# Activity 09/30/2020 ## STAT 757 -- Section 1001
Instructor: Colin Grudzien
## Instructions: We will work through the following series of activities as a group and hold small group work and discussions in Zoom Breakout Rooms. Follow the instructions in each sub-section when the instructor assigns you to a breakout room. ## Activities: ```{r} require("faraway") ``` ### Activity 1: Thirty samples of cheddar cheese were analyzed for their content of acetic acid, hydrogen sulfide and lactic acid. Each sample was tasted and scored by a panel of judges and the average taste score produced. Use the `cheddar` data to answer the following: 1. Fit a regression model with taste as the response and the three chemical contents as predictors. Report the values of the regression coefficients. 2. Compute the correlation between the fitted values and the response. Square it. Identify where this value appears in the regression output. 3. Fit the same regression model but without an intercept term. What is the value of \$R^2\$reported in the output? Compute a more reasonable measure of the goodness of fit for this example. 4. How does the significance of the explanatory variables change between the model with and without intercept? ### Activity 2: An experiment was conducted to determine the effect of four factors on the resistivity of a semiconductor wafer. The data is found in wafer where each of the four factors is coded as − or + depending on whether the low or the high setting for that factor was used. Fit the linear model `resist ∼ x1 + x2 + x3 + x4.` 1. Extract the X matrix using the model.matrix function. Examine this to determine how the low and high levels have been coded in the model. 2. Compute the correlation in the X matrix. Why are there some missing values in the matrix? 3. What difference in resistance is expected when moving from the low to the high level of x1? 4. Refit the model without x4 and examine the regression coefficients and standard errors? What stayed the the same as the original fit and what changed? 5. Explain how the change in the regression coefficients is related to the correlation matrix of X.