Business Statistics for Beginners
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.
- Price: $199.99
- Delivery Method: eLearning
| Name | Buy |
|---|---|
| Business Statistics for Beginners |
Test Prep
Features
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- Get assistance every step of the way from our Texas-based team, ensuring your training experience is hassle-free and aligned with your goals.
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- Benefit from being taught by Professionally Certified Instructors with expertise in their fields and a strong commitment to making sure you learn and succeed.
Outline
Lessons 1:
Introduction
- About This Course
- 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
- 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
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
