1) Develop a linear regression model to predict company revenue, using CPI as th

Question

1) Develop a linear regression model to predict company revenue, using CPI as the only independent variable.

2) Develop a linear regression model to predict company revenue, using Personal Consumption as the only independent variable.

3) Develop a linear regression model to predict company revenue, using Retail Sales Index as the only independent variable.

4) Which of these three models is the best? Use R-square value, Significance F values and other appropriate criteria to explain your answer.

Identify and remove the four cases corresponding to December revenue.

5) Develop a linear regression model to predict company revenue, using CPI as the only independent variable.

6) Develop a linear regression model to predict company revenue, using Personal Consumption as the only independent variable.

7) Develop a linear regression model to predict company revenue, using Retail Sales Index as the only independent variable.

8) Which of these three models is the best? Use R-square values and Significance F values to explain your answer.

9) Comparing the results of parts (d) and (h), which of these two models is better? Use R-square values

Date Revenue CPI Personal Consumption Retail Sales Index December 11/28/04 14.764 552.7 7868495 301337 0 12/30/04 23.106 552.1 7885264 357704 1 1/30/05 12.131 554.9 7977730 281463 0 2/27/05 13.628 557.9 8005878 282445 0 3/31/05 16.722 561.5 8070480 319107 0 4/29/05 13.98 563.2 8086579 315278 0 5/28/05 14.388 566.4 8196516 328499 0 6/30/05 18.111 568.2 8161271 321151 0 7/27/05 13.764 567.5 8235349 328025 0 8/27/05 14.296 567.6 8246121 326280 0 9/30/05 17.169 568.7 8313670 313444 0 10/29/05 13.915 571.9 8371605 319639 0 11/29/05 15.739 572.2 8410820 324067 0 12/31/05 26.177 570.1 8462026 386918 1 1/21/06 13.17 571.2 8469443 293027 0 2/24/06 15.139 574.5 8520687 294892 0 3/30/06 18.683 579 8568959 338969 0 4/29/06 14.829 582.9 8654352 335626 0 5/25/06 15.697 582.4 8644646 345400 0 6/28/06 20.23 582.6 8724753 351068 0 7/28/06 15.26 585.2 8833907 351887 0 8/26/06 15.709 588.2 8825450 355897 0 9/30/06 18.618 595.4 8882536 333652 0 10/31/06 15.397 596.7 8911627 336662 0 11/28/06 17.384 592 8916377 344441 0 12/30/06 27.92 589.4 8955472 406510 1 1/27/07 14.555 593.9 9034368 322222 0 2/23/07 18.684 595.2 9079246 318184 0 3/31/07 16.639 598.6 9123848 366989 0 4/28/07 20.17 603.5 9175181 357334 0 5/25/07 16.901 606.5 9238576 380085 0 6/30/07 21.47 607.8 9270505 373279 0 7/28/07 16.542 609.6 9338876 368611 0 8/29/07 16.98 610.9 9352650 382600 0 9/28/07 20.091 607.9 9348494 352686 0 10/20/07 16.583 604.6 9376027 354740 0 11/24/07 18.761 603.6 9410758 363468 0 12/29/07 28.795 604.5 9478531 424946 1 1/26/08 20.473 606.348 9540335 332797 0

Explanation / Answer

Answer:

1) Develop a linear regression model to predict company revenue, using CPI as the only independent variable.

Regression Analysis

r²

0.114

n

39

r

0.337

k

1

Std. Error

3.689

Dep. Var.

Revenue

ANOVA table

Source

SS

df

MS

F

p-value

Regression

64.5907

1  

64.5907

4.75

.0358

Residual

503.6320

37  

13.6117

Total

568.2228

38  

Regression output

confidence interval

variables

coefficients

std. error

   t (df=37)

p-value

95% lower

95% upper

Intercept

-24.4085

19.2485

-1.268

.2127

-63.4097

14.5926

CPI

0.0718

0.0330

2.178

.0358

0.0050

0.1386

2) Develop a linear regression model to predict company revenue, using Personal Consumption as the only independent variable.

Regression Analysis

r²

0.155

n

39

r

0.394

k

1

Std. Error

3.602

Dep. Var.

Revenue

ANOVA table

Source

SS

df

MS

F

p-value

Regression

88.21939269

1  

88.21939269

6.80

.0131

Residual

480.00339767

37  

12.97306480

Total

568.22279036

38  

Regression output

confidence interval

variables

coefficients

std. error

   t (df=37)

p-value

95% lower

95% upper

Intercept

-8.8951

10.1390

-0.877

.3860

-29.4387

11.6485

Personal Consumption

0.00000303

0.00000116

2.608

.0131

0.00000068

0.00000538

3) Develop a linear regression model to predict company revenue, using Retail Sales Index as the only independent variable.

Regression Analysis

r²

0.574

n

39

r

0.757

k

1

Std. Error

2.559

Dep. Var.

Revenue

ANOVA table

Source

SS

df

MS

F

p-value

Regression

325.97007804

1  

325.97007804

49.79

2.39E-08

Residual

242.25271232

37  

6.54737060

Total

568.22279036

38  

Regression output

confidence interval

variables

coefficients

std. error

   t (df=37)

p-value

95% lower

95% upper

Intercept

-13.8040

4.4557

-3.098

.0037

-22.8320

-4.7759

Retail Sales Index

0.00009186

0.00001302

7.056

2.39E-08

0.00006548

0.00011824

4) Which of these three models is the best? Use R-square value, Significance F values and other appropriate criteria to explain your answer.

R-square value of Retail Sales Index ( 0.574) is highest.

linear regression model to predict company revenue, using Retail Sales Index is the best model.

Identify and remove the four cases corresponding to December revenue.

5) Develop a linear regression model to predict company revenue, using CPI as the only independent variable.

Regression Analysis

r²

0.416

n

35

r

0.645

k

1

Std. Error

1.827

Dep. Var.

Revenue

ANOVA table

Source

SS

df

MS

F

p-value

Regression

78.3486

1  

78.3486

23.48

2.91E-05

Residual

110.1228

33  

3.3371

Total

188.4713

34  

Regression output

confidence interval

variables

coefficients

std. error

   t (df=33)

p-value

95% lower

95% upper

Intercept

-33.1342

10.2427

-3.235

.0028

-53.9731

-12.2954

CPI

0.0849

0.0175

4.845

2.91E-05

0.0493

0.1205

6) Develop a linear regression model to predict company revenue, using Personal Consumption as the only independent variable.

Regression Analysis

r²

0.404

n

35

r

0.635

k

1

Std. Error

1.846

Dep. Var.

Revenue

ANOVA table

Source

SS

df

MS

F

p-value

Regression

76.06360218

1  

76.06360218

22.33

4.13E-05

Residual

112.40771856

33  

3.40629450

Total

188.47132074

34  

Regression output

confidence interval

variables

coefficients

std. error

   t (df=33)

p-value

95% lower

95% upper

Intercept

-10.0401

5.6194

-1.787

.0832

-21.4730

1.3927

Personal Consumption

0.00000304

0.00000064

4.725

4.13E-05

0.00000173

0.00000435

7) Develop a linear regression model to predict company revenue, using Retail Sales Index as the only independent variable.

Regression Analysis

r²

0.325

n

35

r

0.570

k

1

Std. Error

1.964

Dep. Var.

Revenue

ANOVA table

Source

SS

df

MS

F

p-value

Regression

61.22234780

1  

61.22234780

15.88

.0004

Residual

127.24897294

33  

3.85602948

Total

188.47132074

34  

Regression output

confidence interval

variables

coefficients

std. error

   t (df=33)

p-value

95% lower

95% upper

Intercept

-0.6013

4.2980

-0.140

.8896

-9.3458

8.1431

Retail Sales Index

0.00005101

0.00001280

3.985

.0004

0.00002497

0.00007706

8) Which of these three models is the best? Use R-square values and Significance F values to explain your answer.

R-square value of CPI (0.416) is highest.

linear regression model to predict company revenue, using CPI is the best model.

9) Comparing the results of parts (d) and (h), which of these two models is better? Use R-square values

Using R-square value of Retail Sales Index in d is highest.

linear regression model to predict company revenue in part d is better.

Regression Analysis

r²

0.114

n

39

r

0.337

k

1

Std. Error

3.689

Dep. Var.

Revenue

ANOVA table

Source

SS

df

MS

F

p-value

Regression

64.5907

1  

64.5907

4.75

.0358

Residual

503.6320

37  

13.6117

Total

568.2228

38  

Regression output

confidence interval

variables

coefficients

std. error

   t (df=37)

p-value

95% lower

95% upper

Intercept

-24.4085

19.2485

-1.268

.2127

-63.4097

14.5926

CPI

0.0718

0.0330

2.178

.0358

0.0050

0.1386

Related Questions

Navigate

• Browse (All)
• Browse 1
• Subjects

---

---

Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.