# Chapter 17 19 by 0XJKdI

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• pg 1
```									Chap 17

17.6 a

Scatter Diagram

30

25
Test scores

20
15
10
5

0
0               20             40         60           80
Lengths

s xy       51.86
b b1                    =         = .2675, b 0  y  b1x = 13.80 – .2675(38.00) = 3.635
s2
x         193.9

ˆ                          ˆ
Regression line: y = 3.635 + .2675x (Excel: y = 3.636 + .2675x)

c b 1 = .2675; for each additional second of commercial, the memory test score increases on average by

.2675. b 0 = 3.64 is the y-intercept.

17.8 a

Scatter Diagram

200

150
Income

100

50

0
0           5             10         15        20     25
Education

s xy       46.02
b b1                    =          4.138, b 0  y  b1x = 78.13 – 4.138(13.17) =23.63.
s2
x         11.12

ˆ                          ˆ
Regression line: y = 23.63 + 4.138x (Excel: y = 23.63 + 4.137x)
c The slope coefficient tells us that for each additional year of education income increases on average by

\$4.138 thousand (\$4,138). The y-intercept has no meaning.

s xy         10.78
17.16 a b1                    =           .3039, b 0  y  b1x = 17.20 – (–.3039)(11.33) = 20.64.
s2
x           35.47

ˆ                          ˆ
Regression line: y = 20.64 – .3039x (Excel: y = 20.64 – .3038x)

b The slope indicates that for each additional one percentage point increase in the vacancy rate rents on
average decrease by \$.3039. The y-intercept is 20.64.

s xy       .8258
17.18 b1                  =          .0514, b 0  y  b1x = 93.89 –.0514(79.47) = 89.81.
s2
x         16.07

ˆ                          ˆ
Regression line: y = 89.81 + .0514x (Excel: y = 89.81 + .0514x)

s xy         936.82
17.98 a b1                    =           2.47 b 0  y  b1x = 395.21 – 2.47(113.35) = 115.24.
s2
x           378.77

ˆ                          ˆ
Regression line: y = 115.24 + 2.47x (Excel: y = 114.85 + 2.47x)

b b1 = 2.47; for each additional month of age, repair costs increase on average by \$2.47.

b 0 = 114.85 is the y-intercept.

s2
xy              (936.82) 2
c R2                 =                        .5659 (Excel: R 2 = .5659) 56.59% of the variation in repair costs s
s2s2
x y          (378.77)(4,094.79)

explained by the variation in ages.

17.104        H0 :   0

H1 :   0

Rejection region: t  t , n  2  t.05,428  1.645 or

s xy                         255,877
r                                                      .5540 (Excel: .5540)
s xs y           (99.11)(2,152,602,614)

n2                              430  2
tr                    (.5540)                          13.77 (Excel: t = 13.77, p–value = 0). There is enough evidence of a
1  r2                          1  (.5540) 2

positive linear relationship. The theory appears to be valid.
18.8
A                         B               C            D          E            F
1    SUMMARY OUTPUT
2
3            Regression Statistics
4    Multiple R                      0.8415
5    R Square                        0.7081
7    Standard Error                   213.7
8    Observations                       100
9
10    ANOVA
11                                    df             SS           MS          F     Significance F
12    Regression                            2       10,744,454   5,372,227    117.6          0.0000
13    Residual                             97        4,429,664      45,667
14    Total                                99       15,174,118
15
16                            Coefficients Standard Error        t Stat     P-value
17    Intercept                      576.8          514.0              1.12   0.2646
18    Space                          90.61           6.48            13.99    0.0000
19    Water                            9.66          2.41              4.00   0.0001

ˆ
a The regression equation is y = 576.8 + 90.61x 1 + 9.66x 2
b The coefficient of determination is R 2 = .7081; 70.81% of the variation in electricity
consumption is explained by the model. The model fits reasonably well.
c         H 0 : 1   2  0

H1 : At least one  i is not equal to zero

F = 117.6, p-value = 0. There is enough evidence to conclude that the model is valid.
d&e
A           B         C              D
1 Prediction Interval
2
3                       Consumption
4
5 Predicted value             8175
6
7 Prediction Interval
8 Lower limit                 7748
9 Upper limit                 8601
10
11 Interval Estimate of Expected Value
12 Lower limit                 8127
13 Upper limit                 8222

e We predict that the house will consume between 7748 and 8601 units of electricity.
f We estimate that the average house will consume between 8127 and 8222 units of
electricity.

18.10a
A                       B             C            D         E            F
1   SUMMARY OUTPUT
2
3           Regression Statistics
4   Multiple R                    0.8608
5   R Square                      0.7411
7   Standard Error                  2.66
8   Observations                     100
9
10   ANOVA
11                                 df            SS          MS         F     Significance F
12   Regression                          4            1930   482.38     67.97          0.0000
13   Residual                           95             674     7.10
14   Total                              99            2604
15
16                             Coefficients Standard Error   t Stat   P-value
17   Intercept                          3.24          5.42       0.60   0.5512
18   Mother                           0.451         0.0545       8.27   0.0000
19   Father                           0.411         0.0498       8.26   0.0000
20   Gmothers                        0.0166         0.0661       0.25   0.8028
21   Gfathers                        0.0869         0.0657       1.32   0.1890

b        H 0 : 1   2   3  0

H1 : At least one  i is not equal to zero

F = 67.97, p-value = 0. There is enough evidence to conclude that the model is valid.
c b1 = .451; for each one year increase in the mother's age the customer's age increases on
average by .451 provided the other variables are constant (which may not be possible
because of the multicollinearity).
b 2 = .411; for each one year increase in the father's age the customer's age increases on

average by .411 provided the other variables are constant.
b 3 = .0166; for each one year increase in the grandmothers' mean age the customer's age

increases on average by .0166 provided the other variables are constant.
b 4 = .0869; for each one year increase in the grandfathers' mean age the customer's age
increases on average by .0869 provided the other variables are constant.
H 0 : i  0
H1 : i  0
Mothers: t = 8.27, p-value = 0
Fathers: t = 8.26, p-value = 0
Grandmothers: t = .25, p-value .8028
Grandfathers: t = 1.32, p-value = .1890
The ages of mothers and fathers are linearly related to the ages of their children. The
other two variables are not.
d
A          B          C           D
1   Prediction Interval
2
3                          Longvity
4
5   Predicted value           71.43
6
7   Prediction Interval
8   Lower limit               65.54
9   Upper limit               77.31
10
11   Interval Estimate of Expected Value
12   Lower limit                68.85
13   Upper limit                74.00

The man is predicted to live to an age between 65.54 and 77.31
g
A           B         C               D
1 Prediction Interval
2
3                         Longvity
4
5 Predicted value            71.71
6
7 Prediction Interval
8 Lower limit                65.65
9 Upper limit                77.77
10
11 Interval Estimate of Expected Value
12 Lower limit                68.75
13 Upper limit                74.66

The mean longevity is estimated to fall between 68.75 and 74.66.
18.12
A                      B            C             D         E            F
1   SUMMARY OUTPUT
2
3           Regression Statistics
4   Multiple R                    0.8984
5   R Square                      0.8072
7   Standard Error                  7.07
8   Observations                      50
9
10   ANOVA
11                                df          SS            MS         F     Significance F
12   Regression                         2           9,832    4,916     98.37          0.0000
13   Residual                          47           2,349    49.97
14   Total                             49          12,181
15
16                          Coefficients Standard Error     t Stat   P-value
17   Intercept                    -28.43           6.89        -4.13   0.0001
18   Boxes                         0.604         0.0557        10.85   0.0000
19   Weight                        0.374         0.0847         4.42   0.0001

a y = –28.43 + .604x 1 + .374x 2
ˆ

b s  = 7.07 and R 2 = .8072; the model fits well.
c b1 = .604; for each one additional box, the amount of time to unload increases on
average by .604 minutes provided the weight is constant.
b 2 = .374; for each additional hundred pounds the amount of time to unload increases on

average by .374 minutes provided the number of boxes is constant.
H 0 : i  0

H1 :  i  0

Boxes: t = 10.85, p-value = 0
Weight: t = 4.42, p-value = .0001
Both variables are linearly related to time to unload.
d&e
A          B            C          D
1   Prediction Interval
2
3                              Time
4
5   Predicted value              50.70
6
7   Prediction Interval
8   Lower limit                  35.16
9   Upper limit                  66.24
10
11   Interval Estimate of Expected Value
12   Lower limit                44.43
13   Upper limit                56.96

d It is predicted that the truck will be unloaded in a time between 35.16 and 66.24
minutes.
e The mean time to unload the trucks is estimated to lie between 44.43 and 56.96 minutes

18.40
A                     B              C           D         E                F
1   SUMMARY OUTPUT
2
3          Regression Statistics
4   Multiple R                  0.6882
5   R Square                    0.4736
7   Standard Error               2,644
8   Observations                    40
9
10   ANOVA
11                               df           SS           MS         F          Significance F
12   Regression                         4   220,130,124 55,032,531        7.87            0.0001
13   Residual                          35   244,690,939 6,991,170
14   Total                             39   464,821,063
15
16                         Coefficients Standard Error    t Stat    P-value
17   Intercept                    1,433          2,093         0.68   0.4980
18   Size                        -14.55          20.70        -0.70   0.4866
19   Apartments                   113.0          24.01         4.70   0.0000
20   Age                         -50.10          98.81        -0.51   0.6153
21   Floors                      -223.8          171.1        -1.31   0.1994

b        H 0 : 1  2  3  4  0

H1 : At least one  i is not equal to zero

F = 7.87, p-value = .0001. There is enough evidence to conclude that the model is valid.
ˆ
The regression equation for Exercise 17.12 is y = 4040 + 44.97x. The addition of the new
variables changes the coefficients of the regression line in Exercise 17.12.
19.4a First–order model: a Demand =  0 +  1 Price+ 

Second–order model: a Demand =  0 +  1 Price +  2 Price 2 + 

First–order model:
A                         B         C            D         E            F
1   SUMMARY OUTPUT
2
3           Regression Statistics
4   Multiple R                      0.9249
5   R Square                        0.8553
7   Standard Error                   13.29
8   Observations                        20
9
10   ANOVA
11                                   df        SS          MS         F     Significance F
12   Regression                            1     18,798    18,798    106.44          0.0000
13   Residual                             18      3,179     176.6
14   Total                                19     21,977
15
16                           Coefficients Standard Error   t Stat   P-value
17   Intercept                      453.6          15.18      29.87   0.0000
18   Price                         -68.91           6.68     -10.32   0.0000

Second–order model:
A                     B             C           D          E            F
1   SUMMARY OUTPUT
2
3           Regression Statistics
4   Multiple R                    0.9862
5   R Square                      0.9726
7   Standard Error                  5.96
8   Observations                      20
9
10   ANOVA
11                               df           SS           MS         F     Significance F
12   Regression                        2        21,374     10,687    301.15          0.0000
13   Residual                         17           603      35.49
14   Total                            19        21,977
15
16                          Coefficients Standard Error   t Stat   P-value
17   Intercept                     766.9          37.40      20.50   0.0000
18   Price                        -359.1          34.19     -10.50   0.0000
19   Price-sq                      64.55           7.58       8.52   0.0000

c The second order model fits better because its standard error of estimate is 5.96,
whereas that of the first–order models is 13.29
d y .= 766.9 –359.1(2.95) + 64.55(2.95) 2 = 269.3
ˆ

19.8a
A                     B            C            D          E            F
1   SUMMARY OUTPUT
2
3           Regression Statistics
4   Multiple R                    0.9255
5   R Square                      0.8566
7   Standard Error                  5.20
8   Observations                      25
9
10   ANOVA
11                               df           SS          MS          F     Significance F
12   Regression                        3        3398.7    1132.9      41.83          0.0000
13   Residual                         21         568.8     27.08
14   Total                            24        3967.4
15
16                          Coefficients Standard Error   t Stat    P-value
17   Intercept                     260.7          162.3        1.61   0.1230
18   Temperature                    -3.32          2.09       -1.59   0.1270
19   Currency                     -164.3          667.1       -0.25   0.8078
20   Temp-Curr                       3.64          8.54        0.43   0.6741
b
A                     B            C            D         E            F
1   SUMMARY OUTPUT
2
3           Regression Statistics
4   Multiple R                    0.9312
5   R Square                      0.8671
7   Standard Error                  5.27
8   Observations                      25
9
10   ANOVA
11                               df           SS          MS         F     Significance F
12   Regression                        5        3440.3     688.1     24.80          0.0000
13   Residual                         19         527.1     27.74
14   Total                            24        3967.4
15
16                          Coefficients Standard Error   t Stat   P-value
17   Intercept                     274.8          283.8       0.97   0.3449
18   Temperature                    -1.72          6.88      -0.25   0.8053
19   Currency                     -828.6          888.5      -0.93   0.3627
20   Temp-sq                    -0.0024          0.0475      -0.05   0.9608
21   Curr-sq                      2054.0         1718.5       1.20   0.2467
22   Temp-Curr                    -0.870          10.57      -0.08   0.9353

c Both models fit equally well. The standard errors of estimate and coefficients of
determination are quite similar.
19.16a
A                         B              C            D           E            F
1   SUMMARY OUTPUT
2
3           Regression Statistics
4   Multiple R                          0.8368
5   R Square                            0.7002
7   Standard Error                       810.8
8   Observations                            40
9
10   ANOVA
11                                   df            SS           MS           F      Significance F
12   Regression                              4    53,729,535 13,432,384       20.43          0.0000
13   Residual                               35    23,007,438    657,355
14   Total                                  39    76,736,973
15
16                             Coefficients Standard Error     t Stat      P-value
17   Intercept                         3490          469.2          7.44     0.0000
18   Yest Att                         0.369          0.078          4.73     0.0000
19   I1                                1623          492.5          3.30     0.0023
20   I2                               733.5          394.4          1.86     0.0713
21   I3                              -765.5          484.7         -1.58     0.1232

b           H 0 : 1   2   3   4  0

H1 : At least on  i is not equal to 0

F = 20.43, p-value = 0. There is enough evidence to infer that the model is valid.
c           H 0 : i  0

H1 :  i  0

I 2 : t = 1.86, p-value = .0713

I 3 : t = –1.58, p-value = .1232

Weather is not a factor in attendance.
d           H0 : 2  0

H1 :  2 > 0

t = 3.30, p-value = .0023/2 = .0012. There is sufficient evidence to infer that weekend
attendance is larger than weekday attendance.
19.22a
A                     B              C             D          E                F
1   SUMMARY OUTPUT
2
3           Regression Statistics
4   Multiple R                    0.5125
5   R Square                      0.2626
7   Standard Error                  5866
8   Observations                     100
9
10   ANOVA
11                               df             SS            MS         F          Significance F
12   Regression                        5    1,151,889,624 230,377,925        6.70            0.0000
13   Residual                         94    3,234,297,164 34,407,417
14   Total                            99    4,386,186,788
15
16                          Coefficients Standard Error     t Stat     P-value
17   Intercept                    30,523          2,358         12.95    0.0000
18   Pct PT                       -108.9          77.58          -1.40   0.1635
19   Pct U                         63.95          33.86           1.89   0.0620
20   Av Shift                       2591          1,287           2.01   0.0470
21   UM Rel                        -3714          1,347          -2.76   0.0070
22   Absent                        -1260          221.5          -5.69   0.0000

b        H 0 : 4  0

H1 :  4  0

t = 2.01, p-value = .0470. There is enough evidence to infer that the availability of
shiftwork affects absenteeism.
c        H 0 : 5  0

H1 :  5  0

t = –2.76, p-value =.0070. There is enough evidence to infer that in organizations where
the union–management relationship is good absenteeism is lower.

19.40a
H          I           J               K              L           M           N
1   Results of stepwise regression
2
3   Step 1 - Entering variable: Absent
4
5   Summary measures
6       Multiple R               0.3989
7       R-Square                 0.1591
9       StErr of Est          6134.7729
10
11   ANOVA Table
12       Source                      df              SS           MS           F    p-value
13       Explained                    1 697913636.0400 697913636.0400    18.5441     0.0000
14       Unexplained                 98 3688273152.0000 37635440.3265
15
16   Regression coefficients
17                          Coefficient          Std Err       t-value   p-value
18         Constant        28516.9941         1298.6729       21.9586     0.0000
19         Absent           -790.9393          183.6711       -4.3063     0.0000
20
21   Step 2 - Entering variable: UM_Rel
22
23   Summary measures                           Change      % Change
24       Multiple R               0.4509         0.0520        %13.0
25       R-Square                 0.2033         0.0442        %27.8
26       Adj R-Square             0.1869         0.0363        %24.1
27       StErr of Est          6002.1040      -132.6689        -%2.2
28
29   ANOVA Table
30       Source                      df              SS           MS           F    p-value
31       Explained                    2 891737380.0400 445868690.0200    12.3766     0.0000
32       Unexplained                 97 3494449408.0000 36025251.6289
33
34   Regression coefficients
35                          Coefficient          Std Err       t-value   p-value
36         Constant        31636.3125         1850.1073       17.0997     0.0000
37         Absent           -967.8824          195.2204       -4.9579     0.0000
38         UM_Rel          -3150.9519         1358.4437       -2.3195     0.0225

b In the stepwise regression equation only the number of days absent and union–
management relations were statistically significant.
c The three variables that were not statistically significant and one that was borderline
were excluded by the stepwise regression process.
19.48a Depletion =  0 + 1 Temperature +  2 PH–level +  3 PH–level 2 +  4 I 4 +  5 I 5 + 
where
I1 = 1 if mainly cloudy

I1 = 0 otherwise

I 2 = 1 if sunny

I 2 = 0 otherwise

b
A                          B           C             D         E             F
1   SUMMARY OUTPUT
2
3           Regression Statistics
4   Multiple R                    0.8085
5   R Square                      0.6537
7   Standard Error                  4.14
8   Observations                     210
9
10   ANOVA
11                                    df          SS           MS         F     Significance F
12   Regression                              5          6596     1319     77.00          0.0000
13   Residual                              204          3495    17.13
14   Total                                 209         10091
15
16                              Coefficients Standard Error    t Stat   P-value
17   Intercept                          1003          55.12       18.19   0.0000
18   Temperature                       0.194          0.029        6.78   0.0000
19   PH Level                         -265.6          14.75      -18.01   0.0000
20   PH-sq                             17.76          0.983       18.07   0.0000
21   I1                                 -1.07         0.700       -1.53   0.1282
22   I2                                  1.16         0.700        1.65   0.0997

c           H 0 : 1   2   3   4   5  0

H1 : At least on  i is not equal to 0

F = 77.00, p-value = 0. There is enough evidence to infer that the model is valid.
d           H 0 : 1  0

H1 : 1 > 0

t = 6.78, p-value = 0. There is enough evidence to infer that higher temperatures deplete
chlorine more quickly.
e           H 0 : 3  0

H1 :  3 > 0
t = 18.07, p-value = 0. There is enough evidence to infer that there is a quadratic
relationship between chlorine depletion and PH level.
f       H 0 : i  0

H1 :  i  0

I1 : t = –1.53, p-value = .1282. There is not enough evidence to infer that chlorine

depletion differs between mainly cloudy days and partly sunny days.
I 2 : t = 1.65, p-value = .0997. There is not enough evidence to infer that chlorine

depletion differs between sunny days and partly sunny days.
Weather is not a factor in chlorine depletion.

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