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. Below is the regression output: The degrees of freedom for the F test on whether the number of load applications recorded affects the amount of time are Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.9447 0.8924 0.8886 0.3342 30 ANOVA df SS MS F Significance 25.9438 232.2200 4.3946E-15 Regression Residual Total 25.9438 3.1282 28 0.1117 29 29.072 t Stat P-value Lower 95% Upper 95% Standard Error Coefficients 0.0030 0.1492 0.6555 0.4024 0.0126 0.1236 3.2559 Intercept Applications Recorded 0.0008 15.2388 0.0000 0.0109 0.0143

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. Below is the regression output: The degrees of freedom for the F test on whether the number of load applications recorded affects the amount of time are Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.9447 0.8924 0.8886 0.3342 30 ANOVA df SS MS F Significance 25.9438 232.2200 4.3946E-15 Regression Residual Total 25.9438 3.1282 28 0.1117 29 29.072 t Stat P-value Lower 95% Upper 95% Standard Error Coefficients 0.0030 0.1492 0.6555 0.4024 0.0126 0.1236 3.2559 Intercept Applications Recorded 0.0008 15.2388 0.0000 0.0109 0.0143

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Description: 1-The manager of the purchasing department of a large saving and loan organization would like to develop a model topredict the amount of time (measured in hours) it takes to record a loan application. Data are collected from asample of 30 days, and

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Concept explainers

Types of Bias

Statistics is the study of data collection, organization, analysis, interpretation, and presentation. Statistical bias is a characteristic of a statistical technique or its findings in which the expected value deviates from the actual root quantitative parameter being estimated. According to the actual definition of bias, it refers to the tendency of a statistic to overestimate or underestimate a parameter.

Topic Video

Question

1-
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. Below is the
regression output:
The degrees of freedom for the F test on whether the number of load applications recorded affects the amount of
time are
Regression Statistics
Multiple R
R Square
Adjusted R
Square
Standard
0.9447
0.8924
0.8886
0.3342
Error
Observations
30
ANOVA
Significance
F
df
SS
MS
F
25.9438
25.9438
232.2200
4.3946E-15
Regression
Residual
28
3.1282
0.1117
Total
29
29.072
P-value
Lower 95%
Upper 95%
Standard
Error
Coefficients
t Stat
0.4024
0.1236
3.2559
0.0030
0.1492
0.6555
Intercept
Applications
Recorded
0.0126
0.0008
15.2388
0.0000
00109
0.0143
28, 1

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