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 is the value of the measured t-test statistic to test whether the amount of time depends linearly on the number of loan applications recorded? Question content area bottom Part 1 A. 15.2388 B. 3.2559 C. 232.2200 D. 0.8924
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 is the value of the measured t-test statistic to test whether the amount of time depends linearly on the number of loan applications recorded? Question content area bottom Part 1 A. 15.2388 B. 3.2559 C. 232.2200 D. 0.8924
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Question
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 is the value of the measured t-test statistic to test whether the amount of time depends linearly on the number of loan applications recorded?
Question content area bottom
Part 1
15.2388
3.2559
232.2200
0.8924
![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](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F6a9455eb-a2d1-4394-8c64-d1ca77bfccf9%2F2a9d4697-9529-4e8c-b5d4-604c398bf1a3%2Fwtvjbp_processed.png&w=3840&q=75)
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](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F6a9455eb-a2d1-4394-8c64-d1ca77bfccf9%2F2a9d4697-9529-4e8c-b5d4-604c398bf1a3%2Fpsm8e6r_processed.png&w=3840&q=75)
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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