Basic Business Statistics Concepts and Applications 15th Edition By Mark Berenson, David Levine, Kathryn Szabat, David Stephan
eBook[PDF]

Basic Business Statistics Concepts and Applications 15th Edition By Mark Berenson, David Levine, Kathryn Szabat, David Stephan

$30.00
✓ Secure checkout
✓ Instant digital download
✓ PDF document

Document details

Pages
914
File size
23.77 MB
Format
Digital PDF
Category
eBook[PDF]
About this ebook
Basic Business Statistics: Concepts and Applications (15th Edition) by Mark Berenson, David Levine, Kathryn Szabat, and David Stephan covers a comprehensive range of foundational and advanced statistical methodologies structured around the DCOSAC framework (Define, Collect, Organize, Summarize, Analyze, and Communicate). [1, 2]
The textbook integrates theory with hands-on practice using core data tools like Microsoft Excel, Tableau, Minitab, and JMP. [1]
Core Topics and Table of Contents
Part 1: Getting Started and Descriptive Statistics
  • Defining and Collecting Data: Understanding variables, data sources, and sampling methodologies.
  • Organizing and Visualizing Variables: Structuring data using tables, charts, and graphical summaries.
  • Numerical Descriptive Measures: Calculating central tendency, variation, shape, and population parameters. [1, 2]
Part 2: Probability and Probability Distributions
  • Basic Probability: Fundamental concepts, conditional probability, and Bayes’ theorem.
  • Discrete Probability Distributions: Binomial, Poisson, and hypergeometric distributions.
  • The Normal Distribution and Other Continuous Distributions: The normal curve, evaluating normality, and uniform/exponential distributions. [1]
Part 3: Inferential Statistics
  • Sampling Distributions: Core distribution theory and the Central Limit Theorem.
  • Confidence Interval Estimation: Determining intervals for means and proportions, and calculating sample sizes.
  • Fundamentals of Hypothesis Testing (One-Sample Tests): Z-tests, t-tests, and proportion testing methodologies.
  • Two-Sample Tests: Comparing two independent or related populations.
  • Analysis of Variance (ANOVA): One-way ANOVA, randomized block designs, and two-way ANOVA.
  • Chi-Square and Nonparametric Tests: Contingency tables, goodness-of-fit tests, and distribution-free analytical tools. [1]
Part 4: Regression and Analytics (Expanded in the 15th Edition)
  • Simple Linear Regression: Analyzing linear relationships, determining coefficients, and forecasting.
  • Introduction to Multiple Regression: Managing models with multiple independent variables.
  • Multiple Regression Model Building: Selecting variables, handling multicollinearity, and validating step-wise models.
  • Time-Series Forecasting: Trend analysis, seasonal adjustments, and predicting future trends.
  • Business Analytics: Practical introductory concepts concerning descriptive, predictive, and prescriptive modeling. [1, 2]
Part 5: Future Directions and Online Content
  • Getting Ready to Analyze Data in the Future: Adapting to expanding technologies and growing business intelligence domains.
  • Statistical Applications in Quality Management (Online Chapter): Control charts and Six Sigma process optimization.
  • Decision Making (Online Chapter): Opportunity loss, payoff tables, and utility theory. [1, 2]
What's New in the 15th Edition?
The 15th Edition introduces several updates designed to align with modern corporate environments:
  • Expanded Analytics & Regression: Heavily updated sections covering advanced regression modeling and basic data science applications.
  • Software Integration: Full procedural guidance for interpreting outputs inside Microsoft Excel, Tableau, Minitab, and JMP.
  • GAISE Framework Compliance: Upgraded directly to match the American Statistical Association’s Guidelines for Assessment and Instruction in Statistics Education (GAISE), emphasizing interpretation and communication over raw manual math. [1]

File included

PDF
tmpphp542hEm 23.77 MB

Topics

Analysis of Variance (ANOVA) and hypergeometric distributions Basic Probability: Fundamental concepts Business Analytics: Practical introductory concepts Chi-Square and Nonparametric Tests conditional probability and Bayes’ theorem. Confidence Interval Estimation Core distribution theory and the Central Limit Theorem. Decision Making Opportunity loss payoff tables and utility theory. Defining and Collecting Data: Understanding variables descriptive predictive and prescriptive modeling Discrete Probability Distributions: Binomial Poisson expanding technologies and growing business intelligence domains Fundamentals of Hypothesis Testing (One-Sample Tests): Z-tests t-tests GAISE Framework Compliance Getting Started and Descriptive Statistics Guidelines for Assessment and Instruction in Statistics Education (GAISE) Inferential Statistics Sampling Distributions Introduction to Multiple Regression Microsoft Excel Tableau Minitab and JMP Numerical Descriptive Measures: Calculating central tendency Organizing and Visualizing Variables Probability and Probability Distributions Regression and Analytics Statistical Applications in Quality Management The Normal Distribution and Other Continuous Distributions Time-Series Forecasting: Trend analysis
Related ebooks