Now, Consider Longitude $\mathrm { R }$ Squared $= 0.1 \% \quad \mathrm { R }$

Now, consider longitude. Should the longitude of a city have an influence on average
January low temperature? Here is the regression: Dependent variable is: JanTemp
$\mathrm { R }$ squared $= 0.1 \% \quad \mathrm { R }$ squared (adjusted) $= - 1.8 \%$
$s = 13.61$ with $55 - 2 = 53$ degrees of freedom
$\begin{array} { l l l l l } \text { Source } & \text { Sum of Squares } & \text { df } & \text { Mean Square } & \text { F-ratio } \\ \text { Regression } & 8.34647 & 1 & 8.34647 & 0.045 \\ \text { Residual } & 9817.18 & 53 & 185.230 & \\ & & & & \\ \text { Variable } & \text { Coefficient } & \text { SE(Coeff) } & \text { t-ratio } & \text { P-value } \\ \text { Intercept } & 24.0487 & 11.40 & 2.11 & 0.0396 \\ \text { Long } & 0.026186 & 0.1234 & 0.212 & 0.8327 \end{array}$
Test the null hypothesis that the true coefficient of Long is zero in this regression. State the
null and alternative hypotheses and indicate your procedure and conclusion.