R-studio and mutliple regression The variable percent is coded as the percent of
ID: 3333255 • Letter: R
Question
R-studio and mutliple regression
The variable percent is coded as the percent of registered voters that voted for Trump. Regress this variable on the following regressors: crime, education, income, an interaction between education and income, percent of registered Republicans (variable reg rep perc), and urbanization (variable Urb). Perform the necessary calculations and plot the effect of income for various levels of education (with a 95% confidence interval). Interpret this effect. Can you reject the null that income has no effect on voting for Trump? Explain.
Below is my R-stuido calculation.
Call:
lm(formula = percent ~ Crime + inc_educ + Income + Educ + reg_rep_perc +
Urb, data = mydata)
Residuals:
Min 1Q Median 3Q Max
-18.8585 -2.9473 0.6546 2.8104 18.4214
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 111.36219 46.67362 2.386 0.02086 *
Crime -0.12567 0.04653 -2.701 0.00942 **
inc_educ 0.02288 0.02743 0.834 0.40812
Income -1.81392 2.02593 -0.895 0.37489
Educ -0.93796 0.67868 -1.382 0.17310
reg_rep_perc 0.77102 0.09711 7.939 2.06e-10 ***
Urb -0.08314 0.05014 -1.658 0.10352
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 6.474 on 50 degrees of freedom
Multiple R-squared: 0.7936, Adjusted R-squared: 0.7688
F-statistic: 32.04 on 6 and 50 DF, p-value: 1.663e-15
Explanation / Answer
Here
The effect of income is based two explanatory variables which are 'Crime' and 'percent of registered Republicans'
since p value for crime = 0.00942 < 0.05 or 0.01 or 0.1 or 1
p value for reg rep = 2.06e-10 < all significant values
in this case
H0 : income has no effect on voting for Trump
H1 : income has some effect on voting for Trump
Yes we reject the null hypothesis, since p value = 1.663e-15 < all significant values
and we infer that income has some effect on voting for Trump
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