A professor is interested in determining the impact of hours worked per day by students (variable X) on final quiz scores (Y). A random sample of ten students is taken with the following results on X and Y:

XY

010

80

29

67

63

010

45

27

83

45

Determine the simple regression equation (by hand) that best fits this data.

Calculate and interpret the R-Square and the adjusted R-Square for this model.

Using a 0.05 level of significance, test to see if this is a good or bad model using the correlation test.

Using a 0.05 level of significance, test to see if this is a good or bad model using the t-test on slope.

Using a 0.05 level of significance, test to see if this is a good or bad model using the F-test on slope.

Looking over your full results, what can you conclude about the relationship between hours worked per day (X) and final quiz scores (Y)?

An educator believes that students choosing to major in mathematics, engineering, chemistry, economics, and philosophy have the same average ACT scores in the population. A random sample of students majoring in mathematics (X1), engineering (X2), chemistry (X3), economics (X4), and philosophy (X5) is taken and each are asked to report their ACT scores. The results are provided below:

X1X2X3X4X5

2833283528

2136211823

2230222226

2427192027

3230272719

3026302423

2528222020

29172126

232020

2624

26

Use the Analysis of Variance technique to test the educator's claim at the 0.05 level