Business Statistics for Beginners
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Test Prep
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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