Reference the below data You are a manufacturer of outdoor picnic furniture (benches, picnic tables, litter receptacles, etc.). You want to understand the attributes of customers that are correlated with profit per customer. The possible attributes (independent variables)

include:

Whether they order on the web site or call in their orders (WebYN=1 for web site ordering, 0 if they call in orders)

Whether they are located in a coastal or inland state (CoastalState = 1 for located

in a state bordering an ocean or gulf, 0 for inland) How much sun the state this customer is in receives (SunPct)

How many employees in the company (EmpCount)

The average income of the zip code that this customer is located within (ZipInc)

The population of the zip code that this customer is located within (ZipPop)

Create a multiple regression model with all six attributes (WebYN, CoastalState, SunPct, EmpCount, Zipinc and ZipPop) Use a Confidence Level of 95%

1. If you include WebYN, why would you not want to include Web Orders (which is

the number of orders that customer placed by the company's web site).

2. Write out the regression equation for the six independent variables shows above.

3. How much variation is explained by the regression equation (answer in %)? Is

this good or bad, and how would you use this information?

4. Is the regression equation statistically significant?

a. Provide the p-value and confidence level

5. Which independent variables are statistically significant?

a. Provide the p-value and confidence level of each independent variable and

tell me which ones are statistically significant

6. What does the coefficient of each of the statistically significant independent variable mean? (Answer separately for each attribute that is statistically significant)

7. What is the next step of your modeling process? Do the next step and report the results.

CustomerNo	billToState	Profit	Web Orders	WebYN	CoastalState	SunPct	EmpCount	ZipInc	ZipPop
U0137343	MN	1547.63	0	0	0	55	50	65314	30141
U0225537	IN	494.07	0	0	0	57.66	90	40822	38774
U0138648	PA	31.86	0	0	0	53.33	27	70504	30031
U0435328	OH	91	1	1	0	51	5	60457	49067
U0410822	MA	13.8	0	0	1	55	60	94049	20810
U0154446	CA	5677.77	0	0	1	71.85	40	56566	39315
U0157062	OK	1459.26	1	1	0	65	0	52053	47249
U0334044	SC	15480	0	0	1	62.33	60	30698	38302
U0258145	MO	72.96	0	0	0	58.25	55	28000	6283
U0407739	FL	237.3	2	1	1	66.83	0	88530	20975
U0200291	VA	360.6	0	0	1	60.66	0	36325	7269
U0401751	MN	150.95	0	0	0	55	10	36560	54296
U0408618	MD	1699.44	0	0	1	57	5	43108	39086