Here is the regression with both Latitude and Longitude as predictors: Dependent variable is: JanTemp

R

squared

= 74.1 \% \quad R

squared (adjusted)

= 73.1 \%

\mathrm { s } = 7.000

with

55 - 3 = 52

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 } & 7277.18 & 2 & 3638.59 & 74.2 \\ \text { Residual } & 2548.35 & 52 & 49.0067 & \\ & & & & \\ \text { Variable } & \text { Coefficient } & \text { SE(Coeff) } & \text { t-ratio } & \text { P-value } \\ \text { Intercept } & 98.5620 & 8.473 & 11.6 & \leq 0.0001 \\ \text { Lat } & - 2.16286 & 0.1776 & - 12.2 & \leq 0.0001 \\ \text { Long } & 0.134471 & 0.0641 & 2.10 & 0.0407 \end{array}

The coefficient of Long in this regression differs from the coefficient of Long in the simple
regression of JanTemp on Long. What is the meaning of the coefficient of Long in this
regression? Are you confident (

( \text { at } \alpha = 0.05 )

that the coefficient is not zero? Why or why
not?

A Lakeside Restaurant Found the Correlation Between the Daily Temperature

Multiple Choice

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