The Statistics Companion Workbook is part of an interactive learning strategy designed to assist readers with understanding and retaining statistics. Rather than reading a textbook, readers interact with the course material and complete a workbook filled with detailed information and examples.
Readers should use this workbook in conjunction with the Udemy online statistics course referencing the Statistics Companion Workbook, or with their college statistics course which may be offered live and in-person, live via remote technology, or fully online (local college participation varies). Upon completion of their chosen statistics course in conjunction with the Statistics Companion Workbook, readers will possess a detailed, complete, statistics companion filled with valuable information and techniques compiled by the reader through their course interactions. In effect, readers will build their own complete, introductory statistics textbook.
Topics covered in this book include:
- How to use the Statistics Companion Workbook
- Statistics, Populations vs. Samples, Types of Data, Levels of Measurement, Data Collection and Sampling Techniques
- Characteristics of Data, Frequency Distributions, Relative Frequency, Visualization Errors, Histograms, Frequency Polygons
- Measures of Center, Mean, Median, Mode, Mean of a Frequency Distribution, Measures of Variation, Standard Deviation, Variance, Standard Deviation of a Frequency Distribution
- Empirical Rule, Chebyshev’s Theorem, z-score, Percentile, Quartiles, Boxplots
- Classical Probability, Relative Frequency Probability, Law of Large Numbers, Addition Rule, Mutual Exclusivity, Multiplication Rule, Independence
- Complements and Applications, Venn Diagrams, Prior, Posterior, and Conditional Probability, Bayes’ Theorem
- Counting, Multiplication Rule, Factorial Rule, Permutations Rule, Identical Items Rule, Combinations Rule
- Discrete Random Variables, Discrete Probability Distributions, Histograms, Means, Standard Deviations, Expectation
- Binomial Probability Distributions, Significance vs. Unusualness, Poisson Probability Distributions, Approximation of Discrete Binomial Distribution with Discrete Poisson Distribution
- Continuous Random Variables, Continuous Probability Distributions, Density Curve, Normal Distribution, Standard Normal Distribution, Non-Standard Normal Distribution Applications
- Proportion, Sampling Distributions, Foundation for the Central Limit Theorem, Central Limit Theorem and Applications
- Assessing Normality, Normal Quantile Plot, Ryan-Joiner Test, Continuity Correction, Approximation of Discrete Binomial Distribution with Continuous Normal Distribution
- Estimation, Confidence Intervals, and Sample Size Determination for the Proportion, Confidence Intervals for some Means
- Degrees of Freedom, Student t Distribution, Chi-Square Distribution, Confidence Intervals, and Sample Size Determination for the Mean, Confidence Intervals, and Sample Size Determination for the Standard Deviation/Variance
- Hypothesis Testing, Critical Value Method, p-Value Method, Testing Claims about One-Population Proportions
- Hypothesis Testing Methods, Testing Claims about One-Population Means, Standard Deviations/Variances
- Two-Population Confidence Intervals, Testing Claims about Two-Population Proportions, Means with Independent Samples
- F Distribution, Testing Claims about Two-Population Means with Matched Pairs, Standard Deviations/Variances
- Scatterplots, Correlation, Linear Correlation, Coefficient of Determination, Testing Claims about Linear Correlation
- Testing Claims about Linear Correlation using Alternative Methods, Linear Regression, Prediction Intervals
- Contingency Tables, Testing Claims about Goodness-of-Fit, Independence, Homogeneity
- Testing Claims about One-Way ANOVA
