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

Question 535

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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
R\mathrm { R } squared =0.1%R= 0.1 \% \quad \mathrm { R } squared (adjusted) =1.8%= - 1.8 \%
s=13.61s = 13.61 with 552=5355 - 2 = 53 degrees of freedom
 Source  Sum of Squares  df  Mean Square  F-ratio  Regression 8.3464718.346470.045 Residual 9817.1853185.230 Variable  Coefficient  SE(Coeff)  t-ratio  P-value  Intercept 24.048711.402.110.0396 Long 0.0261860.12340.2120.8327\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.

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