It is possible that the distance that a city is from the ocean could affect its average January
low temperature. Coast gives an approximate distance of each city from the East Coast or
West Coast (whichever is nearer). Including it in the regression yields the following
regression table: Dependent variable is:JanTemp
R squared

= 87.6 \% \quad

R squared (adjusted)

= 86.9 \%

\mathrm { s } = 4.878

with

55 - 4 = 51

degrees of freedom

\begin{array} { l c c c c } \text { Source } & \text { Sum of Squares } & \text { df } & \text { Mean Square } & \text { F-ratio } \\ \text { Regression } 8611.86 & 3 & 2870.62 & 121 \\ \text { Residual } & 1213.67 & 51 & 23.7974 & \end{array}\\

\begin{array} { l c l l r } \text { Variable } & \text { Coefficient } & \text { SE(Coeff) } & \text { t-ratio } & \text { P-value } \\ \text { Intercept } & 111.878 & 6.167 & 18.1 & \leq 0.0001 \\ \text { Lat } & - 2.47722 & 0.1307 & - 19.0 & \leq 0.0001 \\ \text { Long } & 0.221997 & 0.0462 & 4.81 & \leq 0.0001 \\ \text { Coast } & - 0.674929 & 0.0901 & - 7.49 & \leq 0.0001 \end{array}

And here is a scatterplot of the residuals: $It is possible that the distance that a city is from the ocean could affect its average January low temperature. Coast gives an approximate distance of each city from the East Coast or West Coast (whichever is nearer). Including it in the regression yields the following regression table: Dependent variable is:JanTemp R squared = 87.6 \% \quad R squared (adjusted) = 86.9 \% \mathrm { s } = 4.878 with 55 - 4 = 51 degrees of freedom \begin{array} { l c c c c } \text { Source } & \text { Sum of Squares } & \text { df } & \text { Mean Square } & \text { F-ratio } \\ \text { Regression } 8611.86 & 3 & 2870.62 & 121 \\ \text { Residual } & 1213.67 & 51 & 23.7974 & \end{array}\\ \begin{array} { l c l l r } \text { Variable } & \text { Coefficient } & \text { SE(Coeff) } & \text { t-ratio } & \text { P-value } \\ \text { Intercept } & 111.878 & 6.167 & 18.1 & \leq 0.0001 \\ \text { Lat } & - 2.47722 & 0.1307 & - 19.0 & \leq 0.0001 \\ \text { Long } & 0.221997 & 0.0462 & 4.81 & \leq 0.0001 \\ \text { Coast } & - 0.674929 & 0.0901 & - 7.49 & \leq 0.0001 \end{array} And here is a scatterplot of the residuals: \text { Write a report on this regression. Interpret the coefficients and } R ^ { 2 } \text {. Are the conditions met? }$

\text { Write a report on this regression. Interpret the coefficients and } R ^ { 2 } \text {. Are the conditions met? }

Which of These Random Variables Is Most Likely to Have

Multiple Choice

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