Introduction to business statistics / Ronald M. Weiers

Part 1: BUSINESS STATISTICS: INTRODUCTION AND BACKGROUND
1. A Preview of Business Statistics
Introduction
Statistics: Yesterday and Today
Descriptive Versus Inferential Statistics
Types of Variables and Scales of Measurement
Statistics in Business Decisions
Business Statistics: Tools Versus Tricks
Summary
2. Visual Description of Data
Introduction
The Data Array and the Frequency Distribution
The Stem and Leaf Display and the Dotplot
Visual Representation of the Data
The Scatter Diagram
Tabulation, Contingency Tables, and the Excel PivotTable Wizard
Summary
3. Statistical Description of Data
Introduction
Statistical Description: Measures of Central Tendency
Statistical Description: Measures of Dispersion
Additional Dispersion Topics
Descriptive Statistics from Grouped Data
Statistical Measures of Association
Summary
4. Data Collection and Sampling Methods
Introduction
Research Basics
Survey Research
Experimentation and Observational Research
Secondary Data
The Basics of Sampling
Sampling Methods
Summary
Part 2: PROBABILITY
5. Probability: Review of Basic Concepts
Introduction
Probability: Terms and Approaches
Unions and Intersections of Events
Addition Rules for Probability
Multiplication Rules for Probability
Bayes' Theorem and the Revision of Probabilities
Counting: Permutations and Combinations
Summary
6. Discrete Probability Distributions
Introduction
The Binomial Distribution
The Poisson Distribution
Simulating Observations from a Discrete Probability Distribution
Summary
7. Continuous Probability Distributions
Introduction
The Normal Distribution
The Standard Normal Distribution
The Normal Approximation to the Binomial Distribution
The Exponential Distribution
Simulating Observations from a Continuous Probability Distribution
Summary
Part 3: SAMPLING DISTRIBUTION AND ESTIMATION
8. Sampling Distributions
Introduction
A Review of Sampling Distributions
The Sampling Distribution of the Mean
The Sampling Distribution of the Proportion
Sampling Distributions When the Population is Finite
Computer Simulation of Sampling Distributions
Summary
9. Estimation from Simple Data
Introduction
Point Estimates
A Preview of Interval Estimates
Confidence Interval Estimates for the Mean: s Known
Confidence Interval Estimates for the Mean: s Unknown
Confidence Interval Estimates for the Population Proportion
Sample Size Determination
When the Population is Finite
Summary
Part 4: HYPOTHESIS TESTING
10. Hypothesis Tests Involving a Simple Mean or Proportion
Introduction
Hypothesis Testing: Basic Procedures
Testing a Mean, Population Standard Deviation Known
Confidence Intervals and Hypothesis Testing
Testing a Mean, Population Standard Deviation Unknown
Testing a Proportion
The Power of a Hypothesis Test
Summary
11. Hypothesis Tests Involving Two Simple Means or Proportions
Introduction
The Pooled-Variances t-Test for Comparing the Means of Two Independent Samples
The Unequal-Variances t-Test for Comparing the Means of Two Independent Samples
The z-Test for Comparing the Means of Two Independent Samples
Comparing Two Means When the Samples are Dependent
Comparing Two Sample Proportions
Comparing the Variances of Two Independent Samples
Summary
12. Analysis of Variance Tests
Introduction
Analysis of Variance: Basic Concepts
One-Way Analysis of Variance
The Randomized Block Design
Two-Way Analysis of Variance
Summary
13. Chi-Square Applications
Introduction
Basic Concepts in Chi-Square Testing
Tests for Goodness-of-Fit and Normality
Testing the Independence of Two Variables
Comparing Proportions from k Independent Samples
Estimation and Tests Regarding the Population Variance
Summary
14. Nonparametric Methods
Introduction
Wilcoxon Signed Rank Test for One Sample
Wilcoxon Signed Rank Test for Comparing Paired Samples
Wilcoxon Rank Sum Test for Comparing Two Independent Samples
Kruskal-Wallis Test for Comparing More Than Two Independent Samples
Friedman Test for the Randomized Block Design
Other Nonparametric Methods
Summary
Part 5: REGRESSION, MODEL BUILDING, AND TIME SERIES
15. Simple Linear Regression and Correlation
Introduction
The Simple Linear Regression Model
Interval Estimation Using the Sample Regression Line
Correlation Analysis
Estimation and Tests Regarding the Sample Regression Line
Additional Topics in Regression and Correlation Analysis
Summary
16. Multiple Regression and Correlation
Introduction
The Multiple Regression Model
Interval Estimation in Multiple Regression
Multiple Correlation Analysis
Significance Tests in Multiple Regression and Correlation
Overview of the Computer Analysis and Interpretation
Additional Topics in Multiple Regression and Correlation
Summary
17. Model Building
Introduction
Polynomial Models with One Quantitative Predictor Variable
Polynomial Models with Two Quantitative Predictor Variables
Qualitative Variables
Data Transformations
Multicollinearity
Stepwise Regression
Selecting a Model
Summary
18. Time Series, Forecasting and Index Numbers
Introduction
Time Series
Smoothing Techniques
Seasonal Indexes
Forecasting
Evaluating Alternative Models: MAD and MSE
Autocorrelation, the Durbin-Watson Test, and Autoregressive Forecasting
Index Numbers
Summary
Part 6: SPECIAL TOPICS
19. Decision Theory
Introduction
Structuring the Decision Situation
Non-Bayesian Decision Making
Bayesian Decision Making
The Opportunity Loss Approach
Incremental Analysis and Inventory Decisions
Summary
Appendix: The Expected Value of Imperfect Information
20. Total Quality Mangement
Introduction
A Historical Perspective and Defect Detection
The Emergence of Total Quality Management
Practicing Total Quality Management
Some Statistical Tools for Total Quality Management
Statistical Process Control: The Concepts
Control Charts for Variables
Control Charts for Attributes
More on Computer-Assisted Statistical Process Control
Summary

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