Question: a) Fit a multiple linear regression model (call it model.1) and comment on the output fully. (6 marks) b) Perform a diagnostic check on your

a) Fit a multiple linear regression model (call it model.1) and comment on the output fully. (6 marks) b) Perform a diagnostic check on your model (model.1) and comment on the residual plots. (4 marks) c) Fit a regression model (call it model.2) consisting of all of the first-order and second- order terms and comment on the output. (6 marks) d) Perform a diagnostic check on your model (model.2) and comment on the residual plots. (4 marks) e) From the results obtained in (e) above, drop the least statistically significant variables and fit the model again (call it model.3) and comment on the output. (6 marks) f) Perform a partial F-test between model.2 and model.3 to select the model that best fits the data. (Conduct a hypothesis test) (4 marks) Odor Data Set An experiment is designed to relate three variables (temperature, ratio, and height) to a measure of odor in a chemical process. We analyze the data using a polynomial regression with multiple predictors. The data obtained (odor txt) was already coded and can be found in the table below. Odor 66 Temperature - 1 -1 Ratio -1 Height 0 -1 0 58 65 -31 -1 -1 0 0 1 0 0 39 0 1 17 7 -35 - 1 0 1 - 1 -1 0 0 0 0 0 I 1 43 -5 -1 - 1 0 1 43 0 - 1 1 -26 0 0 0 1 49 1 0 -40 1 0 1 -22 0 1 1

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