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Finally, Gladly and Super Size run a model called m3 which has two predictor variables (temperature and mood) and the outcome variable meatballs as well as an interaction. The output is shown below. Coefficients:                   Estimate Std. Error t value Pr(>|t|)     (Intercept)      128.04578    2.43944  52.490   <2e-16 *** temperature        7.07231    0.16109  43.903   <2e-16 *** mood              14.62052    1.62318   9.007   <2e-16 *** temperature:mood   1.36555    0.09708  14.066   <2e-16 *** --- Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 19.76 on 361 degrees of freedom Multiple R-squared:  0.9416,    Adjusted R-squared:  0.9411  F-statistic:  1941 on 3 and 361 DF,  p-value: < 2.2e-16 How would you interpret this model?

You notice that the coefficient for temperature is different in the model which has mood as a predictor (m2) compared to the model which doesn't (m1). Why is this?

Gladly wants to find out whether temperature or mood (or both) predicts how many meatballs are sold, and whether there is an interaction. He therefore decides to do a regression. Why is a regression the best analysis for this out of all of the tests we have learned this semester?

In the regression model: ln ⁡ ( 𝑤 𝑎 𝑔 𝑒 ) = 𝛽 0 + 𝛽 1 𝑒 𝑑 𝑢 𝑐 + 𝛽 2 𝑒 𝑥 𝑝 𝑒 𝑟 + 𝑢 , ln ⁡ ( 𝑤 𝑎 𝑔 𝑒 ) is called the ... (Check all you mark as correct.)

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