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Principles
of Statistics
Numbers need to be read correctly. Learn the statistical thinking that separates informed decisions from guesswork across every field.
What You'll Learn
A full semester of principles of statistics — completed at your own pace, with no deadlines or fixed class times.
What You'll Be Able to Do
- Master key statistical concepts, including descriptive and inferential statistics, probability distributions, measures of central tendency, and dispersion, while differentiating between data types for appropriate statistical treatments
- Analyze, interpret, and visualize business data using statistical software like Excel, performing accurate and efficient calculations for data manipulation and graph/chart creation
- Apply statistical methods to real-world business scenarios, using data-driven insights to inform and optimize business strategies, operational efficiencies, and market research; critically assess the quality and reliability of data sources, identify potential biases in statistical analysis, and understand their impact on business decision-making; present statistical findings clearly and concisely, suitable for various audiences, including writing reports and giving presentations to effectively communicate statistical analyses; and hone problem-solving abilities using statistical methods to address complex business challenges, focusing on developing logical and analytical thinking for data-driven problem-solving.
Major Course Topics
Course chapters and lessons
Principles of Statistics has 14 chapters across 67 instructional hours, as listed in the course syllabus. Expand any chapter to see the lessons inside it.
- Chapters
- 14
- Lessons
- 69
- Instructional hours
- 67 hrs
- Semester hours
- 3
Chapter 1
Introduction to Statistical Studies and Experimental Design
- 1.1 Introduction to Statistical Studies and Experimental Design
- 1.2 Understanding Data Types and Sampling Methods in Statistics
- 1.3 Understanding Data Organization and Measurement in Statistics
- 1.4 Experimental Design, Blinding, and Ethics in Statistical Studies
Chapter 2
Exploratory Data Analysis, Graphs, Measures of Central Tendency, and Data Distributions
- 2.1 Exploratory Data Analysis Using Stemplots, Line Graphs, and Bar Graphs
- 2.2 Constructing Time Series Graphs, Frequency Polygons, Histograms, and Statistical Integrity
- 2.3 Percentile and Quartile Calculations: Formula, Examples, and Interpretations
- 2.4 Understanding and Constructing Box Plots: A Comprehensive Guide
- 2.5 Measures of Central Tendency, Law of Large Numbers, and Sampling Distributions
- 2.6 Understanding Population and Sample Mean with a Practical Example
- 2.7 Understanding Measures of Central Tendency and the Geometric Mean
- 2.8 Analyzing Data Distributions and Measures of Central Tendency
- 2.9 Understanding and Calculating Standard Deviation for Grouped Frequency Tables and Comparisons Across Data Sets
Chapter 3
Mastering Probability – Concepts, Calculations, and Practical Applications
- 3.1 Understanding Probability: Key Concepts, Calculations, and Examples
- 3.2 Understanding Independent, Mutually Exclusive Events, and Conditional Probability with Examples
- 3.3 Probability Rules, Examples, and Solutions
- 3.4 Tree Diagrams and Conditional Probability Calculations
- 3.5 Understanding Venn Diagrams and Probability with Practical Examples
Chapter 4
Advanced Probability Distributions and Their Applications
- 4.1 Discrete Probability Distribution and Probability Distribution Tables
- 4.2 Hypergeometric Probability Distribution and Applications
- 4.3 Understanding the Binomial Distribution and Its Applications in Probability Theory
- 4.4 Geometric Distribution and Its Applications in Probability
- 4.5 Poisson Distribution and Its Applications in Probability Theory
- 4.6 Expected Value, Standard Deviation, and the Law of Large Numbers
Chapter 5
Continuous Probability Distributions and Their Applications
- 5.1 Continuous Probability Density Functions and Calculations
- 5.2 Continuous Probability Distributions and Their Applications
- 5.3 Uniform Distribution and Probability
- 5.4 Exponential Probability Density Function, Memorylessness, and Relationship Between Exponential and Poisson Distributions
Chapter 6
Understanding the Normal Distribution: Z-Scores, Probabilities, and Estimating Binomial Outcomes
- 6.1 Standard Normal Distribution: Z-Scores, Empirical Rule, and Examples
- 6.2 Calculating Probabilities in Normal Distributions
- 6.3 Using Normal Distribution to Estimate Binomial Probabilities
Chapter 7
Comprehensive Applications of the Central Limit Theorem and Sampling Distribution Concepts
- 7.1 Central Limit Theorem and Sampling Distribution Applications
- 7.2 Central Limit Theorem for Sums and Applications in Probability
- 7.3 Central Limit Theorem and Law of Large Numbers: Examples and Applications
- 7.4 Central Limit Theorem and the Sampling Distribution of Sample Proportions
- 7.5 Finite Population Correction Factor and Its Applications in Sampling Distribution
Chapter 8
Confidence Intervals, Distributions, and Sample Size Calculations
- 8.1 Confidence Intervals: Changing Confidence Levels, Sample Size, and Working Backwards
- 8.2 Student's t-Distribution and Confidence Intervals
- 8.3 Confidence Intervals for Proportions and the "Plus Four" Method
- 8.4 Continuous and Binary Random Variables
Chapter 9
Comprehensive Overview of Hypothesis Testing Concepts and Applications
- 9.1 Hypothesis Testing: Null and Alternative Hypotheses with Examples
- 9.2 Hypothesis Testing: Type I and Type II Errors
- 9.3 Decision Rule for Hypothesis Testing and Sample Mean Distribution
- 9.4 Hypothesis Testing: Understanding Rare Events, p-Values, and Decision Making
- 9.5 Full Hypothesis Testing for Proportions and Means Test Examples
Chapter 10
Advanced Hypothesis Testing for Two Population Comparisons
- 10.1 Comparison of Two Independent Population Means Using Aspin-Welch t-test
- 10.2 Cohen's Standards for Small, Medium, and Large Effect Sizes
- 10.3 Proportion Hypothesis Testing and Comparison of Two Population Proportions
- 10.4 Paired Sample Hypothesis Testing in Business and Quality Control
- 10.5 Hypothesis Testing for Two Population Means with Pooled Variance
Chapter 11
Chi-Square Distribution and Hypothesis Testing
- 11.1 Chi-Square Distribution and Its Properties
- 11.2 Chi-Square Goodness-of-Fit Test Examples and Solutions
- 11.3 Tests of Independence and Contingency Tables
- 11.4 Goodness-of-Fit Test and Test for Homogeneity
- 11.5 Comparison of Chi-Square Tests: Goodness-of-Fit, Independence, and Homogeneity
- 11.6 Hypothesis Testing for Population Variance
Chapter 12
Linear Regression and Its Applications: Equations, Scatter Plots, Correlation, and Outliers
- 12.1 Linear Regression and Applications
- 12.2 Linear Regression, Scatter Plots, and Understanding Relationships between Variables
- 12.3 Understanding Regression Analysis
- 12.4 Understanding Correlation, Hypothesis Testing, and Regression Analysis
- 12.5 Least Squares Regression and Prediction in Exam Scores
- 12.6 Identifying and Handling Outliers in Regression Analysis
Chapter 13
Analysis of Variance (ANOVA) and F-Distribution – Concepts, Hypothesis Testing, and Variance Analysis
- 13.1 One-Way ANOVA Test: Assumptions, Hypotheses, and Calculation
- 13.2 One-Way ANOVA and F-Distribution: Hypothesis Testing and Variance Analysis
- 13.3 F Distribution and ANOVA Analysis Examples
- 13.4 F Test for Equality of Two Variances
Chapter 14
Non-Parametric Statistical Tests and Their Applications
- 14.1 The Mann-Whitney U Test
- 14.2 Wilcoxon Signed Ranks Test for Matched Pairs Analysis
- 14.3 Spearman's Rank Correlation: Understanding and Application in Real-World Scenarios
These chapters come straight from the official course syllabus, which also sets out the graded assignment and the proctored final exam.
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