With all 4 variables in the model, for each unit increase in Expend what happens to Y and by how much.

With all 4 variables in the model, for each standard deviation change in Salary how much of a standard deviation change in Y occurs and in what direction.

Disregarding whether any of the predictor variables are significant, examine the results and tell me which predictor variable has the strongest relationship with Y and how do you know that.

Which of the 4 predictors accounts for a significant amount of the variance in Y?

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It is generally assumed that spending more money on education will lead to better prepared students. Gruber tested that assumption by collecting data for each of the 50 U.S. States. She recorded the average amount spent per pupil (Expend), the pupil/teacher ratio (Pratio), the average teacher's salary (Salary), the percentage of students in that state taking the SAT exam (PotSAT) and the combined SAT score (SAIcombined). A simultaneous multiple regression was computed on the variables with SAT combined was the dependent variable and the average amount spent per pupil (Expend), the pupil/teacher ratio (PTratio), the average teacher's salary (Salary) and the percentage of students in that state taking the SAT exam (PotSAD) as the predictor variables. Use the output to answer the following questions. Coefficients Model Unstandardized Coefficients Standardize Sig. Correlations Coefficients B Std. Error Beta Zero- Partial Part order (Constant) 1045.972 52.870 19.784 000 expend 4.463 10.547 .081 423 674 -.381 063 026 PTratio -3.624 3.215 -.110 -1.127 266 081 -. 166 -.070 salary 1.638 2.387 130 686 496 -.440 .102 043 PCTsat -2.904 231 -1.039 -12.559 000 -.887 .882 -.784 a. Dependent Variable: satcombined Model Summary Model R R Square Adjusted R Std. Error of the Square Estimate 908 825 809 32.702 a. Predictors: (Constant), PCTsat. PTratio. salary, expend