Develop an estimated regression equation

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Reference no: EM131112772

Question 1 - A 10-year study conducted by the American Heart Association provided data on how age, blood pressure, and smoking relate to the risk of strokes. Data from a portion of this study follow. Risk is interpreted as the probability (times 100) that a person will have a stroke over the next 10-years period. For the smoker variable, 1 indicates a smoker and 0 indicates a nonsmoker.

Risk

Age

Blood Pressure

Smoker

12

57

152

0

24

67

163

0

13

58

155

0

56

86

177

1

28

59

196

0

51

76

189

1

18

56

155

1

31

78

120

0

37

80

135

1

15

78

98

0

22

71

152

0

36

70

173

1

15

67

135

1

48

77

209

1

15

60

199

0

36

82

119

1

8

66

166

0

34

80

125

1

3

62

117

0

37

59

207

1

a. Develop an estimated regression equation that can be used to predict the risk of stroke given the age and blood-pressure level.

b. Consider adding two independent variables to the model developed in part (a), one for (the interaction between age and blood-pressure level and the other for whether the person is a smoker. Develop an estimated regression equation using these four independent variables.

c. At a .05 level of significance, test to see whether the addition of the interaction term and the smoker variable contribute significantly to the estimated regression equation developed in part (a).

Question 2 - The National Football League rates prospects by position on a scale that ranges from 5 to 9. The ratings are interpreted as follows: 8-9 should start the first year; 7.0-7.9 should start; 6.0-6.9 will make the team as backup; and 5.0-5.9 can make the club and contribute. The following table shows the position, weight, time in seconds to run 40 yards, and ratings for 40 NFL prospects (USA Today, April 14, 2000).

Observation

Name

Position

Weight

Time

Rating

1

Peter Warrick

Wide receiver

194

4.53

9.0

2

Plaxico Burress

Wide receiver

231

4.52

8.8

3

Sylvester Morris

Wide receiver

216

4.59

8.3

4

Travis Taylor

Wide receiver

199

4.36

8.1

5

Laveranues Coles

Wide receiver

192

4.29

8.0

6

Dez White

Wide receiver

218

4.49

7.9

7

Jerry Porter

Wide receiver

221

4.55

7.4

8

Ron Dugans

Wide receiver

206

4.47

7.1

9

Todd Pinkston

Wide receiver

169

4.37

7.0

10

Dennis Northcutt

Wide receiver

175

4.43

7.0

11

Anthony Lucas

Wide receiver

194

4.51

6.9

12

Darrell Jackson

Wide receiver

197

4.56

6.6

13

Danny Farmer

Wide receiver

217

4.60

6.5

14

Sherrod Gideon

Wide receiver

173

4.57

6.4

15

Trevor Gaylor

Wide receiver

199

4.57

6.2

16

Cosey Coleman

Guard

322

5.38

7.4

17

Travis Claridge

Guard

303

5.18

7.0

18

Kaulana Noa

Guard

317

5.34

6.8

19

Leander Jordan

Guard

330

5.46

6.7

20

Chad Clifton

Guard

334

5.18

6.3

21

Manula Savea

Guard

308

5.32

6.1

22

Ryan Johanningmeir

Guard

310

5.28

6.0

23

Mark Tauscher

Guard

318

5.37

6.0

24

Blaine Saipaia

Guard

321

5.25

6.0

25

Richard Mercier

Guard

295

5.34

5.8

26

Damion McIntosh

Guard

328

5.31

5.3

27

Jeno James

Guard

320

5.64

5.0

28

Al Jackson

Guard

304

5.20

5.0

29

Chris Samuels

Offensive tackle

325

4.95

8.5

30

Stockar McDougle

Offensive tackle

361

5.50

8.0

31

Chris McIngosh

Offensive tackle

315

5.39

7.8

32

Adrian Klemm

Offensive tackle

307

4.98

7.6

33

Todd Wade

Offensive tackle

326

5.20

7.3

34

Marvel Smith

Offensive tackle

320

5.36

7.1

35

Michael Thompson

Offensive tackle

287

5.05

6.8

36

Bobby Williams

Offensive tackle

332

5.26

6.8

37

Darnell Alford

Offensive tackle

334

5.55

6.4

38

Terrance Beadles

Offensive tackle

312

5.15

6.3

39

Tutan Reyes

Offensive tackle

299

5.35

6.1

40

Greg Robinson-Ran

Offensive tackle

333

5.59

6.0

a. Develop dummy variables that will account for the player's position.

b. Develop an estimated regression equation to show how rating is related to position, weight, and time to run 40 yards.

c. At the .05 level of significance, test whether the estimated regression equation developed in part (b) represents a significant relationship between the independent variables and the dependent variable.

d. Is position a significant factor in the player's rating? Use a = .05. Explain.

Question 3 - A study investigated the relationship between audit delay (Delay), the length of time from a company's fiscal year-end to the date of the auditor s report, and variables that describe the client and the auditor. Some of the independent variables that were included in this study follow.

Industry - A dummy variable coded 1 if the firm was an industrial company or 0 if the firm was a bank, savings and loan, or insurance company.

Public - A dummy variable coded 1 if the company was traded on an organized exchange or over the counter; otherwise coded 0.

Quality  - A measure of overall quality of internal controls, as judged by the auditor, on a five-point scale ranging from "virtually none" (1) to "excellent" (5).

Finished - A measure ranging from 1 to 4, as judged by the auditor, where 1 indicates "all work performed subsequent to year-end" and 4 indicates "most work performed prior to year-end."

A sample of 40 companies provided the following data.

Delay

Industry

Public

Quality

Finished

62

0

0

3

1

45

0

1

3

3

54

0

0

2

2

71

0

1

1

2

91

0

0

1

1

62

0

0

4

4

61

0

0

3

2

69

0

1

5

2

80

0

0

1

1

52

0

0

5

3

47

0

0

3

2

65

0

1

2

3

60

0

0

1

3

81

1

0

1

2

73

1

0

2

2

89

1

0

2

1

71

1

0

5

4

76

1

0

2

2

68

1

0

1

2

68

1

0

5

2

86

1

0

2

2

76

1

1

3

1

67

1

0

2

3

57

1

0

4

2

55

1

1

3

2

54

1

0

5

2

69

1

0

3

3

82

1

0

5

1

94

1

0

1

1

74

1

1

5

2

75

1

1

4

3

69

1

0

2

2

71

1

0

4

4

79

1

0

5

2

80

1

0

1

4

91

1

0

4

1

92

1

0

1

4

46

1

1

4

3

72

1

0

5

2

85

1

0

5

1

a. Develop the estimated regression equation using all of the independent variables.

b. Did the estimated regression equation developed in part (a) provide a good fit? Explain.

c. Develop a scatter diagram showing Delay as a function of Finished. What does this scatter diagram indicate about the relationship between Delay and Finished?

d. On the basis of your observations about the relationship between Delay and Finished, develop an alternative estimated regression equation to the one developed in (a) to ex-plain as much of the variability in Delay as possible.

Detailed Question: I need these questions completed step by step using excel and the details on how to find the solutions.

Reference no: EM131112772

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