# Business Statistics for Beginners

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

This course includes

Lessons

Lab

## About This Course

### Skills You’ll Get

Get the support you need. Enroll in our Instructor-Led Course.

### Interactive Lessons

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

1

### Introduction

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

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

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

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

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

6

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

- Understanding Covariance and Correlation
- Interpreting the Correlation Coefficient

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

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

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

10

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

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

11

### Sampling Techniques and Distributions

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

12

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

- Almost Normal: The Student’s t-Distribution

13

### Testing Hypotheses about the Population Mean

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

14

### Testing Hypotheses about Multiple Population Means

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

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

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

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

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

19

### Ten Common Errors That Arise in Statistical Analysis

- Designing Misleading Graphs
- 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

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

### 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
- Visualizing Exam Grades Distribution

### 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