Introductory Business Statistics 1st edition

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• Chapter 1: Sampling and Data
  • 1.1: Definitions of Statistics, Probability, and Key Terms
  • 1.2: Data, Sampling, and Variation in Data and Sampling
  • 1.3: Levels of Measurement
  • 1.4: Experimental Design and Ethics
  
  
• Chapter 2: Descriptive Statistics
  • 2.1: Display Data
  • 2.2: Measures of the Location of the Data
  • 2.3: Measures of the Center of the Data
  • 2.4: Sigma Notation and Calculating the Arithmetic Mean
  • 2.5: Geometric Mean
  • 2.6: Skewness and the Mean, Median, and Mode
  • 2.7: Measures of the Spread of the Data
  
  
• Chapter 3: Probability Topics
  • 3.1: Terminology
  • 3.2: Independent and Mutually Exclusive Events
  • 3.3: Two Basic Rules of Probability
  • 3.4: Contingency Tables and Probability Trees
  • 3.5: Venn Diagrams
  • 3: Bringing it Together
  
  
• Chapter 4: Discrete Random Variables
  • 4.0: Introduction
  • 4.1: Hypergeometric Distribution
  • 4.2: Binomial Distribution
  • 4.3: Geometric Distribution
  • 4.4: Poisson Distribution
  
  
• Chapter 5: Continuous Random Variables
  • 5.1: Properties of Continuous Probability Density Functions
  • 5.2: The Uniform Distribution
  • 5.3: The Exponential Distribution
  
  
• Chapter 6: The Normal Distribution
  • 6.1: The Standard Normal Distribution
  • 6.2: Using the Normal Distribution
  • 6.3: Estimating the Binomial with the Normal Distribution
  
  
• Chapter 7: The Central Limit Theorem
  • 7.1: The Central Limit Theorem for Sample Means
  • 7.2: Using the Central Limit Theorem
  • 7.3: The Central Limit Theorem for Proportions
  • 7.4: Finite Population Correction Factor
  
  
• Chapter 8: Confidence Intervals
  • 8.1: A Confidence Interval for a Population Standard Deviation, Known or Large Sample Size
  • 8.2: A Confidence Interval for a Population Standard Deviation Unknown, Small Sample Case
  • 8.3: A Confidence Interval for A Population Proportion
  • 8.4: Calculating the Sample Size n: Continuous and Binary Random Variables
  
  
• Chapter 9: Hypothesis Testing with One Sample
  • 9.1: Null and Alternate Hypotheses
  • 9.2: Outcomes and the Type I and Type II Errors
  • 9.3: Distribution Needed for Hypothesis Testing
  • 9.4: Full Hypothesis Test Examples
  
  
• Chapter 10: Hypothesis Testing with Two Samples
  • 10.1: Comparing Two Independent Population Means
  • 10.2: Cohen's Standards for Small, Medium, and Large Effect Sizes
  • 10.3: Test for Differences in Means: Assuming Equal Population Variances
  • 10.4: Comparing Two Independent Population Proportions
  • 10.5: Two Population Means with Known Standard Deviations
  • 10.6: Matched or Paired Samples
  • 10: Bringing it Together
  
  
• Chapter 11: The Chi-Square Distribution
  • 11.1: Facts About the Chi-Square Distribution
  • 11.2: Test of a Single Variance
  • 11.3: Goodness-of-Fit Test
  • 11.4: Test of Independence
  • 11.5: Test for Homogeneity
  • 11.6: Comparison of the Chi-Square Tests
  • 11: Bringing it Together
  
  
• Chapter 12: F Distribution and One-Way ANOVA
  • 12.1: Test of Two Variances
  • 12.2: One-Way ANOVA
  • 12.3: The F Distribution and the F-Ratio
  • 12.4: Facts About the F Distribution
  
  
• Chapter 13: Linear Regression and Correlation
  • 13.1: The Correlation Coefficient r
  • 13.2: Testing the Significance of the Correlation Coefficient
  • 13.3: Linear Equations
  • 13.4: The Regression Equation
  • 13.5: Interpretation of Regression Coefficients: Elasticity and Logarithmic Transformation
  • 13.6: Predicting with a Regression Equation
  • 13.7: How to Use Microsoft Excel^® for Regression Analysis