# The manager of the purchasing department of a large saving and loan organization would like todevelop a model to predict the amount of time (measured in hours) it takes to record a loanapplication. Data are collected from a sample of 30 days, and the number of applications recorded andcompletion time in hours is recorded. Below is the regression output:Regression StatisticsMultiple RR SquareAdjusted RSquareStandard0.94470.89240.88860.3342ErrorObservations30ANOVAMSSignificancedfSS4.3946E-15Regression25.9438 25.9438232.2200Residual283.12820.1117Total2929.072CoefficientsStandardP-valueLower 95%t StatUpper 95%Error0.1492InterceptApplicationsRecorded0.40240.12363.25590.00300.65550.01430.01260.000815.23880.00000.0109

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Referring to the above scenario, interpret the coefficient attached to variable applications recorded:

Referring to the above scenario, interpret R Square:

Referring to the above scenario, interpret the F-statistic in the ANOVA analysis:

Referring to above scenario, predict the amount of time it would take on average to process 150 invoices:

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Step 1

From the regression output, the coefficient of application recorded is 0.0126.

Interpretation:

The slope 0.0126 indicates that for each additional application recorded the mean amount of time will increase by 0.0126 hours.

Step 2

R-square:

The value of R-square is 0.8924.

There is 89.24% of variability in the amount of time can be explained by the number of applications recorded.

Step 3

From the regression output, the F test statistic is 232.2200 and the p-value is 0.0000.

Consider the significance level is 0.05. Here, the p-value is too small compared to s...

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