Your analyst asks for your help with a different model they are working with (for which...

  

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Your analyst asks for your help with a different model they are working with (for which the previous plots don't apply). They are working with an ice cream vendor who is trying to predict ice cream sales (could be units sold, dollars - you don't know and it doesn't matter) based on the price of a cone (in dollars) and the outside temperature (in degrees). The estimated model is the following: In(sales) = -3.03price +.16temperature 6) What is the predicted sales when the price is $3 and the outside temperature is 85 degrees? Your analyst runs a linear regression and showed you the following two diagnostic plots. -4 Residuals 0246 Residuals vs Fitted 0.97 0.99 Fitted values Im(y ~X) 1.01 10 400 Histogram of reg1$residuals Frequency 200 0 -4 -20 2 4 reg1$residuals 5) Given the above plots, which suggestion is most appropriate? a) Transform X alone b) Look into another model, such as Poisson or another GLM c) Nothing wrong with these plots: everything looks fine d) Transform y, maybe try In(y) e) Increase the sample size Your analyst asks for your help with a different model they are working with (for which the previous plots don't apply). They are working with an ice cream vendor who is trying to predict ice cream sales (could be units sold, dollars - you don't know and it doesn't matter) based on the price of a cone (in dollars) and the outside temperature (in degrees). The estimated model is the following: In(sales) = -3.03price +.16temperature 6) What is the predicted sales when the price is $3 and the outside temperature is 85 degrees? Your analyst runs a linear regression and showed you the following two diagnostic plots. -4 Residuals 0246 Residuals vs Fitted 0.97 0.99 Fitted values Im(y ~X) 1.01 10 400 Histogram of reg1$residuals Frequency 200 0 -4 -20 2 4 reg1$residuals 5) Given the above plots, which suggestion is most appropriate? a) Transform X alone b) Look into another model, such as Poisson or another GLM c) Nothing wrong with these plots: everything looks fine d) Transform y, maybe try In(y) e) Increase the sample size