Data collected from the state of Florida includes:
y = crime rate = Annual number of crimes in county per 1000 population
x1 = education = Percentage of adults in county with at least a high school education
x2 = urbanization = Percentage in county living in an urban environment
Multivariate regression output from R is given below (note that the numbers are not the same as in the text book):
>summary(fitmc)
Call:
lm(formula = y ~ x1 + x2)
Residuals:
Min 1Q Median 3Q Max
-34.693 -15.742 -6.226 15.812 50.678
Coefficients:
Estimate Std.Error t value Pr(>|t|)
(Intercept) 42.8462 19.2550 xxxxx xxxxx
x1 -0.3990 0.4320 xxxxx xxxxx
x2 0.6431 0.1075 xxxxx xxxxxx
---
Residual standard error: 17.79 on 64 degrees of freedom
Multiple R-squared: 0.5305 Adjusted R-squared: 0.4549
F-statistic: 29.21 on 2 and 64 DF, p-value: 1.19e-09
NB: THE NUMBERS IN THIS QUESTION ARE NOT NECESSARILY THE SAME AS IN THE PREVIOUS QUESTION!!!!!!!!!!!!!
What is the estimate for the standard deviation of y given x1 and x2?
y = crime rate = Annual number of crimes in county per 1000 population
x1 = education = Percentage of adults in county with at least a high school education
x2 = urbanization = Percentage in county living in an urban environment
Multivariate regression output from R is given below (note that the numbers are not the same as in the text book):
>summary(fitmc)
Call:
lm(formula = y ~ x1 + x2)
Residuals:
Min 1Q Median 3Q Max
-34.693 -15.742 -6.226 15.812 50.678
Coefficients:
Estimate Std.Error t value Pr(>|t|)
(Intercept) 42.8462 19.2550 xxxxx xxxxx
x1 -0.3990 0.4320 xxxxx xxxxx
x2 0.6431 0.1075 xxxxx xxxxxx
---
Residual standard error: 17.79 on 64 degrees of freedom
Multiple R-squared: 0.5305 Adjusted R-squared: 0.4549
F-statistic: 29.21 on 2 and 64 DF, p-value: 1.19e-09
NB: THE NUMBERS IN THIS QUESTION ARE NOT NECESSARILY THE SAME AS IN THE PREVIOUS QUESTION!!!!!!!!!!!!!
What is the estimate for the standard deviation of y given x1 and x2?
