Business Statistics: A First Course 8th Edition By David Levine, Kathryn Szabat, David Stephan
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Business Statistics: A First Course 8th Edition By David Levine, Kathryn Szabat, David Stephan

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732
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About this ebook
Business Statistics: A First Course (8th Edition) by David Levine, Kathryn Szabat, and David Stephan covers introductory to advanced business statistical methods across 17 primary chapters, emphasizing data interpretation over manual calculation. [1, 2]
The complete breakdown of the core topics and chapters included in the Pearson 8th Edition textbook includes: []
Data Collection and Visualization
  • Online Chapter: First Things First – Introduction to big data, analytics, and business metrics.
  • Chapter 1: Defining and Collecting Data – Variable types, sampling methods, data cleaning, and survey errors.
  • Chapter 2: Organizing and Visualizing Variables – Categorical and numerical tables, charts, and data visualization tools. [, 2, 3]
Descriptive Measures and Probability
  • Chapter 3: Numerical Descriptive Measures – Central tendency, dispersion, variance, shape, and population summary measures.
  • Chapter 4: Basic Probability – Probability fundamentals, joint probability, and conditional probability.
  • Chapter 5: Discrete Probability Distributions – Binomial, Poisson, and covariance-related distributions.
  • Chapter 6: The Normal Distribution – Continuous distributions, standard normal distribution, and evaluating normality. [1, 2, 3]
Statistical Inference
  • Chapter 7: Sampling Distributions – Sampling distribution of the mean, sample proportion, and the Central Limit Theorem.
  • Chapter 8: Confidence Interval Estimation – Intervals for means, proportions, and sample size determination.
  • Chapter 9: Fundamentals of Hypothesis Testing: One-Sample Tests – Type I and II errors, Z-tests, and t-tests.
  • Chapter 10: Two-Sample Tests – Comparing two independent means, numerical variances, and two proportions.
  • Chapter 11: Analysis of Variance (ANOVA) – One-way ANOVA and F-tests for multiple mean comparisons.
  • Chapter 12: Chi-Square and Nonparametric Tests – Contingency tables and tests for categorical data. [1, 2, 3]
Regression Models and Business Analytics
  • Chapter 13: Simple Linear Regression – Correlation, slope equations, and forecasting applications.
  • Chapter 14: Introduction to Multiple Regression – Multiple variables, coefficient interpretation, and model building.
  • Chapter 15: Time Series Forecasting – Trend analysis, smoothing methods, and regression in time series.
  • Chapter 16: Statistical Applications in Quality Management – Control charts and process capability indexes.
  • Chapter 17: Business Analytics – Predictive analytics, data mining, classification, clustering, and association techniques. [, 2, 3, 4, 5, 6]

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Topics

Analysis of Variance (ANOVA) Basic Probability – Probability fundamentals Business Analytics – Predictive analytics data mining Business Statistics A First Course Chi-Square and Nonparametric Tests coefficient interpretation and model building Confidence Interval Estimation Descriptive Measures and Probability Discrete Probability Distributions: Binomial Poisson Fundamentals of Hypothesis Testing: One-Sample Tests Introduction to big data analytics and business metrics Introduction to Multiple Regression – Multiple variables Numerical Descriptive Measures – Central tendency dispersio One-way ANOVA and F-tests for multiple mean comparisons. Organizing and Visualizing Variables – Categorical and numerical tables Regression Models and Business Analytics Sampling distribution of the mean sample proportion Simple Linear Regression – Correlation slope equations smoothing methods and regression in time series. standard normal distribution Statistical Applications in Quality Management The Normal Distribution – Continuous distributions Time-Series Forecasting: Trend analysis Two-Sample Tests – Comparing two independent means Type I and II errors Z-tests and t-tests. Variable types sampling methods data cleaning and survey errors.
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