Text: Stats: Data and Models
Grade: Your grade will be determined via a Big Assignment (10% of your grade), Exam 1 (20% of your grade each), Exam 2 (30% of your grade), and the Final Exam (40% of your grade). Three quarters of each exam's contribution to your grade will consist of modified homework problems. The homework assignments and exam dates can be found in the class log.
Honor Principle: On the exams, no help is to be given or received.
Students with disabilities: I encourage students with disabilities, including "invisible" disabilities like chronic diseases and learning disabilities, to discuss with me any appropriate accommodations that might be helpful.
X-Session: The X-session will be used twice for review sessions (April 13th and May 11th). The X-session may also be used to if we get behind in the syllabus, we are forced to cancel lectures, or for help sessions where I can help orient you to various computational tools (like R).
Rough Syllabus : An introduction to the basic concepts of statistics. Topics include elementary probability theory, descriptive statistics, the binomial and normal distributions, confidence intervals, basic concepts of tests of hypotheses, chi-square tests, non-parametric tests, normal theory t-tests, correlation, simple regression, analysis of variance, and (if time permits) multiple regression and multi-factor analysis of variance. We hope to cover every topic in our book, and hence our book's table of contents can be used as a detailed course syllabus. Also, the R-environment for statistical computing and graphics will be used. We will be learning how to use R, and, in particular, we will see how to perform and interpret most of the tasks in the following R-Tutorial .