i) Comments on the changes in the OR and R² from the left to the right side of the table. Task 2: The dataset relates to information collected from 30 participants

about factors predicting their GPA scores. The dataset includes 5 variables only. You are required to perform the following tasks using the information provided in the dataset "GPA.sav". a) Perform simple linear regression for GPA as a dependent variable and all other variables as predictors. (create a dummy variable for a categorical one if applicable). b) Present The "B", its 95% CI, and the p-value in a table for all variables. c) Identify which variable to be included in the multiple linear regression model. d) Perform a multiple linear regression model for plausible predictors. e) Present The "B", its 95% CI, and the p values in a table for all variables. f) Combine tables in "b" and "e" by creating one table that presents the results of simple linear regression on the left side and the results of multiple logistic regression on the right side. g) Assess model fitness. h) Provide a conclusion from the table. Comment on the changes in the "3" and R² from the left to the right side of the table. i) Test and present the assumptions for multiple linear regression model./nTask 1: The dataset relates to information collected from 117 participants about the factors associated with obesity among factory workers. The dataset includes 9 variables. You are required to perform the following tasks using the information provided in the dataset "Obesity among workers.sav". a) Calculate obesity using the cutoff value of 25 Kg/m². b) Perform simple logistic regression for obesity as a dependent variable and all other variables as predictors. c) Present The OR, its 95% CI, and the p value in a table for all variables. d) Identify which variable to be included in the multivariable logistic regression model. a. Test the interaction between Gender and work shift. If you find a significant interaction, discard both variables from the multivariable logistic regression model. e) Perform a multivariable logistic regression model for plausible predictors. f) Present The OR, its 95% CI, and the p values in a table for all variables. g) Combine tables in "b" and "f" by creating one table that presents the results of simple logistic regression on the left side and the results of multivariable logistic regression on the right side. h) Assess model fitness. i) Provide a conclusion on factors associated with obesity. END OF MODULE ASSESSMENT INTAKE: SEPTEMBER 2023 MODULE: BIOSTATISTICS (MSPH114) 2