The regression below predicts the daily number of skiers who visit a small ski resort based on three explanatory variables.
The data is a random sample of 30 days from the past two ski seasons. The variables are: SKIERS the number of skiers who visit the resort on that day
SNOW the number of inches of snow on the ground
TEMP the high temperature for the day in degrees

F

.
WEEKDAY an indicator variable, weekday

= 1

, weekend

= 0

Dependent variable is Skiers
R squared

= 25.4 \% \quad

R squared (adjusted)

= 16.8 \%

\mathrm { s } = 125.1

with

30 - 4 = 26

degrees of freedom
$The regression below predicts the daily number of skiers who visit a small ski resort based on three explanatory variables. The data is a random sample of 30 days from the past two ski seasons. The variables are: SKIERS the number of skiers who visit the resort on that day SNOW the number of inches of snow on the ground TEMP the high temperature for the day in degrees F . WEEKDAY an indicator variable, weekday = 1 , weekend = 0 Dependent variable is Skiers R squared = 25.4 \% \quad R squared (adjusted) = 16.8 \% \mathrm { s } = 125.1 with 30 - 4 = 26 degrees of freedom \begin{array} { l r r r r } \text { Variable } & \text { Coefficient } & \text { SE(Coeff) } & \text { t-ratio } & \text { p-value } \\ \text { Constant } & 559.869 & 76.78 & 7.29 & < 0.0001 \\ \text { Snow } & 1.424 & 2.70 & 0.53 & 0.6019 \\ \text { Temp } & - 1.604 & 2.77 & - 0.58 & 0.5677 \\ \text { Weekend } & 147.349 & 51.86 & 2.84 & 0.0086 \end{array} -Which of the explanatory variables appear to be associated with the number of skiers, and which do not? Explain how you reached your conclusion.$

\begin{array} { l r r r r } \text { Variable } & \text { Coefficient } & \text { SE(Coeff) } & \text { t-ratio } & \text { p-value } \\ \text { Constant } & 559.869 & 76.78 & 7.29 & < 0.0001 \\ \text { Snow } & 1.424 & 2.70 & 0.53 & 0.6019 \\ \text { Temp } & - 1.604 & 2.77 & - 0.58 & 0.5677 \\ \text { Weekend } & 147.349 & 51.86 & 2.84 & 0.0086 \end{array}

$The regression below predicts the daily number of skiers who visit a small ski resort based on three explanatory variables. The data is a random sample of 30 days from the past two ski seasons. The variables are: SKIERS the number of skiers who visit the resort on that day SNOW the number of inches of snow on the ground TEMP the high temperature for the day in degrees F . WEEKDAY an indicator variable, weekday = 1 , weekend = 0 Dependent variable is Skiers R squared = 25.4 \% \quad R squared (adjusted) = 16.8 \% \mathrm { s } = 125.1 with 30 - 4 = 26 degrees of freedom \begin{array} { l r r r r } \text { Variable } & \text { Coefficient } & \text { SE(Coeff) } & \text { t-ratio } & \text { p-value } \\ \text { Constant } & 559.869 & 76.78 & 7.29 & < 0.0001 \\ \text { Snow } & 1.424 & 2.70 & 0.53 & 0.6019 \\ \text { Temp } & - 1.604 & 2.77 & - 0.58 & 0.5677 \\ \text { Weekend } & 147.349 & 51.86 & 2.84 & 0.0086 \end{array} -Which of the explanatory variables appear to be associated with the number of skiers, and which do not? Explain how you reached your conclusion.$
-Which of the explanatory variables appear to be associated with the number of skiers, and
which do not? Explain how you reached your conclusion.

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