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(Solved): The following data are the monthly salaries and the grade point averages for students who obtained ...




The following data are the monthly salaries and the grade point averages for students who obtained a bachelors degree in bus
a. What should be the dependent variable, \( Y \) in this study? Salary or GPA?
b. What should be the independent variable, \
f. According to the estimated regression equation, by how much would the salary change if GPA increased by 1 point?
g. As GPA
The following data are the monthly salaries and the grade point averages for students who obtained a bachelor's degree in business administration. We want to investigate how GPA affects salaries using regression analysis. Answer the following questions. a. What should be the dependent variable, Y in this study? Salary or GPA? a. What should be the dependent variable, in this study? Salary or GPA? b. What should be the independent variable, in this study? Salary or GPA? c. Estimate the regression equation relevant for this study. "X (Use Analysis ToolPak and use 2 decimals) d. To test if the relation between and is significantly different from 0 , what is the -value that we should consider? (to two decimals) e. According to the estimated regression equation, by how much would the salary change if GPA increased by 0.5 points? (Use 2 ) decimals) 1. According to the estimated regression equation, by how much would the salary change if GPA increased by 1 point? f. According to the estimated regression equation, by how much would the salary change if GPA increased by 1 point? g. As GPA increases, in what direction does the salary move? Does it increase/decrease? h. What is the predicted salary for a GPA of 3 ? (use 2 decimals) 1. What proportion of the variation in salaries is explained by the variation in GPA? (use 2 decimals) J. Is the effect of GPA on salary significantly different from 0 according to this model?


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a. The dependent variable should be Salary in this study.

b. The independent variable should be GPA in this study.

c. The regression equation relevant for this study is:


Y^ = b0 + b1*X
where Y^ is the predicted value of Salary, b0 is the intercept, b1 is the slope, and X is the independent variable (GPA).


Using the given data to calculate the slope and intercept, we get:


b1 = 1337.5 b0 = -2743.75


Therefore, the estimated regression equation is:
Y^ = -2743.75 + 1337.5*X
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