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This course covers descriptive statistics, the foundation of statistics, probability and random distributions, and the relationships between various characteristics of data. Upon successful completion of the course, the student will be able to: Define the meaning of descriptive statistics and statistical inference; Distinguish between a population and a sample; Explain the purpose of measures of location, variability, and skewness; Calculate probabilities; Explain the difference between how probabilities are computed for discrete and continuous random variables; Recognize and understand discrete probability distribution functions, in general; Identify confidence intervals for means and proportions; Explain how the central limit theorem applies in inference; Calculate and interpret confidence intervals for one population average and one population proportion; Differentiate between Type I and Type II errors; Conduct and interpret hypothesis tests; Compute regression equations for data; Use regression equations to make predictions; Conduct and interpret ANOVA (Analysis of Variance). (Mathematics 121; See also: Biology 104, Computer Science 106, Economics 104, Psychology 201)
Material Type: Full Course
Here you’ll find extensive support for APA, MLA, and Chicago documentation styles. This section features instructional videos that show you how to set up your papers in APA, MLA, and Chicago formats, interactive checklists, and visual support for both in-text documenting and referencing at the end of your paper. If you’re new to documentation or just need a refresher, the Citations & Documentation area can help.
Material Type: Module
This is a four page sheet of equations that are based on the chapter summaries of OpenStax University Physics Volume 1. The primary intent is to create an equation sheet for students to use during in-class exams. It is on a wiki, and therefore can be edited. Formula sheets for Volumes 2 and 3 will come out this summer (if all goes well).
Material Type: Reading