Step 3:

We decided to create a full model to predict Cases of Eggs. Even though from earlier we know that there isn't strong correlation between Cases Sold and any of the other food prices. We decide to include each of these main effects in our model to see if a linear combination of them has an effect. Additionally, we aren't sure if the First Week of the month affects pricing of Eggs, Beef, Pork, Chicken, or Cereal, so we decide to include the interaction of First.Week with these variables to see if there is an effect. Since we know that Egg prices are affected by Easter we know we want to include that interaction into our model, but we don't believe there is an Easter effect on any of the food prices so we leave those out. We aren't interested in week number so will leave that out but are also interested in relationship between Month and Egg Price so will include that interaction and main effect in the model. Fit a Multiple Linear Regression model to this full model and interpret the results.

Question 11: Write the fitted regression model calculated above.

Question 12: Provide the hypotheses, test statistic, and the p-value for the F Test. Based on this test, draw some conclusions about the overall significance of the model. Assume we are using a significance level of 5%.

Question 13: Go through the output for the T test in R and provide a list of the variables that are significant. Assume we are using a significance level of 5%.

Question 14: Based on the analysis from step 3, draw some final conclusions on the variables and interactions (if any) that seem important to the model.