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Question: Follow these instructions to complete this multiple regression assignment to forecast the sales (…

by Essaynice Tutors | Sep 9, 2023 | statistics and probability

Follow
these instructions to complete this multiple regression assignment
to forecast the sales (= demand) of detergent cases.

1. Using the 30-week
time series data in the table below, create a new Excel data file
and do regression analysis on the data consisting of dependent
variable Q and independent variables P, Px, A, and I.

Q = Detergent demand
in cases; P = Price per case; Px = Competitor price; A =
Advertising expenses; I = Household income.

Week

SUMMARY OUTPUT

1,290

137

814

53,123

1,177

147

896

51,749

Regression Statistics

1,155

149

852

49,881

Multiple R

0.460424

1,299

117

854

43,589

R Square

0.211991

1,166

135

810

42,799

Adjusted R Square

0.047822

1,186

143

768

55,565

Standard Error

8.590332

1,293

113

978

37,959

Observations

1,322

111

821

47,196

1,338

109

843

50,163

ANOVA

1,160

129

849

39,080

Significance F

1,293

124

797

43,263

Regression

476.4487

95.28975

1.291297

0.300426

1,413

117

988

51,291

Residual

1771.051

73.7938

1,299

106

914

38,343

Total

2247.5

1,238

135

913

39,473

1,467

117

867

51,501

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

1,089

147

785

37,809

Intercept

-101.135

52.16986

-1.93858

0.0644

-208.809

6.53785

-208.809

6.53785

1,203

124

817

41,471

0.085372

0.049127

1.737762

0.09507

-0.01602

0.186765

-0.01602

0.186765

1,474

103

846

46,663

0.428392

0.271586

1.577373

0.127801

-0.13213

0.988918

-0.13213

0.988918

1,235

140

768

55,839

-0.32566

0.34355

-0.94794

0.352609

-1.03472

0.383388

-1.03472

0.383388

1,367

115

856

47,438

0.010646

0.030297

0.351403

0.728353

-0.05188

0.073176

-0.05188

0.073176

1,310

119

771

54,348

-0.00061

0.000504

-1.20172

0.241195

-0.00165

0.000435

-0.00165

0.000435

1,331

138

100

947

45,066

1,293

122

831

44,166

Y =
101.135+0.085372(Q+0.428392(P)-0.32566(PX)+0.010646(A)-0.00061(I)=
-100.93686

1,437

105

905

55,380

1,165

145

996

38,656

1,328

138

929

46,084

1,515

116

1,000

52,249

1,223

148

951

50,855

1,293

134

848

54,546

1,215

127

891

38,085

2. Show your
regression output.

3. Build and present
the regression model (long equation) to predict Q (dependent
variable).

4. Interpret the
coefficient estimate of each independent variable (i.e, when “A”
increases by one unit, what changes occur in “Q”?).

5. Characterize
(explain) the overall explanatory power of this multiple regression
model in light of the R squared. (You will need to do

an online search to learn about
the meaning and importance of â€œRâ€ to explain the
model).

6. Applying your
final model in forecasting: Use your regression model to forecast
weekly detergent demand (Q) in the companyâ€™s five

new markets (A through E) based
on the following expected data for the independent
variables:


Regional independent variable forecast for the following period (week 31)	P	Px	A	I	FORECASTED WEEKLY DEMAND OF DETERGENT (Q)
Market A	115	90	790	41,234	?
Market B	122	101	812	39,845	?
Market C	116	87	905	47,543	?
Market D	140	82	778	53,560	?
Market E	133	79	996	39,870	?