OpenStax Introductory Statistics

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Introduction to Statistics

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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

Elementary Statistics

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Elementary Statistics is an introduction to data analysis course that makes use of graphical and numerical techniques to study patterns and departures from patterns. The student studies randomness with emphasis on understanding variation, collects information in the face of uncertainty, checks distributional assumptions, tests hypotheses, uses probability as a tool for anticipating what the distribution of data may look like under a set of assumptions, and uses appropriate statistical models to draw conclusions from data. The course introduces the student to applications in engineering, business, economics, medicine, education, the sciences, and other related fields. The use of technology (computers or graphing calculators) will be required in certain applications.

Material Type: Activity/Lab, Full Course, Homework/Assignment, Simulation, Syllabus

Authors: Barbara Illowsky, Susan Dean

OpenStax Introductory Statistics PowerPoint Slides

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This is a series of lesson plans based upon the OpenStax Introductory Statistics Textbooks. It contains sections on Sampling and Data, Descriptive Statistics, Probability, Discrete Random Variables, Continuous Random Variables, Distributions, The Central Limit Theorem, Confidence Intervals, Hypothesis Testing, Linear Regression and Correlation. Downloading of the slides will begin as soon as you click "View Resource."

Material Type: Lecture Notes

Authors: German Vargas, Jamil Mortada, Jose Lugo, Laura Lynch, Syvillia Averett, Treg Thompson, Victor Vega

Spreadsheet-based Statistics Labs Published

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This collection of spreadsheet-based labs was funded as part of the Digital Learning Research Network (dLRN) made possible by a grant from the Bill and Melinda Gates Foundation. The labs were adapted from the Statistics book, “Introduction to Statistics,” published by OpenStax College. The original labs used graphing calculators and were found within the book after each chapter. These interactive spreadsheet-based labs are effective for online and face-face courses. They may also be used with the book (see Resource: Lab Mapping to Book Chapters) or stand-alone.Authors: Barbara Illowsky PhD, Foothill-De Anza Community College District; Larry Green PhD, Lake Tahoe Community College; James Sullivan, Sierra College; Lena Feinman,College of San Mateo; Cindy Moss, Skyline College; Sharon Bober, Pasadena Community College; Lenore Desilets, De Anza Community College.Lab Mapping to Book ChaptersGrading RubricLabsUnivariarate Data Normal DistributionCentral Limit TheoremHyporhesis Test - Single MeanHyporhesis Test - Single ProportionGoodness of FitLinear Regression 

Material Type: Module

Authors: Barbara Illowsky, lenore desilets