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In order to get a model with better predictive power, we can add other variables. The output below is from a multiple linear regression that adds two additional explanatory variables to the previous model: the percent of a state's population that self-identifies as white, and the median age of the state's population, both estimated from the Census Bureau. Use this output to answer the following. Coefficients: Eştimate Std. Error t value Pr(>It|) 7.87838 0.572 0.570 0.76989 0.05521 13.946 < 2e-16 *** 0.04283 4.679 2.56e-05 *** 0.957 4.50851 (Intercept) percent_vote percent_white 0.20039 median_age 0.01237 0.22683 0.055 Signif. codes: 0 ***** 0.001 **' 0.01 '*' 0.05 '.' 0.1 · '1 Residual standard error: 3.331 on 46 degrees of freedom Multiple R-squared: 0.8414, F-statistic: 81.33 on 3 and 46 DF, p-value: < 2.2e-16 Adjusted R-squared: 0.831 f. Assuming the other variables are in the model, which of the additional variables seem to be a useful predictor of the vaccination rate? Explain. g. Interpret the value of 0.20039 highlighted in the output above.

In order to get a model with better predictive power, we can add other variables. The output below is from a multiple linear regression that adds two additional explanatory variables to the previous model: the percent of a state's population that self-identifies as white, and the median age of the state's population, both estimated from the Census Bureau. Use this output to answer the following. Coefficients: Eştimate Std. Error t value Pr(>It|) 7.87838 0.572 0.570 0.76989 0.05521 13.946 < 2e-16 *** 0.04283 4.679 2.56e-05 *** 0.957 4.50851 (Intercept) percent_vote percent_white 0.20039 median_age 0.01237 0.22683 0.055 Signif. codes: 0 ***** 0.001 **' 0.01 '*' 0.05 '.' 0.1 · '1 Residual standard error: 3.331 on 46 degrees of freedom Multiple R-squared: 0.8414, F-statistic: 81.33 on 3 and 46 DF, p-value: < 2.2e-16 Adjusted R-squared: 0.831 f. Assuming the other variables are in the model, which of the additional variables seem to be a useful predictor of the vaccination rate? Explain. g. Interpret the value of 0.20039 highlighted in the output above.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
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In order to get a model with better predictive power, we can add other variables. The output below is from a multiple linear regression that adds two additional explanatory variables to the previous model: the percent of a state's population that self-identifies as white, and the median age of the state's population, both estimated from the Census Bureau. Use this output to answer the following. Coefficients: Estimate Std. Error t value Pr(>|t|) 4.50851 0.76989 7.87838 0.572 0.05521 13.946 < 2e-16 *** 0.04283 4.679 2.56e-05 *** 0.22683 0.570 (Intercept) percent_vote percent_white 0.20039 median_age 0.01237 0.055 0.957 signif. codes: 0 ****' 0.001 ***' 0.01 *** 0.05 *.' 0.1 · ' 1 Residual standard error: 3.331 on 46 degrees of freedom Multiple R-squared: 0.8414, F-statistic: 81.33 on 3 and 46 DF, p-value: < 2.2e-16 Adjusted R-squared: 0.831 f. Assuming the other variables are in the model, which of the additional variables seem to be a useful predictor of the vaccination rate? Explain. g. Interpret the value of 0.20039 highlighted in the output above.
Transcribed Image Text:In order to get a model with better predictive power, we can add other variables. The output below is from a multiple linear regression that adds two additional explanatory variables to the previous model: the percent of a state's population that self-identifies as white, and the median age of the state's population, both estimated from the Census Bureau. Use this output to answer the following. Coefficients: Estimate Std. Error t value Pr(>|t|) 4.50851 0.76989 7.87838 0.572 0.05521 13.946 < 2e-16 *** 0.04283 4.679 2.56e-05 *** 0.22683 0.570 (Intercept) percent_vote percent_white 0.20039 median_age 0.01237 0.055 0.957 signif. codes: 0 ****' 0.001 ***' 0.01 *** 0.05 *.' 0.1 · ' 1 Residual standard error: 3.331 on 46 degrees of freedom Multiple R-squared: 0.8414, F-statistic: 81.33 on 3 and 46 DF, p-value: < 2.2e-16 Adjusted R-squared: 0.831 f. Assuming the other variables are in the model, which of the additional variables seem to be a useful predictor of the vaccination rate? Explain. g. Interpret the value of 0.20039 highlighted in the output above.
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