Question Details

(Answered)-Hi, please finish these questions, thanks! The deadline is May.


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

 

R-Sq(adj)

 


 

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

 


 

R-Sq(adj) = 93.8%

 


 

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

 


 

R-Sq(adj) = 92.2%

 


 

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

 

R-Sq(adj)

 


 

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

 

AD

 

5.501

 

P-Value

 

<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

 


 

R-Sq(adj) = 56.1%

 


 

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

 

R-Sq(adj)

 


 

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

 


 

R-Sq(adj) = 0.0%

 


 

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

 

R-Sq(adj)

 


 

4.78696

 

38.5%

 

36.3%

 


 

S

 

R-Sq

 

R-Sq(adj)

 


 

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

 


 

R-Sq(adj) = 0.0%

 


 

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

 


 

R-Sq(adj) = 44.9%

 


 

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]

 


 

10) [2 points]

 


 

11) [2 points]

 


 

12) [2 points]

 


 

13) [2 points]

 


 

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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