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Required Resources

Hoerl, R., & Snee, R. (2012). Statistical thinking: Improving business performance (2nd ed.).

Hoboken, NJ: John Wiley and Sons, Inc.

Supplemental Resources

Balestracci, D. (1988). Data “sanity”: Statistical thinking applied to everyday data. Milwaukee, WI: American Society for Quality Statistics Division. Retrieved from http://www.donaldpoland.com/documents_and_links/5-Other_Documents/Statistical_Thinking.pdf.

Paulk, M. C., & Hyder, E. B. (2007). Common pitfalls in statistical thinking. Carnegie Mellon University. Retrieved from http://repository.cmu.edu/cgi/viewcontent.cgi?article=1000&context=isr.

Peck, R., Casella, G., Cobb, G. W., Hoerl, R., & Nolan, D. (2006). Statistics: A Guide to the unknown (4th ed.). Pacific Grove, CA: Duxbury Press.

Snee, R. D., & Hoerl, R.W. (2003). Leading six sigma: A Step-by-step guide based on experience with GE and other six sigma companies. Upper Saddle River, NJ: Financial Times / Prentice Hall.

Homework Assignment

Worth 30 points

The experiment data in below table was to evaluate the effects of three variables on invoice errors for a company. Invoice errors had been a major contributor to lengthening the time that customers took to pay their invoices and increasing the accounts receivables for a major chemical company. It was conjectured that the errors might be due to the size of the customer (larger customers have more complex orders), the customer location (foreign orders are more complicated), and the type of product. A subset of the data is summarized in the following Table.

Table: Invoice Experiment Error Customer Size	Customer Location	Product Type	Number of Errors -	-	-	15 +	-	-	18 -	+	-	6 +	+	-	2 -	-	+	19 +	-	+	23 -	+	+	16 +	+	+	21 Customer Size: Small (-), Large (+) Customer Location: Foreign (-), Domestic (+) Product Type: Commodity (-), Specialty (=)

Reference: Moen, Nolan, and Provost (R. D. Moen, T. W. Nolan and L. P. Provost. Improving Quality through Planned Experimentation. New York: McGraw-Hill, 1991)

Use the date in table above and answer the following questions in the space provided below:

  1. What is the nature of the effects of the factors studied in this experiment?
  2. What strategy would you use to reduce invoice errors, given the results of this experiment?

*****Please include the data solution******