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The Introductory Statistics course contains 71 self-grading microlearning lessons that work instantly inside your Learning Management System (LMS).

Introductory Statistics is intended for the one-semester introduction to statistics course. It focuses on the interpretation of statistical results, especially in real world settings, and assumes that students have an understanding of intermediate algebra. In addition to practice and homework sets, examples of each microlearning topic are explained step-by-step throughout. This course also includes collaborative exercises and statistics labs designed to give students the opportunity to work together and explore key concepts. While the course has been built so that each microlearning topic builds on the previous, it can be rearranged to accommodate any instructor’s particular needs.

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  • 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
  • Final Assessment
  • 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  <– TRY ONE!
  • Measures of the Spread of the Data
  • Final Assessment
  • Terminology
  • Independent and Mutually Exclusive Events
  • Two Basic Rules of Probability
  • Contingency Tables
  • Tree and Venn Diagrams
  • Final Assessment
  • Probability Distribution Function (PDF) for a Discrete Random Variable
  • Mean or Expected Value and Standard Deviation
  • Binomial Distribution
  • Geometric Distribution
  • Hypergeometric Distribution
  • Poisson Distribution
  • Final Assessment
  • Continuous Probability Functions
  • The Uniform Distribution
  • The Exponential Distribution
  • Final Assessment
  • The Standard Normal Distribution
  • Using the Normal Distribution
  • Final Assessment
  • The Central Limit Theorem for Sample Means (Averages)
  • The Central Limit Theorem for Sums
  • Using the Central Limit Theorem
  • Final Assessment
  • A Single Population Mean using the Normal Distribution
  • A Single Population Mean using the Student t Distribution
  • A Population Proportion
  • Final Assessment
  • 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
  • Final Assessment
  • Two Population Means with Unknown Standard Deviations
  • Two Population Means with Known Standard Deviations
  • Comparing Two Independent Population Proportions
  • Matched or Paired Samples
  • Final Assessment
  • Facts About the Chi-Square Distribution
  • Goodness-of-Fit Test
  • Test of Independence
  • Test for Homogeneity
  • Comparison of the Chi-Square Tests
  • Final Assessment
  • Linear Equations
  • Scatter Plots
  • The Regression Equation
  • Testing the Significance of the Correlation Coefficient
  • Prediction
  • Outliers
  • Final Assessment
  • One-Way ANOVA
  • The F Distribution and the F-Ratio
  • Facts About the F Distribution
  • Test of Two Variances
  • Final Assessment

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