Develop a multiple regression model to explain our DV, favorability toward Trump. Compute the model. Interpret the results, as appropriate. Paste your R code beneath your interpretation. Include any recoding or indexing you did.

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## Comments

## Ahmed and Greg the Adventurers

> summary(lm(V161087~V162171+Gender+V161310a+RaceResent+V162095+V162105))

Call:

lm(formula = V161087 ~ V162171 + Gender + V161310a + RaceResent +

V162095 + V162105)

Residuals:

Min 1Q Median 3Q Max

-83.430 -20.548 -0.777 20.627 93.336

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) -17.411917 2.312359 -7.530 6.46e-14 ***

V162171 -0.021716 0.014571 -1.490 0.136209

Gender -5.268408 0.977761 -5.388 7.60e-08 ***

V161310a 10.470125 1.323566 7.911 3.44e-15 ***

RaceResent 4.023865 0.110904 36.282 < 2e-16 ***

V162095 0.013724 0.004660 2.945 0.003254 **

V162105 0.033094 0.009279 3.567 0.000367 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 28.35 on 3403 degrees of freedom

(861 observations deleted due to missingness)

Multiple R-squared: 0.3372, Adjusted R-squared: 0.336

F-statistic: 288.6 on 6 and 3403 DF, p-value: < 2.2e-16

Women are 5.3 degrees less favorable toward Trump than men.

White people are 10 degrees more favorable toward Trump than non white people.

For every 1 point increase in racial resentment there is a 4 degree increase in favorability toward Trump.

For every 10 degree change in favorability toward Trump there is a .1 degree change in favorability toward Christian Fundamentalists.

For every 10 degree change in favorability toward Trump there is a .3 degree change in favorability toward rich people.

This model explains 34 percent of variance in this model.

## Negla and Brandon and Anna the baddieeees

> summary(lm(V161087~V162171+Gender+V161310a+RaceResent+V161228x+V161229x))

Call:

lm(formula = V161087 ~ V162171 + Gender + V161310a + RaceResent +

V161228x + V161229x)

Residuals:

Min 1Q Median 3Q Max

-81.453 -18.098 -0.316 17.913 83.220

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 4.41692 2.65818 1.662 0.0967 .

V162171 -0.03323 0.01398 -2.377 0.0175 *

Gender -3.92263 0.93418 -4.199 2.75e-05 ***

V161310a 12.78562 1.26798 10.083 < 2e-16 ***

RaceResent 2.97483 0.11764 25.288 < 2e-16 ***

V161228x -4.53647 0.26428 -17.165 < 2e-16 ***

V161229x 3.33636 0.48695 6.852 8.67e-12 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 26.7 on 3325 degrees of freedom

(939 observations deleted due to missingness)

Multiple R-squared: 0.4169, Adjusted R-squared: 0.4159

F-statistic: 396.3 on 6 and 3325 DF, p-value: < 2.2e-16

I added the variables of favoring or opposing laws to protect gays and lesbians against job discrimination and the scale of agreeing or disagreeing with the transgender bathroom policy. I added these two variables because people who often disagree with homosexuality and being transgender (as if it's a choice lmao) tend to favor policies that attack these types of people. Trump has not been extremely open with his feelings towards the LGBTQA+ community during his candidacy, but his running mate Mike Pence literally hates the gays. So I theorize other people who do not care or do not like members of the LGBTQA+ community will favor Trump and his presidency. The model is reliable, all the independent variables in the model have a unique effect on favorability towards Trump. This model accounts for 42% of the variation in the favorability towards Trump.

## Ball Busters! Long Live Laginas!

> summary(lm(V161087~V162171+Gender+V161310a+RaceResent+V161083))

Call:

lm(formula = V161087 ~ V162171 + Gender + V161310a + RaceResent +

V161083)

Residuals:

Min 1Q Median 3Q Max

-73.902 -14.489 -1.583 15.901 91.890

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) -36.82517 2.08014 -17.703 < 2e-16 ***

V162171 -0.01515 0.01262 -1.200 0.23008

Gender -4.75735 0.85247 -5.581 2.58e-08 ***

V161310a 3.34105 1.17334 2.847 0.00443 **

RaceResent 2.28840 0.11096 20.624 < 2e-16 ***

V161083 33.21809 1.01870 32.608 < 2e-16 ***

---

Signif. codes:

0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 24.94 on 3463 degrees of freedom

(802 observations deleted due to missingness)

Multiple R-squared: 0.4904, Adjusted R-squared: 0.4897

F-statistic: 666.6 on 5 and 3463 DF, p-value: < 2.2e-16

## ELISA N, LIZ B., CAT C., ALESSIA S.

Both variables that we chose were identity markers that may influence favorability towards Trump. Furthermore, agreeance and disagreeance towards allowing the President to strongly run the economy is important because Trump is often praised for his "Business-man" persona, and "money-oriented" thinking. Therefore, individuals who agree to having the president control the economy more may also do so because of these specific qualities.

## Election was focused on

Election was focused on Racial Resentment and Economic dissatisfaction

## kentonb.Samir F

> summary(lm(V161087~V162171+Gender+V161310a+RqaceResent+V162158+V162100+V162099+V162107+V162140))

Call:

lm(formula = V161087 ~ V162171 + Gender + V161310a + RqaceResent +

V162158 + V162100 + V162099 + V162107 + V162140)

Residuals:

Min 1Q Median 3Q Max

-83.345 -19.780 0.534 20.628 74.548

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 4.31709 4.34703 0.993 0.320760

V162171 -0.03883 0.01723 -2.254 0.024306 *

Gender -6.48355 1.16132 -5.583 2.64e-08 ***

V161310a 13.33565 1.72307 7.739 1.48e-14 ***

RqaceResent 2.94868 0.16528 17.840 < 2e-16 ***

V162158 -11.96986 0.60766 -19.698 < 2e-16 ***

V162100 0.04157 0.01105 3.761 0.000173 ***

V162099 -0.00945 0.01854 -0.510 0.610266

V162107 0.06228 0.01806 3.448 0.000574 ***

V162140 7.72809 0.74157 10.421 < 2e-16 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 27.71 on 2311 degrees of freedom

(1950 observations deleted due to missingness)

Multiple R-squared: 0.3668, Adjusted R-squared: 0.3643

F-statistic: 148.7 on 9 and 2311 DF, p-value: < 2.2e-16

## Brenda and Marta

> summary(lm(V161087~V162171+Gender+V161310a+V162095+V162094+V162099+V161196x))

Call:

lm(formula = V161087 ~ V162171 + Gender + V161310a + V162095 +

V162094 + V162099 + V161196x)

Residuals:

Min 1Q Median 3Q Max

-73.91 -15.19 -2.12 16.68 86.82

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 75.490224 2.105939 35.846 < 2e-16 ***

V162171 -0.004164 0.013221 -0.315 0.7528

Gender -4.279288 0.878666 -4.870 1.17e-06 ***

V161310a 12.520347 1.172365 10.680 < 2e-16 ***

V162095 0.021077 0.004321 4.878 1.12e-06 ***

V162094 -0.002515 0.003145 -0.800 0.4238

V162099 -0.026181 0.011021 -2.376 0.0176 *

V161196x -9.378160 0.185752 -50.488 < 2e-16 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 25.21 on 3343 degrees of freedom

(920 observations deleted due to missingness)

Multiple R-squared: 0.477, Adjusted R-squared: 0.4759

F-statistic: 435.5 on 7 and 3343 DF, p-value: < 2.2e-16

## > summary(lm(V161087~V162171

> summary(lm(V161087~V162171+Gender+V161310a+RaceResent+V161188r+V161194xr+V161196r+V161241+V162100))

Call:

lm(formula = V161087 ~ V162171 + Gender + V161310a + RaceResent +

V161188r + V161194xr + V161196r + V161241 + V162100)

Residuals:

Min 1Q Median 3Q Max

-80.25 -18.07 -0.62 17.93 93.44

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) -22.627228 3.437882 -6.582 5.36e-11 ***

V162171 -0.004196 0.013638 -0.308 0.758

Gender -5.224879 0.914336 -5.714 1.19e-08 ***

V161310a 10.580659 1.246739 8.487 < 2e-16 ***

RaceResent 3.072571 0.111689 27.510 < 2e-16 ***

V161188r -0.360458 0.430407 -0.837 0.402

V161194xr 2.797570 0.220642 12.679 < 2e-16 ***

V161196r 9.260205 0.656267 14.110 < 2e-16 ***

V161241 -8.399896 0.985960 -8.520 < 2e-16 ***

V162100 0.040826 0.009519 4.289 1.84e-05 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 26.44 on 3432 degrees of freedom

(829 observations deleted due to missingness)

Multiple R-squared: 0.4257, Adjusted R-squared: 0.4242

F-statistic: 282.7 on 9 and 3432 DF, p-value: < 2.2e-16

> I chose building wall with mexico and favorability towards birthright citizenship, imp of gun access, and favor towards big business. I chose these bec they all deal with issues that affect our freedom as citizens. Building a wall would most likely favor trump and those who did not favor birthright citizenship would also favor trump. Gun access and big business would also affect favor towards trump.

the model is reliable and