Academic Integrity: tutoring, explanations, and feedback — we don’t complete graded work or submit on a student’s behalf.

Researchers conducted a survey to study how well student age predicts number of

ID: 3437251 • Letter: R

Question

Researchers conducted a survey to study how well student age predicts number of hours worked (per week). Summary statistics of the following data set are

Mx = 21.67, sx = 2.50

My = 23.33, sy =11.69.

Age (X)

Number of Hours Worked per Week (Y)

18

10

20

15

21

20

23

35

23

40

25

20

4a.        The correlation between age and number of hours worked per week is 0.63. Use it to predict the standardized number of hours worked per week at X = 35 years .

4b.       Calculate the intercept of the non-standardized (raw-score) regression equation.

4c.        Calculate the slope of the non-standardized (raw-score) regression equation .

4d.        Use the non-standardized regression equation to predict number of hours worked for X = 27

Age (X)

Number of Hours Worked per Week (Y)

18

10

20

15

21

20

23

35

23

40

25

20

Explanation / Answer

Sol) From The Excel

a) Correlation coeff= 0.63

b) The Regression Model is y= -40.05+2.925(x)

c) Slope=2.295

d) whenx=27

The Regression Model is y= -40.05+2.925(27)=38.925

SUMMARY OUTPUT Regression Statistics Multiple R 0.626458 R Square 0.392449 Adjusted R Square 0.240562 Standard Error 10.18773 Observations 6 ANOVA df SS MS F Significance F Regression 1 268.1738 268.1738 2.583814 0.18324 Residual 4 415.1596 103.7899 Total 5 683.3333 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -40.0532 39.65232 -1.01011 0.369583 -150.146 70.0393 -150.146 70.0393 Age (X) 2.925532 1.820012 1.607425 0.18324 -2.12763 7.978695 -2.12763 7.978695
Hire Me For All Your Tutoring Needs
Integrity-first tutoring: clear explanations, guidance, and feedback.
Drop an Email at
drjack9650@gmail.com
Chat Now And Get Quote