Learning Outcomes

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The textbook content and assessments for Introduction to Statistics are aligned to the following learning outcomes. A full list of course learning outcomes can be viewed here: Introduction to Statistics Corequisite Learning Outcomes.

Module 1: Sampling and Data

  • Review topics needed for success in Algebra Essentials.
  • Definitions of Statistics, Probability, and Key Terms.
  • Data, Sampling, and Variation in Data and Sampling.
  • Frequency, Frequency Tables, and Levels of Measurement.
  • Experimental Design and Ethics.

Module 2: Descriptive Statistics

  • Review topics needed for success in Descriptive Statistics.
  • Stem-and-Leaf Graphs (Stemplots), Line Graphs, and Bar Graphs.
  • Histograms, Frequency Polygons, and Time Series Graphs.
  • Measures of the Location of the Data.
  • Box Plots.
  • Measures of the Center of the Data.
  • Skewness and the Mean, Median, and Mode.
  • Measures of the Spread of the Data.

Module 3: Probability Topics

  • Review topics needed for success in Probability Topics.
  • Terminology.
  • Independent and Mutually Exclusive Events.
  • Two Basic Rules of Probability.
  • Contingency Tables.
  • Tree and Venn Diagrams.

Module 4: Discrete Random Variables

  • Review topics needed for success in Discrete Random Variables.
  • Probability Distribution Function (PDF) for a Discrete Random Variable.
  • Mean or Expected Value and Standard Deviation.
  • Binomial Distribution.
  • Geometric Distribution.
  • Hypergeometric Distribution.
  • Poisson Distribution.

Module 5: Continuous Random Variables

  • Review topics needed for success in Continuous Random Variables.
  • Continuous Probability Functions.
  • The Uniform Distribution.
  • The Exponential Distribution.

Module 6: The Normal Distribution

  • Review topics needed for success in The Normal Distribution.
  • The Standard Normal Distribution.
  • Using the Normal Distribution.

Module 7: The Central Limit Theorem

  • Review topics needed for success in 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

  • Review topics needed for success in Confidence Intervals.
  • A Single Population Mean using the Normal Distribution.
  • A Single Population Mean using the Student t Distribution.
  • A Population Proportion.

Module 9: Hypothesis Testing With One Sample

  • Review topics needed for success in Hypothesis Testing with One Sample.
  • Null and Alternative Hypotheses.
  • Outcomes and the Type I and Type II Errors.
  • Distribution Needed for Hypothesis Testing.
  • Rare Events, the Sample, Decision and Conclusion.
  • Additional Information and Full Hypothesis Test Examples.

Module 10: Hypothesis Testing With Two Samples

  • Review topics needed for success in 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

  • Review topics needed for success in 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

  • Review topics needed for success in 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

  • Review topics needed for success in F Distribution and One-Way ANOVA.
  • One-Way ANOVA.
  • The F Distribution and the F-Ratio.
  • Facts About the F Distribution.
  • Test of Two Variances.