Use regression analysis to examine the effect of Foreign Born Percent on Difference in Interest in Trump relative to Clinton Google searches. Before beginning this portion, make sure you have...

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Use regression analysis to examine the effect of Foreign Born Percent on Difference in Interest in Trump relative to Clinton Google searches. Before beginning this portion, make sure you have downloaded the "trumpclinton_googlesearch.csv", and variable explanations text as well as completed Code Video from the Week 4 Code folder.




1. Generate a new variable for “Foreign Born Percent”. What is the correlation between Foreign Born Percent and Difference in Interest in Trump relative to Clinton (hint: try to cor() function)?








2. Create a linear regression model that uses Foreign Born Percent to explain Difference in Interest in Trump Relative to Clinton. Make a scatter plot of Foreign Born Percent (x) on Difference in Interest in Trump Relative to Clinton (y) with proper axes labels, and add the regression line to your plot. Paste the plot below.








3. Look at the summary statistics for your regression model. Interpret the intercept, coefficient estimate, and R-squared below.










4. Construct a 95% confidence interval, and interpret what it means below.










5. Let’s now say we want to control for previous partisanship at the state level, and we decide to use Vote for Romney in 2010. Create a linear regression model that now includes this variable. Look at the summary statistics of your new regression model, and interpret the intercept, coefficient estimate, and R-squared below.










6. Construct a 95% confidence interval, and interpret what it means below.




Answered Same DayJun 01, 2021

Answer To: Use regression analysis to examine the effect of Foreign Born Percent on Difference in Interest in...

Himanshu answered on Jun 02 2021
144 Votes
Question 1:
The new variables namely ‘Foreign_born_per’ and ‘Difference_interest’ can be introduced
in R using the “mutate” function under “dplyr” library. The correlation coefficient between the two is 0.51. This means a medium positive relationship (Gujarati, Porter, & Gunasekar, 2012).
Question 2:
We run regression using “lm” function. The intercept comes out to be 0.47 and the slope comes out to be 1.43. The scatter plot with the fit line is provided below.
Question 3:
The result of the summary statistics is provided in the screenshot below. We observe that the intercept has an estimated value of 0.47 and significant at 0.1% level of significance. The slope has an estimated value of 1.43 and significant at 0.1% level. This means for a unit increase in foreign born percent the difference of interest increase by 1.43 times. Also,...
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