##### INTRODUCTORY STATISTICS COURSE
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The Introductory Statistics course contains 93 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. • 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
• Data Collection Experiment
• Sampling Experiment
• 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
• Descriptive Statistics
• Final Assessment • Terminology
• Independent and Mutually Exclusive Events
• Two Basic Rules of Probability
• Contingency Tables
• Tree and Venn Diagrams
• Probability Topics
• 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
• Discrete Distribution (Playing Card Experiment)
• Discrete Distribution (Lucky Dice Experiment)
• Final Assessment • Continuous Probability Functions
• The Uniform Distribution
• The Exponential Distribution
• Continuous Distribution
• Final Assessment • The Standard Normal Distribution
• Using the Normal Distribution
• Normal Distribution (Lap Times)
• Normal Distribution (Pinkie Length)
• Final Assessment • The Central Limit Theorem for Sample Means (Averages)
• The Central Limit Theorem for Sums
• Using the Central Limit Theorem
• Central Limit Theorem (Pocket Change)
• Central Limit Theorem (Cookie Recipes)
• Final Assessment • A Single Population Mean using the Normal Distribution
• A Single Population Mean using the Student t Distribution
• A Population Proportion
• Confidence Interval (Home Costs)
• Confidence Interval (Place of Birth)
• Confidence Interval (Women’s Heights)
• 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
• Hypothesis Testing of a Single Mean and Single Proportion
• 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
• Hypothesis Testing for Two Means and Two Proportions
• Final Assessment • 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
• Lab 1: Chi-Square Goodness-of-Fit
• Lab 2: Chi-Square Test of Independence
• Final Assessment • Linear Equations
• Scatter Plots
• The Regression Equation
• Testing the Significance of the Correlation Coefficient
• Prediction
• Outliers
• Regression (Distance from School)
• Regression (Textbook Cost)
• Regression (Fuel Efficiency)
• Final Assessment • One-Way ANOVA
• The F Distribution and the F-Ratio
• Facts About the F Distribution
• Test of Two Variances
• Lab: One-Way ANOVA
• Final Assessment
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