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Reflecting the latest trends and methodologies, this comprehensive and innovative study on business statistics uses a practical, data-analytic approach. Based on the development of a survey which integrates the various topics and provides a cohesive study of descriptive statistics, probability, statistical inference, and regression analysis, it now focuses on data analysis and interpretation of computer output with a reduced focus on hand calculations. Creates an Employee Satisfaction Survey yielding 400 sample responses which readers can use to integrate such topics as descriptive statistics, probability, statistical inference, and regression analysis. Offers over 1200 realistic applications problems, 170 Survey/Database Projects, and relevant case studies. Contains two distinct types of summary sections to facilitate understanding - Exploratory and Confirmatory Data Analysis sections (looking at the four components of good data analysis - plotting, observing, computing and describing), and Ethical Issue sections (helps readers learn to think critically about the ramifications of the ethical issues involved in data analysis). Provides thorough coverage of regression and multiple regression, and considers many popular methodologies, including exploratory data analysis (EDA) techniques and dot charts, Pareto diagrams and supertables. Now opens each chapter with a "Using Statistics" example that shows how statistics can be applied to accounting, finance, management or marketing - plus includes appendices on using Microsoft Excel 97 and Minitab; an additional chapter on multiple regression that focuses on model building; a new chapter on decision making; a running case study, and more.

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• 1 Introduction and Data Collection
• Overview of Statistics
• Applying Statistics to Business
• How This Text Is Organized
• The Importance of Collecting Data
• Identifying Sources of Data
• Design of Survey Research
• Types of Survey Sampling Methods
• Types of Data
• Evaluating Survey Worthiness
• 2 Presenting Data in Tables and Charts
• Organizing Numerical Data
• Tables and Charts for Numerical Data
• Graphing Bivariate Numerical Data
• Tables and Charts for Categorical Data
• Tabulating and Graphing Bivariate Categorical Data
• Graphical Excellence
• 3 Numerical Descriptive Measures
• Exploring Numerical Data and Their Properties
• Measures of Central Tendency, Variation, and Shape
• Obtaining Descriptive Summary Measures from a Population
• Obtaining Descriptive Summary Measures from a Frequency Distribution
• Exploratory Data Analysis
• The Coefficient of Correlation
• Pitfalls in Numerical Descriptive Measures and Ethical Issues
• 4 Basic Probability
• Basic Probability Concepts
• Conditional Probability
• Bayes' Theorem
• Counting Rules
• Ethical Issues and Probability
• 5 Some Important Discrete Probability Distributions
• The Probability Distribution for a Discrete Random Variable
• Covariance and Its Application in Finance
• Binomial Distribution
• Hypergeometric Distribution
• Poisson Distribution
• CD ROM Topic Using the Poisson Distribution to Approximate the Binomial Distribution
• 6 The Normal Distribution and Other Continuous Distributions
• The Normal Distribution
• Evaluating the Normality Assumption
• The Uniform Distribution
• The Exponential Distribution
• CD ROM Topic The Normal Approximation to the Binomial Distribution
• 7 Sampling Distributions
• Sampling Distribution of the Mean
• Sampling Distribution of the Proportion
• CD ROM Topic Sampling from Finite Populations
• 8 Confidence Interval Estimation
• Confidence Interval Estimation of the Mean (s Known)
• Confidence Interval Estimation of the Mean (s Unknown)
• Confidence Interval Estimation for the Proportion
• Determining Sample Size
• Applications of Confidence Interval Estimation in Auditing
• Confidence Interval Estimation and Ethical Issues
• CD ROM Topic Estimation and Sample Size Determination for Finite
• 9 Fundamentals of Hypothesis Testing: One-Sample Tests
• Hypothesis-Testing Methodology
• Z Test of Hypothesis for the Mean (s Known)
• One-Tailed Tests
• T Test of Hypothesis for the Mean (s Unknown)
• Z Test of Hypothesis for the Proportion
• X2 Test of Hypothesis for the Variance or Standard Deviation
• Potential Hypothesis-Testing Pitfalls and Ethical Issues
• CD ROM Topic The Power of a Test
• 10 Two Sample Tests with Numerical Data
• Comparing Two Independent Samples: Tests for Differences in Two Means
• F Test for Differences in Two Variances
• Comparing Two Related Samples: Tests for the Mean Difference
• Wilcoxon Rank Sum Test: Nonparametric Analysis for Two Independent Populations
• Wilcoxon Signed-Ranks Test: Nonparametric Analysis for Two Related Populations
• 11 Analysis of Variance
• The Completely Randomized Design: One-Way Analysis of Variance
• The Randomized Block Design
• The Factorial Design: Two-Way Analysis of Variance
• Kruskal-Wallis Rank Test: Nonparametric Analysis for the One-Way Design
• Friedman Rank Test: Nonparametric Analysis for the Randomized Block Design
• 12 Tests for Two or More Samples with Categorical Data
• Z Test for the Difference between Two Proportions
• Chi-Square Test for Differences between Two Proportions
• Chi-Square Test for Differences in Among More than Two Proportions
• Chi-Square Test of Independence
• CD ROM Topic Chi-Square Goodness of Fit Tests
• 13 Simple Linear Regression
• Types of Regression Models
• Determining the Simple Linear Regression Equation
• Measures of Variation
• Assumptions
• Residual Analysis
• Measuring Autocorrelation: The Durbin-Watson Statistic
• Inferences about the Slope and Correlation Coefficient
• Estimation of Predicted Values
• Pitfalls in Regression and Ethical Issues
• Computations in Simple Linear Regression
• 14 Introduction to Multiple Regression
• Developing the Multiple Regression Model
• Residual Analysis for the Multiple Regression Model
• Testing for the Significance of the Multiple Regression Model
• Inferences Concerning the Population Regression Coefficients
• Testing Portions of the Multiple Regression Model
• Using Dummy-Variables and Interaction Terms in Regression Models
• Introduction to Logistic Regression
• 15 Multiple Regression Model Building
• The Quadratic Regression Model
• Using Transformations in Regression Models
• Influence Analysis
• Collinearity
• Model Building
• Pitfalls in Multiple Regression and Ethical Issues
• 16 Time-Series Analysis
• The Importance of Business Forecasting
• Component Factors of the Classical Multiplicative Time-Series Model
• Smoothing the Annual Time Series
• Least-Squares Trend Fitting and Forecasting
• The Holt-Winters Method for Trend Fitting and Forecasting
• Autoregressive Modeling for Trend Fitting and Forecasting
• Choosing an Appropriate Forecasting Model
• Time-Series Forecasting of Monthly or Quarterly Data
• Index Numbers
• Pitfalls Concerning Time-Series Analysis
• 17 Decision Making
• Payoff Tables and Decision Trees
• Criteria for Decision Making
• Decision Making with Sample Information
• Utility
• 18 Statistical Applications in Quality and Productivity Management
• Total Quality Management
• Six SigmaÂ" Management
• The Theory of Control Charts
• Control Chart for the Proportion of Nonconforming Items The p Chart
• The Red Bead Experiment: Understanding Process Variability
• The c Chart
• Control Charts for the Range and the Mean
• Process Capability