Applied Statistical Methods
College of Arts + Science
Students will review the fundamentals of probability theory and then move to distribution theory and parameter estimation techniques to create a bases for understanding the application of statistical tests. Topics covered will include hypothesis testing and model building strategies, assumption checking such as checking for normality and outliers, visualization methods such as scatterplots and box plots, model diagnostics such as serial correlation and normality. We will use free statistical package R to do most problems in class and in homework. Students do not need to know R prior to this class. Basic R programming will be taught in class and more complex codes for simulations and other application.