Week 8: Quiz
Question 1 of 10
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A cost accountant is developing a regression model to predict the total cost of producing a batch of printed circuit
boards as a linear function of batch size (the number of boards produced in one lot or batch), production plant
(Kingsland, and Yorktown), and production shift (day, and evening). The response variable in this model is ______.
production shift
total cost
variable cost
batch size
production plant
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Week 8: Quiz
Question 2 of 10
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A multiple regression analysis produced the following tables.
Summary Output
Regression Statistics
Multiple R
0.978724022
R Square
0.957900711
Adjusted R Square
0.952287472
Standard Error
67.67055418
Observations
18
ANOVA
df
SS
MS
F
Significance F
170.6503
4.80907E-11
Regression
2
1562918.941
781459.5
Residual
15
68689.55855
4579.304
Total
17
1631608.5
Coefficients
Standard Error
t Stat
P-value
Intercept
1959.709718
306.4905312
6.39403
1.21E-05
X1
-0.469657287
0.264557168
-1.77526
0.096144
X2
-2.163344882
0.278361425
-7.77171
1.23E-06
The regression equation for this analysis is ____________.
ŷ = 1959.71 – 0.47 x1 + 2.16 x2
ŷ = 1959.71 – 0.47 x1 – 2.16 x2
ŷ = 1959.71 + 0.47 x1 + 2.16 x2
ŷ = -0.47 x1 – 2.16 x2
ŷ = 1959.71 + 0.47 x1 – 2.16 x2
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Week 8: Quiz
Question 3 of 10
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A multiple regression analysis produced the following tables.
Predictor Coefficients
Standard
Error
Intercept
752.0833
336.3158 2.236241 0.042132
x1
11.87375
5.32047 2.231711 0.042493
x2
1.908183
0.662742 2.879226
Source
df
Regression
2 203693.3 101846.7 6.745406 0.010884
Residual
12 181184.1 15098.67
Total
14 384877.4
SS
MS
tt
Statistic
FF
pp–value
0.01213
pp–value
These results indicate that ____________.
x1 is the only predictor variable significant at the 5% level
none of the predictor variables are significant at the 5% level
the intercept is not significant at the 5% level
each predictor variable is significant at the 5% level
x2 is the only predictor variable significant at the 5% level
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Week 8: Quiz
Question 4 of 10
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A multiple regression analysis produced the following tables.
Summary Output
Regression Statistics
Multiple R
0.978724022
R Square
0.957900711
Adjusted R Square
0.952287472
Standard Error
67.67055418
Observations
18
ANOVA
df
SS
MS
F
Significance F
170.6503
4.80907E-11
Regression
2
1562918.941
781459.5
Residual
15
68689.55855
4579.304
Total
17
1631608.5
Coefficients
Standard Error
t Stat
P-value
Intercept
1959.709718
306.4905312
6.39403
1.21E-05
X1
-0.469657287
0.264557168
-1.77526
0.096144
X2
-2.163344882
0.278361425
-7.77171
1.23E-06
Using α = 0.05 to test the null hypothesis H0: 𝛽 1 = 0, the correct decision is ____.
fail to reject the null hypothesis
there is not enought information provided to make a decision
fail to reject the alternative hypothesis
reject the alternative hypothesis
reject the null hypothesis
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Week 8: Quiz
Question 5 of 10
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A multiple regression analysis produced the following tables.
Predictor Coefficients
Standard
Error
tt
Statistic
pp–value
Intercept
624.5369
78.49712 7.956176
6.88E-06
x1
8.569122
1.652255 5.186319 0.000301
x2
4.736515
0.699194 6.774248
Source
df
Regression
2
Residual
11 156637.5 14239.77
Total
13
SS
MS
FF
3.06E-05
pp–value
1660914 830457.1 58.31956 1.4E-06
1817552
These results indicate that ____________.
x2 is the only predictor variable significant at the 5% level
the intercept is not significant at 5% level
each predictor variable is significant at the 5% level
none of the predictor variables are significant at the 5% level
x1 is the only predictor variable significant at the 5% level
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Week 8: Quiz
Question 6 of 10
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A multiple regression analysis produced the following tables.
Predictor Coefficients
Standard
Error
tt
Statistic
pp–value
Intercept
624.5369
78.49712 7.956176
6.88E-06
x1
8.569122
1.652255 5.186319 0.000301
x2
4.736515
0.699194 6.774248
Source
df
Regression
2
Residual
11 156637.5 14239.77
Total
13
SS
MS
FF
3.06E-05
pp–value
1660914 830457.1 58.31956 1.4E-06
1817552
For x1= 30 and x2 = 100, the predicted value of y is ____________.
1,355.26
615.13
753.77
1,173.00
6153.13
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Week 8: Quiz
Question 7 of 10
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A multiple regression analysis produced the following tables.
Predictor Coefficients
Standard
Error
tt
Statistic
Intercept
-139.609
2548.989
-0.05477 0.957154
x1
24.24619
22.25267 1.089586 0.295682
x2
32.10171
17.44559 1.840105
Source
df
Regression
2
Residual
13 1153309 88716.07
Total
15 1455998
SS
MS
FF
pp–value
0.08869
pp–value
302689 151344.5 1.705942 0.219838
The regression equation for this analysis is ____________.
ŷ = -139.609 + 24.24619 x1 + 32.10171 x2
ŷ = 2548.989 + 22.25267 x1 + 17.44559 x2
ŷ = 302689 + 1153309 x1 + 1455998 x2
ŷ = 0.05477 + 1.089586 x1 + 1.840105 x2
ŷ = -0.05477 + 1.089586 x1 + 1.840105 x2
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Week 8: Quiz
Question 8 of 10
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A multiple regression analysis produced the following tables.
Predictor Coefficients
Standard
Error
tt
Statistic
Intercept
-139.609
2548.989
-0.05477 0.957154
x1
24.24619
22.25267 1.089586 0.295682
x2
32.10171
17.44559 1.840105
Source
df
Regression
2
Residual
13 1153309 88716.07
Total
15 1455998
SS
MS
FF
pp–value
0.08869
pp–value
302689 151344.5 1.705942 0.219838
These results indicate that ____________.
none of the predictor variables are significant at the 5% level
x1 is the only predictor variable significant at the 5% level
x2 is the only predictor variable significant at the 5% level
each predictor variable is significant at the 5% level
all variables are significant at 5% level
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Week 8: Quiz
Question 9 of 10
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A multiple regression analysis produced the following tables.
Predictor Coefficients
Standard
Error
tt
Statistic
Intercept
-139.609
2548.989
-0.05477 0.957154
x1
24.24619
22.25267 1.089586 0.295682
x2
32.10171
17.44559 1.840105
Source
df
Regression
2
Residual
13 1153309 88716.07
Total
15 1455998
SS
MS
FF
pp–value
0.08869
pp–value
302689 151344.5 1.705942 0.219838
For x1= 40 and x2 = 90, the predicted value of y is ____________.
1,565.75
1,355.26
3,719.39
753.77
1,173.00
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Week 8: Quiz
Question 10 of 10
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A multiple regression analysis produced the following output from Minitab.
Regression Analysis: Y versus x1 and x2
Predictor
Coef
SE Coef
T
P
Constant
-0.0626
0.2034
-0.31
0.762
x1
1.1003
0.5441
2.02
0.058
x2
-0.8960
0.5548
-1.61
0.124
S = 0.179449 R-Sq = 89.0% R-Sq(adj) = 87.8%
Analysis of Variance
Source
DF
SS
MS
Regression
2
4.7013 2.3506
Residual
Error
18
0.5796 0.0322
Total
20
5.2809
F
P
73.00 0.000
The overall proportion of variation of y accounted by x1 and x2 is _______
0.5441
0.203
0.89
0.878
0.179
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