Report Properties
Owner: brunet42
Created: May 5, 2017
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week 15

independent variables : %graduate degree, foreign born, and veterans.

Result 1: Multiple Linear Regression   [Info]
Multiple linear regression results:
Dependent Variable: hrcpct
Independent Variable(s): gtba, foreign_born, veterans
hrcpct = 18.66721 + 1.5036299 gtba + 0.51318059 foreign_born + 0.00015208847 veterans

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept18.667211.3949751 ≠ 030313.381751<0.0001
gtba1.50362990.20555643 ≠ 03037.3149254<0.0001
foreign_born0.513180590.19386327 ≠ 03032.64712650.0085
veterans0.000152088470.000052936886 ≠ 03032.8730150.0044

Analysis of variance table for multiple regression model:
SourceDFSSMSF-statP-value
Model322564.6987521.56646.874288<0.0001
Error30348620.141160.46251
Total30671184.839

Summary of fit:
Root MSE: 12.66738
R-squared: 0.317

There is a moderatley strong positive relationship between all variables, based on the r value of .56

The relationships between hrcpct and the independent variables are all statistically significant, based on the p values for each. (they are all within the value of r)

the most important variable is the %of graduate degrees, because its tstat and pvalue. forgeign born is the next most important and veterans is the least important variable.

since the r squared value is .317, the chance of error is only reduced by 32%.

In this report i found that all three variables are staticially significant when looking at the influences of how the counties voted. I wasn't surprised to find that the graduate degree % was the most significant variable.I found similar in last weeks assignment. I thought veterans would be more significant, in a negative way, though.

The overall fit of the model
What does all of this say about the influences on how counties voted in 2016?

Data set 1. counties_sample.xls   [Info]