A survey is conducted on 700 Californians older than 30 years of age. The study wants to obtain inference on the relationship between years of education and yearly income in dollars. The response variable is income in dollars and the explanatory variable is years of education. A simple linear regression model is fit, and the output from R is below: lm(formula = Income ~ Education, data = CA) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 25200.25 1488.94 16.93 3.08e-10 *** Education 2905.35 112.61 25.80 1.49e-12 *** Residual standard error: 32400 on 698 degrees of freedom Multiple R-squared: 0.7602 Now say using the same dataset an analysis is done but using Euros as the unit of measurement of income, not US dollars (1 dollar=0.85 euros). Thus this model is all in units of measurement being euros for income. What happens to the slope estimate on Education? Group of answer choices Will be larger. Stays the same. Will be 0. Will be smaller.
A survey is conducted on 700 Californians older than 30 years of age. The study wants to obtain inference on the relationship between years of education and yearly income in dollars. The response variable is income in dollars and the explanatory variable is years of education.
A simple linear regression model is fit, and the output from R is below:
lm(formula = Income ~ Education, data = CA)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 25200.25 1488.94 16.93 3.08e-10 ***
Education 2905.35 112.61 25.80 1.49e-12 ***
Residual standard error: 32400 on 698 degrees of freedom
Multiple R-squared: 0.7602
Now say using the same dataset an analysis is done but using Euros as the unit of measurement of income, not US dollars (1 dollar=0.85 euros). Thus this model is all in units of measurement being euros for income.
What happens to the slope estimate on Education?
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