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Description
Statistics for Business and Economics is a straightforward and detailed introduction to the concepts and theory which appear in most undergraduate or MBA courses in this field. The text complements such well-referenced textbooks as D.K. Hildebrand and R.L. Ott’s “Basic Statistical Ideas for Managers,” and “The Practice of Business Statistics: Using Data for Decisions,” by D.S. Moore et al. It is available as a free e-book and can be downloaded here.
The text starts by outlining the main steps of statistical analysis, from data gathering and data handling to establishing probability and statistic interference within a sample. Moving on, readers are introduced to data description and measures of dispersion, basic principals of probability (including set theory), probability distributions (including random variables, random vectors and joint distributions, and conditional density functions), random sampling, point and interval estimation, and hypothesis testing.
Full-color charts and diagrams are used alongside theorems and equations throughout the text. Each chapter also includes multiple examples for practice and better comprehension.
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Preface
This compendium aims at providing a comprehensive overview of the main topics that appear in any well-structured course sequence in statistics for business and economics at the undergraduate and MBA levels. The idea is to supplement either formal or informal statistic textbooks such as, e.g., “Basic Statistical Ideas for Managers” by D.K. Hildebrand and R.L. Ott and “The Practice of Business Statistics: Using Data for Decisions” by D.S. Moore, G.P. McCabe, W.M. Duckworth and S.L. Sclove, with a summary of theory as well as with a couple of extra examples. In what follows, we set the road map for this compendium by describing the main steps of statistical analysis.
Content
- Introduction
- Gathering data
- Data handling
- Probability and statistical inference
- Data description
- Data distribution
- Typical values
- Measures of dispersion
- Basic principles of probability
- Set theory
- From set theory to probability
- Probability distributions
- Random variable
- Random vectors and joint distributions
- Marginal distributions
- Conditional density function
- Independent random variables
- Expected value, moments, and co-moments
- Discrete distributions
- Continuous distributions
- Random sampling
- Sample statistics
- Large-sample theory
- Point and interval estimation
- Point estimation
- Interval estimation
- Hypothesis testing
- Rejection region for sample means
- Size, level, and power of a test
- Interpreting p-values
- Likelihood-based tests
Statistics For Business And Economics Pdf 13e
WEEK # | TOPICS |
---|---|
1 | Set and Probability Theory (PDF) Basics of Set Theory |
2 | Random Variables, Probability Mass/Density Function, and Cumulative Distribution Function (Univariate Model) (PDF) |
3 | Multiple Random Variables, Bivariate Distribution, Marginal Distribution, Conditional Distribution, Independence, Multivariate Distribution (Multivariate Model) (PDF) |
4 | Expectation (Moments) (PDF) |
5 | Review for Exam 1 |
6 | Random Variable and Random Vector Transformations (Univariate and Multivariate Models) (PDF) |
7 | Special Distributions (Discrete and Continuous) (PDF) Graph Representation: Special Distributions (PDF) |
8 | Review for Exam 2 |
9 | Random Sample, Law of Large Numbers, Central Limit Theorem (PDF) Simulations: Magnifying Glass (PDF) |
10 | Point Estimators and Point Estimation Methods (PDF) An Overview (PDF) |
11 | Interval Estimation and Confidence Intervals (PDF) t-Student versus Standard Normal: A Graphical View (PDF) The t-Distribution versus the Normal Distribution (Java Applet) |
12 | Hypothesis Testing (PDF) An Applied Review (PDF) |
13 | Review for Exam 3 |