This course provides direction on how to prepare real-world datasets for statistical analysis, as well as perform data cleansing, reformatting, and data wrangling. Students will perform exploratory data analysis using real-world datasets and develop data visualization to facilitate statistical inquiry.
This course addresses planning and organization of experiments, including ethical considerations. One-factor experiments, randomized blocks, Latin squares and related designs, factorial designs and fractional factorial designs, response surface methodology, nested and split-plot designs.
This course introduces basic regression techniques, focusing on the theoretical foundations of regression analysis and its application to real data sets. Emphasis is placed on specifying and interpreting regression models.
Introduces conceptions of statistics, data analysis, and concepts of probability. Focus is on understanding variability and probability, sampling and random variables, descriptive and inferential statistics.
Concurrent Prerequisite
MATH 132 with a minimum grade of C