Regression)

DATAFRAME: ANES 2012

ANES2016<-read.csv("http://www.shortell.nyc/online/files/anes_timeseries_2016.csv")

DV: Favorability to Democratic Party (ft_dem)

IVs: Political view (libcpre_self)

Attitude toward DACA (immig_citizen) * recoded

Attitude toward affirmative action (aa_uni) * recoded

Religiosity (relig_import)

Gender (gender_respondent)

RECODES

immig_citizenr<-ifelse(immig_citizen==1,1,0)

aa_unir<-ifelse(aa_uni==1,1,0)

## Group Exercise: Correlation and Linear Regression

Use the ANES 2016 to:

(a) examine the relationship between political view and a favorability variable with a correlation coefficient; and,

(b) compute a linear regression for the same variables and interpret the results.

How is the interpretation of the correlation and linear regression different?

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## More practice with factorial ANOVA

Post your interpretation the results of your factorial analysis of variance, based on the model shown in class.

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## Group Exercise: Factorial Analysis of Variance

Select a different DV from the class demonstration and compute a factorial analysis of variance and interpret the results.

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## Group Exercise: Analysis of Variance (F-test)

A. Select another categorical variable to use as an independent variable and test mean differences for our DV: V162098, FT Labor unions.

B. Next, select a new DV and run another test for mean differences.

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## Group Exercise: T-test

Identify a numeric dependent variable from ANES 2016. Select a binary independent variable to compare two groups. (You may need to recode a categorical variable into a binary.) Perform the hypothesis test and interpret the results, as appropriate.

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## Group Exercise: Confidence Interval

# ANES2012<-read.csv("http://www.courseserve.info/files/ANES2012r.csv")

# ANES2016<-read.csv("http://www.shortell.nyc/online/files/anes_timeseries_2016.csv")

In 2012, the mean favorability toward the Democratic Party was 54.9 degrees. We can use this to formulate a hypothesis about favorability toward the Democrats in 2016.

H_{0}: mu_{Dems} = 54.9

H_{1}: mu_{Dems} =/= 54.9

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## Group Exercise: Another Z-test

Let's say that we're studying household size in Kings County. The mean HH size is 4.2 persons, with a standard deviation of 1.8 persons.

You can compute the area under the normal curve here: http://www.stat.berkeley.edu/~stark/SticiGui/Text/clt.htm#normal_curve

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