Can someone help me with these questions below, please?

Enterprise Industries produces Fresh, a brand of liquid laundry detergent. In order to manage its inventory more effectively and make revenue projections, the company would like to better predict demand for Fresh. To develop a prediction model, the company has gathered data concerning demand for Fresh over the last 30 sales periods (each sales period is defined to be a four-week period). The demand data are presented in table below concerningy(demand for Fresh liquid laundry detergent),x₁(the price of Fresh),x₂(the average industry price of competitors' similar detergents), andx₃(Enterprise Industries' advertising expenditure for Fresh). To ultimately increase the demand for Fresh, Enterprise Industries' marketing department is comparing the effectiveness of three different advertising campaigns. These campaigns are denoted as campaignsA,B, andC. CampaignAconsists entirely of television commercials, campaignBconsists of a balanced mixture of television and radio commercials, and campaignCconsists of a balanced mixture of television, radio, newspaper, and magazine ads. To conduct the study, Enterprise Industries has randomly selected one advertising campaign to be used in each of the 30 sales periods in table below. Although logic would indicate that each of campaignsA,B, andCshould be used in 10 of the 30 sales periods, Enterprise Industries has made previous commitments to the advertising media involved in the study. As a result, campaignsA,B, andCwere randomly assigned to, respectively, 9, 11, and 10 sales periods. Furthermore, advertising was done in only the first three weeks of each sales period, so that the carryover effect of the campaign used in a sales period to the next sales period would be minimized. Table lists the campaigns used in the sales periods.

To compare the effectiveness of advertising campaignsA,B, andC, we define two dummy variables. Specifically, we define the dummy variableD_Bto equal 1 if campaignBis used in a sales period and 0 otherwise. Furthermore, we define the dummy variableD_Cto equal 1 if campaign C is used in a sales period and 0 otherwise. Table presents the JMP output of a regression analysis of the Fresh demand data by using the model

Historical Data Concerning Demand for Fresh Detergent

Sales Period Price for Fresh,x₁ Average Industry Price,x₂ Advertising Expenditure for Fresh,x₃Demand

for Fresh,y

1 3.92 3.84 5.54 7.33

2 3.76 4.03 6.75 8.57

3 3.76 4.36 7.26 9.20

4 3.76 3.70 5.57 7.59

5 3.67 3.87 7.04 9.34

6 3.62 3.85 6.51 8.27

7 3.64 3.71 6.72 8.77

8 3.88 3.85 5.29 7.88

9 3.88 3.64 5.29 7.13

10 3.88 4.01 6.03 8.01

11 3.93 4.17 6.50 7.81

12 3.92 4.05 6.25 8.14

13 3.79 4.15 7.01 9.14

14 3.77 4.20 6.98 8.84

15 3.78 4.13 6.80 8.90

16 3.88 4.18 6.86 8.89

17 3.77 4.29 7.15 9.29

18 3.81 4.36 7.07 9.02

19 3.78 4.14 6.89 8.77

20 3.80 3.75 6.59 7.97

21 3.87 3.75 6.29 7.63

22 3.79 3.60 6.04 7.23

23 3.74 3.92 6.56 8.03

24 3.50 3.62 7.03 8.58

25 3.67 4.16 6.88 8.77

26 3.68 4.22 6.81 9.22

27 3.70 3.65 6.57 8.24

28 3.70 3.75 5.70 7.61

29 3.87 3.81 5.87 7.97

30 3.70 4.23 6.85 9.29

Advertising Campaigns Used by Enterprise Industries

Sales Period Advertising Campaign

1 B

2 B

3 B

4 A

5 C

6 A

7 C

8 C

9 B

10 C

11 A

12 C

13 C

14 A

15 B

16 B

17 B

18 A

19 B

20 B

21 C

22 A

23 A

24 A

25 A

26 B

27 C

28 B

29 C

30 C

Summary of Fit

RSquare 0.933166

RSquare Adj 0.919242

Root Mean Square Error 0.19205

Mean of Response 8.382667

Observations (or Sum Wgts) 30

Analysis of Variance

Source DF Sum of Squares Mean Square F Ratio

Model 5 12.359104 2.47182 67.0198

Error 24 0.885166 0.03688 Prob > F

C. Total 29 13.244270 <.0001*

Term Estimate Std Error t Ratio Prob>|t| Lower 95% Upper 95%

Intercept 8.991323 1.928019 4.66 <0.0001* 5.0120884 12.970558

Price(X1) 2.711960 0.518950 5.23 <0.0001* 3.78302 1.640901

IndPrice(X2) 1.5752808 0.243237 6.48 <0.0001* 1.0732643 2.0772973

AdvExp(X3) 0.4779225 0.107045 4.46 0.0001620* 0.256993 0.698852

DB 0.249958 0.088835 2.81 0.0096170* 0.0666122 0.4333032

DC 0.5223511 0.092342 5.66 <0.0001* 0.3317660 0.7129362

Predicted Demand Lower 95%Mean Demand Upper 95% Mean Demand Lower 95%

Indiv Demand Upper 95% Indiv Demand

31 8.681216263 8.548898824 8.813533702 8.263349051 9.099083475

y = ₀+ ₁x1+ ₂ x2+ ₃x3+ ₄D_B+ ₅D_C+

Click here for the Excel Data File

(a)In this model the parameter₄represents the effect on mean demand of advertising campaignBcompared to advertising campaignA, and the parameter₅represents the effect on mean demand of advertising campaignCcompared to advertising campaignA. Use the regression output to find and report a point estimate of each of the above effects and to test the significance of each of the above effects. Also, find and report a 95 percent confidence interval for each of the above effects. Interpret your results.(Round your answers to 4 decimal places.)

(b)The prediction results at the bottom of the output correspond to a future period when Fresh's price will bex1= 3.74, the average price of similar detergents will bex2= 3.92, Fresh's advertising expenditure will bex3= 6.56, and advertising campaignCwill be used. Show howy^= 8.68122 is calculated. Then find, report, and interpret a 95 percent confidence interval for mean demand and a 95 percent prediction interval for an individual demand whenx1= 3.74,x2= 3.92,x3= 6.56, and campaignCis used.(Round your answers to 5 decimal places.)

(c)Consider the alternative model

y = ₀+ ₁x1+ ₂x2+ ₃x3+ ₄D_A+ ₅D_C+

HereD_Aequals 1 if advertising campaignAis used and equals 0 otherwise.Describe the effect represented by the regression parameter₅.