Let's assume that there is a manufacturing company which produces a type of machine. Every machine needs some time to be manufactured. We have the following variables in the dataset, • Y = RunTime, the time in minutes for a production run to be completed • X = RunSize, the number of items being produced in each run • We have 20 randomly selected orders as supervised by one of three managers We are interested in predicting the run time for an order. The following is an R output after running a linear model: Call: 1m (formula = RunTime RunSize, data = production Residuals: Min 1Q Median 3Q Max -28.597 -11.079 3.329 8.302 29.627 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 149.74770 8.32815 17.98 6.00e-13 *** RunSize 0.25924 0.03714 6.98 1.61e-06 *** Residual standard error: 16.25 on 18 degrees of freedom Multiple R-squared: 0.7302, Adjusted R-squared: 0.7152 F-statistic: 48.72 on 1 and 18 DF, p-value: 1.615e-06 (a) (b) Calculate the 99% confidence interval for the regression coefficient corresponding to the RunSize variable (i.e., ß₁) based on the information that you have. Interpret the 99% confidence interval from (a)
Let's assume that there is a manufacturing company which produces a type of machine. Every machine needs some time to be manufactured. We have the following variables in the dataset, • Y = RunTime, the time in minutes for a production run to be completed • X = RunSize, the number of items being produced in each run • We have 20 randomly selected orders as supervised by one of three managers We are interested in predicting the run time for an order. The following is an R output after running a linear model: Call: 1m (formula = RunTime RunSize, data = production Residuals: Min 1Q Median 3Q Max -28.597 -11.079 3.329 8.302 29.627 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 149.74770 8.32815 17.98 6.00e-13 *** RunSize 0.25924 0.03714 6.98 1.61e-06 *** Residual standard error: 16.25 on 18 degrees of freedom Multiple R-squared: 0.7302, Adjusted R-squared: 0.7152 F-statistic: 48.72 on 1 and 18 DF, p-value: 1.615e-06 (a) (b) Calculate the 99% confidence interval for the regression coefficient corresponding to the RunSize variable (i.e., ß₁) based on the information that you have. Interpret the 99% confidence interval from (a)
MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
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Chapter1: Starting With Matlab
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Related questions
Question

Transcribed Image Text:Let's assume that there is a manufacturing company which produces a type of machine. Every machine
needs some time to be manufactured. We have the following variables in the dataset,
• Y = RunTime, the time in minutes for a production run to be completed
• X = RunSize, the number of items being produced in each run
. We have 20 randomly selected orders as supervised by one of three managers
We are interested in predicting the run time for an order. The following is an R output after running a
linear model:
Call: 1m (formula = RunTime ~ RunSize, data = production
Residuals:
Min
1Q Median
3Q
Max
-28.597 -11.079 3.329 8.302 29.627
Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 149.74770 8.32815 17.98 6.00e-13 ***
RunSize
0.25924
0.03714
6.98 1.61e-06 ***
Residual standard error: 16.25 on 18 degrees of freedom
Multiple R-squared: 0.7302, Adjusted R-squared: 0.7152
F-statistic: 48.72 on 1 and 18 DF, p-value: 1.615e-06
(a)
(b)
Calculate the 99% confidence interval for the regression coefficient corresponding to the
RunSize variable (i.e., B₁) based on the information that you have.
Interpret the 99% confidence interval from (a)

Transcribed Image Text:(c) |
Let's assume we have a new order size (i.e., RunSize) of 150. Calculate the predicted value
of the RunTime.
(d) L
(e) |
Calculate the 99% prediction interval for the predicted RunTime from (c)
Interpret the prediction interval from (d).
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