Fundamental concepts for data analysis and experimental design. Descriptive and inferential statistics, including classical and nonparametric methods, regression, correlation, and analysis of variance. Statistical software. Prerequisite: Minimum Junior standing or STAT 141 or STAT 143 and Instructor permission.
Prereqs: STAT 141 or STAT 143; Minimum junior standing or instructor permission
This course serves as a first course in Statistics for graduate students and well-prepared undergraduates, or as a deeper dive into the material covered in the department's 100-level introductory statistics courses. Students will also be introduced to using R for basic data analysis.
Basic knowledge of R (or other programming language) is expected. After taking this course, students should be able to: • Recognize the importance of data collection and how the methods used to collect data affect the scope of the inference that can be made. • Summarize data effectively using numerical summaries, graphical methods and appropriate statistical language. • Calculate probabilities of events using basic rules of probability and common distributions such as binomial and normal distributions. • Use distributions of random variables to calculate means and variances of linear combinations of random variables. • Apply appropriate estimation and testing methods to understand natural phenomena and make data-based decisions. • Perform basic power and sample size calculations. • Interpret statistical results correctly, effectively, and in context. • Critique data-based claims and evaluate data-based decisions. • Use R to perform basic statistical analyses with clean datasets.
**Subject to change ** Homework (15%); R Labs (15%); Quizzes (50%); Final Exam (20%)
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