( = monthlymaintenance spending (dollars), Y = monthly machinedowntime (hours), and n= 15 copy machines areused in the following regression. (a) Formulatetheregression equation hat has beenfitted. (a) For atwo-tailedtest for zero slope, state the degrees of freedomand use Appendix D to find thecritical value at =.05. (c) What are your thoughts ontheslope? (d) nterprettheslope's 95 percent confidence limits. (e) Check that the slope is F =t 2. (f) Describe the regression's fit in your own words.
( = monthlymaintenance spending (dollars), Y = monthly machinedowntime (hours), and n= 15 copy machines areused in the following regression. (a) Formulatetheregression equation hat has beenfitted. (a) For atwo-tailedtest for zero slope, state the degrees of freedomand use Appendix D to find thecritical value at =.05. (c) What are your thoughts ontheslope? (d) nterprettheslope's 95 percent confidence limits. (e) Check that the slope is F =t 2. (f) Describe the regression's fit in your own words.
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Transcribed Image Text:Question 3
X = monthly maintenance spending (dollars), Y = monthly machinedowntime (hours), and n=
15 copy machines areused in the following regression. (a) Formulatetheregression equation
that has beenfitted. (a) For atwo-tailed test for zero slope, state thedegrees of freedomand
use Appendix D to findthecritical valueat =05. (c) What are your thoughts ontheslope? (d)
Interpret the slope's 95 percent confidence limits. (e) Check that the slope is F =t 2. (f)
Describe the regression's fit in your own words.
$$
\begin{array}{I}
\begin{array}{II}
\mathrm{R}^{2} & 0.370 \\
\text { Std. Error } & 286.793 ||
\mathrm{n} & 15
\end{array}\\
\text { ANOVA table }\\
\begin{array}{Irrrr}
\hline \text { Source } & \text { SS} & \text { df } & \text { MS } & F & p \text {-value } \\
\hline \text { Regression } & 628,298.2 & 1 & 628,298.2 & 7.64 & .0161 |\
\text { Residual } & 1,069,251.8 & 13 & 82,250.1 & & \\
\hline \text { Total } & 1,697,550.0 & 14 & & & \\
\hline
\end{array}\\
\begin{array}{IIIrIII}
\multicolumn{2}{I{\text {Regression output }} & & & \multicolumn{3}{c}{\text { confidence
interval }} \\
\hline\text{variables }& \text { coefficients } & \text { std. error } & t(\text { df }=13) & p
\text (-value } & 95 \% \text { lower } & 95 \% \text { upper} \\
\hline \text { Intercept } & 1,743.57 & 288.82 & 6.037 & .0000 & 1,119.61 & 2,367.53 ||
\text { Slope } & -1.2163 & 0.4401 & -2.764 & .0161 & -2.1671 & -0.2656
\end{array}
\end{array}
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