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The Table Below Shows Data For n=20n = 20 Observations A Use a First-Order Regression Model to Find a Least

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The table below shows data for n=20n = 20 observations.

yx1x2183823510152731612244928511172719383071028581436327111728245102661127611213631713192825510\begin{array}{ccc}\hline \mathrm{y} & \mathrm{x} 1 & \mathrm{x} 2 \\\hline 18 & 3 & 8 \\23 & 5 & 10 \\15 & 2 & 7 \\31 & 6 & 12 \\24 & 4 & 9 \\28 & 5 & 11 \\17 & 2 & 7 \\19 & 3 & 8 \\30 & 7 & 10 \\28 & 5 & 8 \\14 & 3 & 6 \\32 & 7 & 11 \\17 & 2 & 8 \\24 & 5 & 10 \\26 & 6 & 11 \\27 & 6 & 11 \\21 & 3 & 6 \\31 & 7 & 13 \\19 & 2 & 8 \\25 & 5 & 10 \\\hline\end{array}

a. Use a first-order regression model to find a least squares prediction equation for the model.
b. Find a 95%95 \% confidence interval for the coefficient of x1x _ { 1 } in your model. Interpret the result.
c. Find a 95%95 \% confidence interval for the coefficient of x2x _ { 2 } in your model. Interpret the result.
d. Find R2R ^ { 2 } and Ra2R _ { a } 2 and interpret these values.
e. Test the null hypothesis H0:β1=β2=0H _ { 0 } : \beta _ { 1 } = \beta _ { 2 } = 0 against the alternative hypothesis Ha:H _ { \mathrm { a } } : at least one βi0\beta _ { i } \neq 0 . Use α=.05\alpha = .05 . Interpret the result.


Definitions:

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