# Regression Modeling for Time to Process Invoices

See data file attached.

The manager of the purchasing department of a large banking organization would like to develop a model to predict the amount of time it takes to process invoices. Data are collected from a sample of 30 days, and the number of invoices processed and completion time, in hours, is stored in the file invoice.xls. This file can be found in the Resources section of this syllabus.

1. Assuming a linear relationship, use the least squares method to compute the regression coefficients b0 and b1.

2. Interpret the meaning of the Y intercept, b0, and the slope, b1, in this problem. Make your answer is comprehensive.

3. Determine the coefficient of determination, r2, and interpret its meaning.

4. Compute the Durbin Watson statistic and, at the .05 level of significance, determine whether there is any autocorrelation in the residuals.

5. Based on the results of (1) through (4), what conclusions can you reach concerning the validity of the model? Make sure your answer is comprehensive.

6. At the 95% confidence level, is there evidence of a linear relationship between the amount of time and the number of invoices processed? On what basis did you make your decision?

7. Construct a 95% confidence interval estimate of the mean amount of time it would take to process 150 invoices.

8. Construct a 95% prediction interval of the amount of time it would take to process 150 invoices on a particular day.

Please copy and paste your statistical output and answers in a word processing file.

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#### Solution Summary

The solution discusses regression modeling for time to process invoices for a large organization.