What are the hypotheses for research design

Question

Discussion:

Q1: A sales manager is curious whether day of the week makes any difference in number of sales made.  She decides to sample the records to determine if sales are distributed evenly throughout the six-day workweek. The sample results are:

Day of Week	Sales Made
Monday	6
Tuesday	9
Wednesday	11
Thursday	10
Friday	10
Saturday	18
Total	64

a. What are the hypotheses for your research design?

b. What is the critical value for the c² statistic at a .05 level of significance?  State your decision rule.

c.  Compute the c² for this goodness of fit test.

d.  On the basis of your results, are there significant differences between days?  Explain the rationale for your results with a short summary statement of your research and at least one recommendation for the sales manager.

Q2: The sales manager is pleased with the results of your initial research study and give you another challenge. She asks you to study the results of three sales approaches to see if one of the approaches would result in increased sales. The approaches are:

1. a sales-information DVD mailed to prospective customers

2. a personal sales call

3. a telephone call to prospective customers

A random sample of 340 recent customers were selected. The results, in terms of purchases of the full program tapes are as follows:

Observed	Sales Approach
Action	DVD	Personal sales call	Telephone call	Total
Purchased	17	35	20	72
Return	81	84	103	268
Total	98	119	123	340

Expected	Sales Approach
Action	DVD	Personal sales call	Telephone call	Total
Purchased
Return
Total

e.  What are the hypotheses for your research design?

f.  Compute the expected frequencies for each cell in the second table.

g.  What is the table value for the c² statistic at a .01 level of significance?  State your decision rule.

h. Compute the c² for this contingency table.

i. What are the hypotheses for your research design?

j. On the basis of your results, what is your decision?  Explain the rationale for your results with a short summary statement of your research and at least one recommendation for the sales manager.

Q3 After running a regression to determine a predictive model for sales (monthly revenue in thousands) based on average number of hours spent per day making sales calls by your sales staff, you come up with the following regression output:

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.92
R Square	0.84
Adjusted R Square	0.81
Standard Error	89.59
Observations	7.00
ANOVA
	df	SS	MS	F
Regression		206907.64
Residual
Total		247041.43
	Coefficients	Standard Error	t Stat
Intercept	136.33	71.89	1.90
Sales persons	70.43	13.87	5.08

Complete the ANOVA table.  What is the computed F statistic?  How many degrees of freedom are there in this regression?  Is the model significant?

How much variation in the data is explained?  How much is left unexplained?

If salespersons spend 6 hours per day making sales calls, how much revenue can the Sales Manager expect to earn this month?