 (BUS-STATS.AE1)/ISBN:978-1-64459-461-2

This course includes
Lessons
Lab

This course is designed to empower individuals new to statistical analysis and eager to unlock the potential of data in the business realm. In this course, we've thoughtfully structured content to provide you with foundational skills essential for harnessing the power of statistical insights in a business context. Our comprehensive course covers key areas that form the backbone of business statistics. From fundamental concepts to hands-on practice and real-world applications, this course equips you with the tools to turn raw data into actionable insights.

#### Lessons

20+ Lessons | 55+ Exercises | 276+ Quizzes | 100+ Flashcards | 100+ Glossary of terms

# Here's what you will learn

### Lessons 1: Introduction

• Foolish Assumptions
• Icons Used in This Course
• Where to Go from Here

### Lessons 2: The Art and Science of Business Statistics

• Representing the Key Properties of Data
• Probability: The Foundation of All Statistical Analysis
• Using Sampling Techniques and Sampling Distributions
• Statistical Inference: Drawing Conclusions from Data

### Lessons 3: Pictures Tell the Story: Graphical Representations of Data

• Analyzing the Distribution of Data by Class or Category
• Histograms: Getting a Picture of Frequency Distributions
• Checking Out Other Useful Graphs

### Lessons 4: Finding a Happy Medium: Identifying the Center of a Data Set

• Looking at Methods for Finding the Mean
• Getting to the Middle of Things: The Median of a Data Set
• Comparing the Mean and Median
• Discovering the Mode: The Most Frequently Repeated Element

### Lessons 5: Searching High and Low: Measuring Variation in a Data Set

• Determining Variance and Standard Deviation
• Finding the Relative Position of Data
• Measuring Relative Variation

### Lessons 6: Measuring How Data Sets Are Related to Each Other

• Understanding Covariance and Correlation
• Interpreting the Correlation Coefficient

### Lessons 7: Probability Theory: Measuring the Likelihood of Events

• Working with Sets
• Betting on Uncertain Outcomes
• Looking at Types of Probabilities
• Following the Rules: Computing Probabilities

### Lessons 8: Probability Distributions and Random Variables

• Defining the Role of the Random Variable
• Assigning Probabilities to a Random Variable
• Characterizing a Probability Distribution with Moments

### Lessons 9: The Binomial, Geometric, and Poisson Distributions

• Looking at Two Possibilities with the Binomial Distribution
• Determining the Probability of the Outcome That Occurs First: Geometric Distribution
• Keeping the Time: The Poisson Distribution

### Lessons 10: The Uniform and Normal Distributions: So Many Possibilities!

• Comparing Discrete and Continuous Distributions
• Working with the Uniform Distribution
• Understanding the Normal Distribution

### Lessons 11: Sampling Techniques and Distributions

• Sampling Techniques: Choosing Data from a Population
• Sampling Distributions
• The Central Limit Theorem

### Lessons 12: Confidence Intervals and the Student’s t-Distribution

• Almost Normal: The Student’s t-Distribution

### Lessons 13: Testing Hypotheses about the Population Mean

• Applying the Key Steps in Hypothesis Testing for a Single Population Mean

### Lessons 14: Testing Hypotheses about Multiple Population Means

• Getting to Know the F-Distribution
• Using ANOVA to Test Hypotheses

### Lessons 15: Testing Hypotheses about the Population Mean

• Staying Positive with the Chi-Square Distribution
• Testing Hypotheses about the Population Variance
• Practicing the Goodness of Fit Tests
• Testing Hypotheses about the Equality of Two Population Variances

### Lessons 16: Simple Regression Analysis

• The Fundamental Assumption: Variables Have a Linear Relationship
• Defining the Population Regression Equation
• Estimating the Population Regression Equation
• Testing the Estimated Regression Equation
• Using Statistical Software
• Assumptions of Simple Linear Regression

### Lessons 17: Multiple Regression Analysis: Two or More Independent Variables

• The Fundamental Assumption: Variables Have a Linear Relationship
• Estimating a Multiple Regression Equation
• Checking for Multicollinearity

### Lessons 18: Forecasting Techniques: Looking into the Future

• Defining a Time Series
• Modeling a Time Series with Regression Analysis
• Forecasting a Time Series
• Changing with the Seasons: Seasonal Variation
• Implementing Smoothing Techniques
• Comparing the Forecasts of Different Models

### Lessons 19: Ten Common Errors That Arise in Statistical Analysis

• Drawing the Wrong Conclusion from a Confidence Interval
• Misinterpreting the Results of a Hypothesis Test
• Placing Too Much Confidence in the Coefficient of Determination (R2)
• Assuming Normality
• Thinking Correlation Implies Causality
• Drawing Conclusions from a Regression Equation when the Data do not Follow the Assumptions
• Including Correlated Variables in a Multiple Regression Equation
• Placing Too Much Confidence in Forecasts
• Using the Wrong Distribution

### Lessons 20: Ten Key Categories of Formulas for Business Statistics

• Summary Measures of a Population or a Sample
• Probability
• Discrete Probability Distributions
• Continuous Probability Distributions
• Sampling Distributions
• Confidence Intervals for the Population Mean
• Testing Hypotheses about Population Means
• Testing Hypotheses about Population Variances
• Using Regression Analysis
• Forecasting Techniques

# Hands-on LAB Activities (Performance Labs)

### The Art and Science of Business Statistics

• Understanding the Daily Step Counts of Your Club Members
• Keeping Track of Visitors on a Personal Blog
• Assessing the Level of Student Participation in Various Extracurricular Activities
• Conducting a Survey
• Visualizing the Temperature Fluctuations

### Pictures Tell the Story: Graphical Representations of Data

• Calculating the Relative Frequency
• Figuring the Class Width
• Calculating the Cumulative Frequency
• Illustrating a Cumulative Frequency
• Illustrating a Relative Frequency
• Illustrating a Frequency Distribution
• Representing Fluctuations of Gold Price

### Finding a Happy Medium: Identifying the Center of a Data Set

• Calculating the Arithmetic Mean
• Calculating the Weighted Geometric Mean
• Calculating the Weighted Arithmetic Mean
• Representing Positively Skewed Data Set
• Representing Negatively Skewed Data Set
• Representing Symmetrical Data Set
• Discovering the Mode

### Searching High and Low: Measuring Variation in a Data Set

• Calculating Percentiles
• Finding Quartiles
• Finding Coefficient of Variation

### Measuring How Data Sets Are Related to Each Other

• Calculating the Sample Covariance

### Probability Theory: Measuring the Likelihood of Events

• Performing Set Operations
• Looking at Types of Probabilities
• Finding Unconditional Probabilities
• Finding the Conditional Probability
• Calculating the Multiplication Rule
• Calculating the Complement Rule

### Probability Distributions and Random Variables

• Calculating the Probability Distribution
• Calculating the Expected Value

### The Binomial, Geometric, and Poisson Distributions

• Calculating the Binomial Probability
• Representing the Binomial Distribution
• Calculating Geometric Probabilities
• Computing Poisson Probabilities

### The Uniform and Normal Distributions: So Many Possibilities!

• Representing the Discrete Distribution
• Uniform Distribution: Computing Variance and Standard Deviation
• Calculating the Expected Value
• Computing Uniform Probabilities with Formulas

### Sampling Techniques and Distributions

• Portraying Sampling Distributions Graphically
• Calculating the Moments a Sampling Distribution
• Converting Random Variable into a Standard Normal Random Variable

### Confidence Intervals and the Student’s t-Distribution

• Graphing the t-distribution
• Calculating the Variance of a t-distribution

### Testing Hypotheses about the Population Mean

• Graphing the Standard Normal Distribution
• Determining the Two-Tailed Hypothesis Test
• Determining the Test Statistic

### Testing Hypotheses about Multiple Population Means

• Calculating the Error Sum of Squares (SSE)

### Testing Hypotheses about the Population Mean

• Testing Hypotheses about the Population Variance

### Simple Regression Analysis

• Calculating the Slope of a Line from Two Given Points
• Calculating Coefficients and Predicting Sales Revenue in Simple Linear Regression
• Calculating Total Sum of Squares (TSS)

### Multiple Regression Analysis: Two or More Independent Variables

• Visualizing the Test Statistics

### Forecasting Techniques: Looking into the Future

• Analyzing User Growth Trends