Question

Invoices	Time
103	1.5
173	2.0
149	2.1
193	2.5
169	2.5
29	0.5
188	2.3
19	0.3
201	2.7
58	1.0
110	1.5
83	1.2
60	0.8
25	0.4
60	1.8
190	2.9
233	3.4
289	4.1
45	1.2
70	1.8
241	3.8
163	2.8
120	2.5
201	3.3
135	2.0
80	1.7
77	1.7
222	3.1
181	2.8
30	1.0
61	1.9
120	2.6

You are an accountant for a large department store and have been given the assignment of developing a model to predict the amount of time it takes to process invoices. Data is collected daily for 32 consecutive working days, and the number of invoices processed and completion time (in hours) are stored under the tab Invoice in the Excel data file for this homework.

a. Create the appropriate scatter plot and calculate the coefficient of correlation. Comment on the results. Does it appear a linear relationship exists?

b. Use the least-squares method to compute the regression coefficients b₀ and b₁.

c. Interpret the meaning of b₀ and b₁ in this problem.

d. What if the predicted mean amount of time it would take to process 150 invoices? Include a confidence interval for this prediction.

e. Determine the coefficient of determination, R², and explain its meaning in this problem.  

g. Perform a complete residual analysis. Does the analysis support all the assumptions required for a valid model? Explain.

h. Based on your results in part g, what can you say about the validity of the prediction you made in part c?

h. What conclusions can you reach about the relationship between then number of invoices processed and the completion time? What actions, if any, would you take next?

Answer 1

From From From From Other ExistingRefresh BARAT Access web Text Sources. |Connections l'All、 EditLinks Sort Filte Reapply Text to Remove Data C Advanced | Columns Duplicates Validation▼ Get External Data Connections Sort & Filter Data Tools N11 1 SUMMARY OUTPUT 2 Regression Statistics 4 Multiple R 5 R Square 6 Adjusted R Square 7 Standard Error 8 Observations 9 10 ANOVA 0.371620284 0.138101636 0.10937169 3.938579801 32 MSF gnificance f df 1 74.56642 74.56642 4.806888 0.03624 30 465.3723 15.51241 31 539.9388 12 Regression 13 Residual 14 Total 15 16 17 Intercept 18 invoices 19 20 21 Lower 95%Upper 95%ower 95.09 pper 95.0% P-value t Stat andard Err coefficients 0.070555252 140901 0.050074 0.960395 2.80703 2.948137 -2.80703 2,948137 0.021074603 0.009612 2.192462 0.03624 0.001444 0.040706 0.001444 0.040706 24 25 éet15 1Sheeto. SheetSheeti

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