#### Question Details

Hi, please finish these questions, thanks! The deadline is May. 10th, 2016. 11:59 a.m.
Printouts for PART B

1.

Fitted Line Plot

y = 5.194 + 0.9942 x

S

R-Sq

25

1.63481

81.8%

81.3%

y

20

15

10

5

0

5

10

x

15

20

2.

Regression Analysis: y versus x

The regression equation is

y = - 21.4 + 16.4 x

Predictor

Constant

x

Coef

-21.382

16.4186

S = 9.75635

SE Coef

2.404

0.4761

R-Sq = 93.8%

T

-8.89

34.49

P

0.000

0.000

Analysis of Variance

Source

Regression

Residual Error

Lack of Fit

Pure Error

Total

DF

1

78

6

72

79

SS

113220

7425

5933

1492

120644

MS

113220

95

989

21

F

1189.45

P

0.000

47.72

0.000

19

3.

Regression Analysis: y versus time

The regression equation is

y = 1.85 + 0.901 time

Predictor

Constant

time

Coef

1.8504

0.90106

S = 3.05621

SE Coef

0.9849

0.04186

R-Sq = 92.4%

T

1.88

21.52

P

0.068

0.000

Analysis of Variance

Source

Regression

Residual Error

Total

DF

1

38

39

SS

4327.5

354.9

4682.4

MS

4327.5

9.3

F

463.31

P

0.000

Unusual Observations

Obs

26

time

26.0

y

31.393

Fit

25.278

SE Fit

0.535

Residual

6.115

St Resid

2.03R

R denotes an observation with a large standardized residual.

Durbin-Watson statistic = 0.419386

4.

Histogram of RESI1

12

Frequency

10

8

6

4

2

0

-5

0

5

RESI1

10

15

20

5.

Histogram of DFIT1

25

Frequency

20

15

10

5

0

-0.5

0.0

0.5

1.0

1.5

2.0

DFIT1

6.

Fitted Line Plot

y = 4.85 + 0.5122 x

100

S

R-Sq

19.9390

7.3%

6.3%

75

y

50

25

0

40

50

60

x

70

80

21

7.

Probability Plot of RESI1

Normal

99.9

Mean

-7.09122E-14

StDev

3.672

N

100

5.501

P-Value

&lt;0.005

99

Percent

95

90

80

70

60

50

40

30

20

10

5

1

0.1

-10

-5

0

RESI1

5

10

15

8.

Time Series Plot of RESI1

15

10

RESI1

5

0

-5

-10

-15

3

6

9

12

15

Index

18

21

24

27

30

22

9.

Regression Analysis: y versus x

The regression equation is

y = 11.2 + 0.759 x

Predictor

Constant

x

Coef

11.218

0.7593

S = 3.80082

SE Coef

2.052

0.1231

R-Sq = 57.6%

T

5.47

6.17

P

0.000

0.000

Analysis of Variance

Source

Regression

Residual Error

Total

DF

1

28

29

SS

549.73

404.50

954.23

MS

549.73

14.45

F

38.05

P

0.000

Unusual Observations

Obs

2

x

16.1

y

38.190

Fit

23.407

SE Fit

0.695

Residual

14.783

St Resid

3.96R

10.

Time Series Plot of HI1

0.25

0.20

HI1

0.15

0.10

0.05

0.00

4

8

12

16

20

Index

24

28

32

36

40

23

11.

Fitted Line Plot

y = - 21.38 + 16.42 x

140

S

R-Sq

120

9.75635

93.8%

93.8%

100

y

80

60

40

20

0

0

1

2

3

4

x

5

6

7

8

12.

Time Series Plot of COOK1

0.9

0.8

0.7

COOK1

0.6

0.5

0.4

0.3

0.2

0.1

0.0

4

8

12

16

20

Index

24

28

32

36

40

24

13.

Scatterplot of RESI1 vs FITS1

50

RESI1

25

0

-25

-50

-75

25

30

35

FITS1

40

45

14.

Scatterplot of RESI1 vs FITS1

15

RESI1

10

5

0

-5

15.0

17.5

20.0

22.5

FITS1

25.0

27.5

30.0

25

15.

Histogram of RESI1

40

Frequency

30

20

10

0

-4

0

4

RESI1

8

12

16.

Regression Analysis: y versus x1, x2

The regression equation is

y = 7.27 + 0.001 x1 - 0.118 x2

Predictor

Constant

x1

x2

Coef

7.273

0.0013

-0.1181

S = 2.01260

SE Coef

1.261

0.1250

0.1150

R-Sq = 2.8%

T

5.77

0.01

-1.03

P

0.000

0.992

0.311

Analysis of Variance

Source

Regression

Residual Error

Total

DF

2

37

39

SS

4.308

149.871

154.179

MS

2.154

4.051

F

0.53

P

0.592

26

17.

Fitted Line Plot

y = 12.44 + 0.6494 x

40

35

S

R-Sq

4.78696

38.5%

36.3%

S

R-Sq

3.05621

92.4%

92.2%

y

30

25

20

15

10

5

10

15

x

20

25

18.

Fitted Line Plot

y = 1.850 + 0.9011 time

40

y

30

20

10

0

0

10

20

time

30

40

27

19.

Regression Analysis: error versus x

The regression equation is

error = 0.241 - 0.0162 x

Predictor

Constant

x

Coef

0.2414

-0.01621

S = 0.786320

SE Coef

0.4387

0.04032

R-Sq = 0.4%

T

0.55

-0.40

P

0.585

0.690

Analysis of Variance

Source

Regression

Residual Error

Total

DF

1

38

39

SS

0.0999

23.4954

23.5953

MS

0.0999

0.6183

F

0.16

P

0.690

Unusual Observations

Obs

18

x

20.3

error

0.247

Fit

-0.087

SE Fit

0.415

Residual

0.334

St Resid

0.50 X

X denotes an observation whose X value gives it large leverage.

20.

Scatterplot of RESI1 vs x

1.5

1.0

RESI1

0.5

0.0

-0.5

-1.0

-1.5

5.0

7.5

10.0

12.5

x

15.0

17.5

20.0

28

21.

Regression Analysis: y versus x1, x2

The regression equation is

y = - 0.166 + 2.36 x1 + 0.48 x2

Predictor

Constant

x1

x2

Coef

-0.1657

2.357

0.484

S = 0.985172

SE Coef

0.3027

2.887

1.552

T

-0.55

0.82

0.31

R-Sq = 47.2%

P

0.587

0.418

0.756

Analysis of Variance

Source

Regression

Residual Error

Total

DF

2

47

49

SS

40.746

45.616

86.363

MS

20.373

0.971

F

20.99

P

0.000

22.

Scatterplot of RESI1 vs FITS1

20

RESI1

10

0

-10

-20

0

20

40

60

FITS1

80

100

120

29

Part B. [44 points] Each of the computer printouts for Part B reveals a problem or issue of

concern for the linear regression analysis on which the printout is based. Your task is to diagnose

the problem or issue apparent in each printout and to give brief justification of your answer. Note

that the printouts pertain to separate regression analyses, so the printouts are unrelated. For

simplicity, in the regression analyses, the response variable is denoted by y ; the predictor

variable is denoted by x for simple linear regression, and the predictor variables are denoted by

x1, x 2 , etc. for multiple regression.

1) [2 points]

2) [2 points]

3) [2 points]

4) [2 points]

5) [2 points]

6) [2 points]

7) [2 points]

8) [2 points]

9) [2 points]

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11) [2 points]

12) [2 points]

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14) [2 points]

15) [2 points]

16) [2 points]

17) [2 points]

18) [2 points]

19) [2 points]

20) [2 points]

21) [2 points]

22) [2 points]

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Approved

Oct 07, 2020

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