Homework Assignment for Statistics Class: Regression Analysis and ANOVA Model - Prof. A. J, Assignments of Statistics

Download Homework Assignment for Statistics Class: Regression Analysis and ANOVA Model - Prof. A. J and more Assignments Statistics in PDF only on Docsity! Homework 4 Assigned: 19 September 2003 Due: 26 September 2003 C:\Documents and Settings\John Bailer\My Documents\baileraj\Classes\Fall 2003\sta402\hw\Homework-4.doc 1. Repeat the regression analysis of the Manatee – Boat registration data a. Fit the model MANATEE = 00 +1 NBOATSi + i b. Fit the model MANATEE = 01 +1 [NBOATSNBOATSi-min(NBOATS)] + ] + i c. Fit the model MANATEE = 02 +1[NBOATSNBOATSi-average(NBOATS)] + ] + i Prepare a table summarizing the estimated regression coefficients, standard errors, P-values for these three models. Hint: use ODS output to manipulate the coefficient information. 2. Refer to the 4 group (packaging method)- log-bacterial growth study. In this exercise, you will compare the one-way anova model to regression with coding. a. Fit the anova model and obtain the estimated effects (estimates of , 1, 2, 3, 4) using SOLUTION option to the GLM model statement. proc glm data=meat order=data; title2 fitting the one-way anova model via GLM; class condition; model logcount = condition/solution; means condition; run; b. Define 4 indicator variables, one for each of the conditions. For example, let ICO2 = 1 if condition=”CO2” and ICO2=0 otherwise. Define the other 3 indicator variables similarly. c. Can you fit a regression model with all indicator variables included? If so, how? d. Fit 4 different regression models with 3 of the 4 indicator variables. Which fit matches the solution from part a? What does this tell you about the coding in the model fit from part 2a? model logcount = IPlastic IVacuum IMixed; model logcount = IPlastic IVacuum ICO2; model logcount = IPlastic IMixed ICO2; model logcount = IVacuum IMixed ICO2; 3. Fit a regression model with a linear and a quadratic term to the nitrofen data using the square root of “total” as the response variable. As an aside, the square root transformation is a normalizing transformation for Poisson data and is often performed prior to fitting models that