Final answer:
The coefficient on PPG in the regression equation will be 742 when the salary is measured in thousands of dollars since the scale for salary is multiplied by 1,000.
Step-by-step explanation:
If we adjust the units for the salary from millions to thousands in the regression equation, all the salaries will be scaled by a factor of 1,000 since there are 1,000 thousand dollars in one million dollars. Thus, the coefficient on PPG needs to be adjusted by the same factor to reflect this change in the scale of the dependent variable (salary).
Therefore, the new coefficient for PPG will be 0.742 thousand dollars, which is the same as saying 742 when salary is measured in thousands. The regression equation will remain structurally the same, except for the scale of the coefficients.
Regression coefficients, like means and proportions, are also point estimates and therefore can have confidence intervals. Calculating the confidence intervals for linear regression coefficients is straightforward and similar to the method for means and proportions just described. The difference lies in calculating the standard error for the coefficients.
The regression coefficients are interpreted as the effect of each variable on page costs, if all of the other explanatory variables are held constant. This is often “adjusting for” or “controlling for” the other explanatory variables. Because of this, the regression coefficient for an X variable may change (sometimes considerably) when other X variables are included or dropped from the analysis. In particular, each regression coefficient gives you the average increase in page costs per increase of 1 in its X variable, where 1 refers to one unit of whatever that X variable measures.