This course will provide students with an understanding of statistical designs and inference with a focus on computational statistics. The course will expose students to useful classical statistics including various experimental designs and sampling, the likelihood, principles of estimation and hypothesis testing. Students will also learn about more modern variants including areas of computational statistics such as Bayesian statistics, resampling, and Gibbs sampling, simulation, and methods for missing data. Prerequisites: STAT 2000 or equivalent and MATH 2110 or equivalent and successful completion of at least one university level computer programming course. Recommended Requisites: MATH 2120 or equivalent, MATH 3020 or equivalent, STAT 3060 or equivalent, STAT 4040 or equivalent, or STAT 3050 or equivalent
This course will provide students with an understanding of statistical designs and inference with a focus on computational statistics. The course will expose students to useful classical statistics including various experimental designs and sampling, the likelihood, principles of estimation and hypothesis testing. Students will also learn about more modern variants including areas of computational statistics such as Bayesian statistics, resampling, and Gibbs sampling, simulation, and methods for missing data. Prerequisites: STAT 2000 or equivalent and MATH 2110 or equivalent and successful completion of at least one university level computer programming course. Recommended Requisites: MATH 2120 or equivalent, MATH 3020 or equivalent, STAT 3060 or equivalent, STAT 4040 or equivalent, or STAT 3050 or equivalent
