Module 1: Sampling and Data

Course Contents at a Glance

Barbara Illowsky & OpenStax et al.

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The following list shows a summary of the topics covered in this e-book.

Module 1: Sampling and Data

  • Definitions of Statistics, Probability, and Key Terms
  • Sampling and Data
  • Frequency, Frequency Tables, and Levels of Measurement
  • Experimental Design and Ethics

Module 2: Descriptive Statistics

  • Stem-and-Leaf Graphs (Stemplots)
  • Measures of the Location of the Data
  • Histograms, Frequency Polygons, and Time Series Graphs
  • Box Plots
  • Measures of the Center of the Data
  • Skewness and the Mean, Median, and Mode
  • Measures of the Spread of Data
  • When to use each measure of Central Tendency

Module 3: Probability

  • The Terminology of Probability
  • Independent and Mutually Exclusive Events
  • Two Basic Rules of Probability
  • Contingency Tables
  • Tree and Venn Diagrams

Module 4: Discrete Random Variables

  • Probability Distribution Function (PDF) for a Discrete Random Variable
  • Mean or Expected Value and Standard Deviation
  • Binomial Distribution
  • Geometric Distribution
  • Poisson Distribution

Module 5: Continuous Random Variables

  • Continuous Probability Functions
  • The Uniform Distribution
  • The Exponential Distribution

Module 6: Normal Distribution

  • The Standard Normal Distribution
  • Using the Normal Distribution

Module 7: The Central Limit Theorem

  • The Central Limit Theorem for Sample Means (Averages)
  • The Central Limit Theorem for Sums
  • Using the Central Limit Theorem

Module 8: Confidence Intervals

  • A Single Population Mean using the Normal Distribution
  • A Single Population Mean using the Student Distribution
  • A Population Proportion

Module 9: Hypothesis Testing With One Sample

  • Null and Alternative Hypotheses
  • Outcomes and the Type I and Type II Errors
  • Distributions Needed for Hypothesis Testing
  • Rare Events, the Sample, Decision and Conclusion
  • Additional Informational and Full Hypothesis Test Examples

Module 10: Hypothesis Testing With Two Samples

  • Two Population Means with Unknown Standard Deviations
  • Two Population Means with Known Standard Deviations
  • Comparing Two Independent Population Proportions
  • Matched or Paired Samples

Module 11: The Chi Square Distribution

  • Facts About the Chi-Square Distribution
  • Goodness-of-Fit Test
  • Test of Independence
  • Test for Homogeneity
  • Comparison of the Chi-Square Tests
  • Test of a Single Variance

Module 12: Linear Regression and Correlation

  • Linear Equations
  • Scatter Plots
  • The Regression Equation
  • Testing the Significance of the Correlation Coefficient
  • Prediction
  • Outliers

Module 13: F-Distribution and the One-Way ANOVA

  • One-Way ANOVA
  • The F Distribution and the F-Ratio
  • Facts about the F Distribution
  • Test of Two Variances
  • Relationships in an ANOVA Table

Module 14: Multiple and Logistic Regression

  • Model Selection
  • Checking Model Assumptions Using Graphs
  • Line Fitting, Residuals, and Correlation
  • Fitting a Line by Least Linear Regression
  • Types of Outliers in Linear Regression
  • Inference for Linear Regression


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Adapted By Darlene Young Inroductory Statistics by Barbara Illowsky & OpenStax et al. is licensed under a Creative Commons Attribution 4.0 International License, except where otherwise noted.

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