The manager of the purchasing department of a large saving and loan organization would like to develop a model to predict the amount of time​ (measured in​ hours) it takes to record a loan application. Data are collected from a sample of 30​ days, and the number of applications recorded and completion time in hours is recorded. Attached below is the regression output. What can be said about the​ 90% confidence interval for the mean change in the amount of time needed as a result of recording one additional loan​ application? Question content area bottom Part 1 A. The​ 90% confidence interval is narrower than​ [0.0109, 0.0143]. B. The​ 90% confidence interval is wider than​ [0.1492, 0.6555]. C. The​ 90% confidence interval is wider than​ [0.0109, 0.0143].

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
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The manager of the purchasing department of a large saving and loan organization would like to develop a model to predict the amount of time​ (measured in​ hours) it takes to record a loan application. Data are collected from a sample of 30​ days, and the number of applications recorded and completion time in hours is recorded. Attached below is the regression output. What can be said about the​ 90% confidence interval for the mean change in the amount of time needed as a result of recording one additional loan​ application?
 
 
 
 
 

Question content area bottom

Part 1
 
A.
The​ 90% confidence interval is narrower than​ [0.0109, 0.0143].
 
B.
The​ 90% confidence interval is wider than​ [0.1492, 0.6555].
 
C.
The​ 90% confidence interval is wider than​ [0.0109, 0.0143].
 
Applications residuals plot
Applications Recorded Residual Plot
0.8
0.6
0.4
Residuals 0.2
-0.2
-0.4
-0.6
-0.8
50
100
150
200
250
300
350
Loan Applications Recorded
Print
Done
Transcribed Image Text:Applications residuals plot Applications Recorded Residual Plot 0.8 0.6 0.4 Residuals 0.2 -0.2 -0.4 -0.6 -0.8 50 100 150 200 250 300 350 Loan Applications Recorded Print Done
Regression output
Regression Statistics
Multiple R
R Square
Adjusted R
Square
0.9447
0.8924
0.8886
Standard
0.3342
Error
Observations
30
ANOVA
df
SS
MS
Significance
F
Regression
1
25.9438 25.9438
232.2200 4.3946E-15
Residual
28
3.1282
0.1117
Total
29
29.072
Coefficients Standard
Error
0.1236
t Stat
P-value
Lower 95% Upper 95%
Intercept
Applications
Recorded
0.4024
3.2559
0.0030
0.1492
0.6555
0.0126
0.0008 15.2388
0.0000
0.0109
0.0143
Print
Done
Transcribed Image Text:Regression output Regression Statistics Multiple R R Square Adjusted R Square 0.9447 0.8924 0.8886 Standard 0.3342 Error Observations 30 ANOVA df SS MS Significance F Regression 1 25.9438 25.9438 232.2200 4.3946E-15 Residual 28 3.1282 0.1117 Total 29 29.072 Coefficients Standard Error 0.1236 t Stat P-value Lower 95% Upper 95% Intercept Applications Recorded 0.4024 3.2559 0.0030 0.1492 0.6555 0.0126 0.0008 15.2388 0.0000 0.0109 0.0143 Print Done
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