Katelynn Kochalski

Classes

• MATH 221: R/Calculus I

  Topics studied are limits and continuity; derivatives and antiderivatives of the algebraic, exponential, logarithmic, trigonometric, and inverse functions; the definite integral; and the fundamental theorem of the calculus.

• MATH 262: R/Applied Statistics

  An introduction to statistics with emphasis on applications. Topics include the description of data with numerical summaries and graphs, the production of data through sampling and experimental design, techniques of making inferences from data such as confidence intervals and hypothesis tests for both categorical and quantitative data. The course includes an introduction to computer analysis of data with a statistical computing package.

• MATH 341: Probability & Applied Stats

  Topics include probability definitions and theorems; discrete and continuous random variables including the binomial, geometric, Poisson, and normal random variables; and the applications of statistical topics such as sampling distributions, estimation, confidence intervals, and tests of hypothesis. Both the theory and applications of probability will be included with applications of statistics.