This course will introduce you to a number of statistical tools and techniques that are routinely used by modern statisticians for a wide variety of applications.
First, we will review basic knowledge and skills that you learned in Introduction to Statistics. Units 2-5 will introduce you to new ways to design experiments and to test hypotheses, including multiple and nonlinear regression and nonparametric statistics. You will learn to apply these methods to building models to analyze complex, multivariate problems. You will also learn to write scripts to carry out these analyses in R, a powerful statistical programming language. The last unit is designed to give you a grand tour of several advanced topics in applied statistics.
Upon successful completion of this course, the student will be able to:
- apply statistical hypothesis testing for one population;
- conduct statistical hypothesis testing and estimation for two populations;
- apply multiple regression analysis to analyze a multivariate problem;
- analyze the outputs for a multiple regression model and interpret the regression results;
- conduct test hypotheses about the significance of a multiple regression model and test the significance of the independent variables in the model;
- select appropriate multiple regression models using automatic model selection, forward selection, backward elimination, and stepwise selection;
- recognize and address issues when using multiple regression analysis;
- identify situations when nonparametric tests are appropriate;
- conduct nonparametric tests; and
- explain the principles underlying General Linear Model, Multilevel Modeling, Data Mining, Machine Learning, Bayesian Belief Networks, Neural Network, and Support Vector Machine.
Course Requirements:
Have completed the following course: Introduction to Statistics.
