### Business Statistics for Beginners

ISBN: 978-1-64459-461-2uCertify BUS-STATS.AE1

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

This course includes

Lessons

Lab

The Business Statistics for Beginners 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 carefully organized the material to help you learn the basic skills you need to use statistical insights effectively in a business setting. The 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.

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

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

1

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

2

- 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

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

4

- 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

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

6

- Understanding Covariance and Correlation
- Interpreting the Correlation Coefficient

7

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

8

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

9

- 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

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

11

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

12

- Almost Normal: The Student’s t-Distribution

13

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

14

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

15

- 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

- 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

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

18

- 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

- 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

- 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

- 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

- 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

- 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

- Calculating Percentiles
- Finding Quartiles
- Finding Coefficient of Variation

- Calculating the Sample Covariance

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

- Calculating the Probability Distribution
- Calculating the Expected Value

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

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

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

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

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

- Calculating the Error Sum of Squares (SSE)

- Testing Hypotheses about the Population Variance

- 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)

- Visualizing the Test Statistics

- Analyzing User Growth Trends