For courses in Business Statistics.
This comprehensive text focuses on the underlying statistical concepts that are important to students majoring in business. This Tenth edition has been substantially revised and rewritten to improve readability and comprehension, now featuring a more active, conversational writing style and a streamlined design. The authors take an applied approach and relate the concepts and applications of statistics to the functional areas of business-accounting, marketing, management, and economics and finance. This text also emphasizes the proper use of statistics to analyze data and assumes that computer software is an integral part of this analysis. Excel and Minitab, and SPSS® are featured.
Table of Contents:
1. Introduction and Data Collection.
Using Statistics: Good Tunes.
Basic Concepts of Statistics.
The Growth of Statistics and Information Technology.
How This Text Is Organized.
The Importance of Collecting Data.
Identifying Sources of Data.
Types of Data.
Levels of Measurement and Types of Measurement Scales.
Summary.
Appendix1. Introduction to Using Software.
2. Presenting Data in Tables and Charts.
Using Statistics: Comparing the Performance of Mutual Funds.
Tables and Charts for Categorical Data
The Summary Table
The Bar Chart
The Pie Chart
The Pareto Diagram
Organizing Numerical Data
The Ordered Array
The Stem-and-Leaf Display
Tables and Charts for Numerical Data
The Frequency Distribution
The Relative Frequency Distribution and the Percentage Distribution
The Cumulative Distribution
The Histogram
The Polygon
The Cumulative Percentage Polygon (Ogive)
Cross Tabulations
The Contingency Table
The Side-by-Side Bar Chart
Scatter Diagrams and Time Series Plots
The Scatter diagram
The Time series plot
Misusing Graphs and Ethical Issues
Summary
Appendix 2. Using Software for Tables and Charts.
3. Numerical Descriptive Measures.
Using Statistics: Comparing the Performance of Mutual Funds.
Measures of Central Tendency, Variation, and Shape.
The Mean
The Median
The Mode
Quartiles
The Geometric Mean
The Range
The Interquartile Range
The Variance and Standard Deviation
The Coefficient of Variation
Shape
Visual Explorations: Exploring Descriptive Statistics
Microsoft Excel Descriptive Statistics Output
Minitab Descriptive Statistics Output
Descriptive Numerical Measures for a Population.
The Population Mean
The Population Variance and Standard Deviation
The Empirical Rule
The Chebychev Rule
Computing Descriptive Numerical Measures from a Frequency Distribution.
Exploratory Data Analysis.
The Five-Number Summary
The Box-and-Whisker Plot
The Covariance and the Coefficient of Correlation.
The Covariance
The Coefficient of Correlation
Pitfalls in Numerical Descriptive Measures and Ethical Issues.
Summary.
Appendix 3. Using Software for Descriptive Statistics.
4. Basic Probability.
Using Statistics: The Consumer Electronics Company.
Basic Probability Concepts.
Sample Spaces and Events.
Contingincy Tables and Venn Diagrams.
Simple (Marginal) Probability.
Joint Probability.
General Addition Rule.
Conditional Probability.
Computing Conditional Probabilities
Decision Trees
Statistical Independence
Multiplication Rule
Bayes' Theorem.
Counting Rules.
Ethical Issues and Probability.
Summary.
Appendix 3. Using Software for Basic Probability.
5. Some Important Discrete Probability Distributions.
Using Statistics: The Accounting Information System of the Saxon Plumbing Company.
The Probability Distribution for a Discrete Random Variable.
Expected Value of a Discrete Random Variable.
Variance and Standard Deviation of a Discrete Random Variable
Covariance and Its Application in Finance.
The Covariance
The Expected Value, Variance, and Standard Deviation of the Sum of Two Random Variables
Portfolio Expected Return and Portfolio Risk
Binomial Distribution.
Poisson Distribution.
Hypergeometric Distribution.
CD ROM Topic : Using the Poisson Distribution to Approximate the Binomial Distribution.
Summary.
Appendix 5. Using Software for the Covariance and for Discrete Probability Distributions.
6. The Normal Distribution and Other Continuous Distributions.
Using Statistics: Download Time for a Web Site Home Page.
Continuous Probability Distributions.
The Normal Distribution.
Evaluating Normality.
Evaluating the Properties
Constructing the Normal Probability Plot
The Uniform Distribution.
The Exponential Distribution.
The Normal Approximation to the Binomial Distribution.
Need for a Correction for Continuity Adjustment
Approximating the Binomial Distribution
Computing a Probability Approximation for an Individual Value
Summary.
Appendix 6. Using Software with Continuous Probability Distributions.
7. Sampling Distributions.
Using Statistics: The Oxford Cereal Company Packaging Process.
Sampling Distributions.
Sampling Distribution of the Mean.
The Unbiased Property of the Sample Mean
Standard Error of the Mean
Sampling from Normally Distributed Populations
Sampling from Nonnormally Distributed Populations — The Central Limit Theorem
Sampling Distribution of the Proportion.
Types of Survey Sampling Methods
Simple Random Sample
Systematic Sample
Stratified Sample
The Cluster Sample
Evaluating Survey Worthiness.
Survey Errors
Ethical Issues
CD ROM Topic Sampling from Finite Populations.
Summary.
Appendix 7. Using Software for Sampling Distributions.
8. Confidence Interval Estimation.
Using Statistics: Auditing Invoices at the Saxon Home Improvement Company.
Confidence Interval Estimation of the Mean (Ã Known).
Confidence Interval Estimation of the Mean (Ã Unknown).
Student’s t Distribution
The Concept of Degrees of Freedom
The Confidence Interval Statement
Confidence Interval Estimation for the Proportion.
Determining Sample Size.
Sample Size Determination for the Mean
Sample Size Determination for the Proportion
Applications of Confidence Interval Estimation in Auditing.
Estimating the Population Total Amount
Difference Estimation
Confidence Interval Estimation and Ethical Issues.
CD ROM Topic: Estimation and Sample Size Determination for Finite Populations
Summary.
Appendix 8. Using Software for Confidence Interval Estimation.
9. Fundamentals of Hypothesis Testing.
Using Statistics: The Oxford Cereal Company Packaging Process.
Hypothesis-Testing Methodology.
The Null and Alternative Hypotheses
The Critical Value of the Test Statistic
Regions of Rejection and Nonrejection
Risks in Decision Making using Hypothesis Testing Methodology
Z Test of Hypothesis for the Mean (Ã Known).
The Critical Value Approach to Hypothesis Testing
The p-Value Approach to Hypothesis Testing
A Connection between Confidence Interval Estimation and Hypothesis Testing One-Tailed Tests.
One-Tail Tests.
The Critical Value Approach
The p-Value Approach
t Test of Hypothesis for the Mean (Ã Unknown).
Z Test of Hypothesis for the Proportion.
The Power of a Test.
Potential Hypothesis-Testing Pitfalls and Ethical Issues.
Summary.
Appendix 9. Using Software for One-Sample Tests of Hypothesis.
10. Two-Sample Tests.
Comparing The Means of Two Independent Samples.
Z test for the Difference between Two Means
Pooled - Variance t test for the Difference between Two Means
Confidence Interval Estimate for the Difference between the Means of two Independent Groups
Separate - Variance t test for the Difference between Two Means
Comparing the Means of Two Related Populations.
The Paired t Test
Confidence Interval Estimate for the Mean Difference
Comparing Two Population Proportions.
Z Test for the Difference between Two Proportions
Confidence Interval Estimate for the Difference between Two Proportions
F Test for the Difference between Two Variances.
Finding Lower-Tail Critical Values
Summary.
Appendix 10. Using Software for Two-Sample Tests of Hypothesis for Numerical Data.
11. Analysis of Variance.
Using Statistics: The Perfect Parachute Company.
The Completely Randomized Design: One-Way Analysis of Variance.
F Test for Differences in More than Two Means
Multiple Comparisons: The Tukey-Kramer Procedure
ANOVA Assumptions
Levene’s Test for Homogeneity of Variance
The Randomized Block Design.
Tests for the Treatment and Block Effects
Multiple Comparisons: The Tukey Procedure
The Factorial Design: Two-Way Analysis of Variance.
Testing for Factor and Interaction Effects
Interpreting Interaction Effects
Multiple Comparisons: The Tukey Procedure
Summary.
Appendix 11. Using Software for ANOVA.
12. Chi-Square Tests and Nonparametric Tests.
Using Statistics: Guest Satisfaction at T. C. Resort Properties.
Chi-Square Test for Differences between Two Proportions (Independent Samples).
Chi-Square Test for Differences among More than Two Proportions.
Chi-Square Test of Independence .
McNemar Test for the Difference between Two Proportions (Related Samples).
Chi-Square Test for a Variance or Standard Deviation.
Chi-Square Goodness of Fit Tests.
Chi-Square Goodness of Fit Test for the Poisson Distribution
Chi-Square Goodness of Fit Test for the Normal Distribution
Wilcoxon Rank Sum Test: Nonparametric Analysis for Two Independent Populations.
Wilcoxon Signed Ranks Test: Nonparametric Analysis for Two Related Populations.
Kruskal-Wallis Rank Test: Nonparametric Analysis for the One-Way Design.
Friedman Rank Test: Nonparametric Analysis for the Randomized Block Design.
Summary.
Appendix 12. Using Software for Chi-Square Tests and Nonparametric Tests.
13. Simple Linear Regression.
Using Statistics: Forecasting Sales at the Sunflowers Clothing Stores.
Types of Regression Models.
The Least-Squares Method
Visual Explorations: Exploring Simple Linear Regression Coefficients
Predictions in Regression Analysis: Interpolation versus Extrapolation
Measures of Variation.
Computing the Sum of Squares
The Coefficient of Determination
Standard Error of the Estimate
Assumptions.
Residual Analysis.
Evaluating the Assumptions
Measuring Autocorrelation: The Durbin-Watson Statistic.
Residual Plots to Detect Autocorrelation
The Durbin-Watson Statistic
Inferences about the Slope and Correlation Coefficient.
t Test for the Slope
F Test for the Slope
Confidence Interval Estimate for the Slope
t Test for the Correlation Coefficient
Estimation of Predicted Values.
The Confidence Interval Estimate
The Prediction Interval
Pitfalls in Regression and Ethical Issues.
Summary.
Appendix 13. Using Software for Simple Linear Regression.
14. Introduction to Multiple Regression.
Using Statistics: Predicting OmniPower Sales.
Developing the Multiple Regression Model.
Interpreting the Regression Coefficients
Predicting the Dependent Variable Y
R2, Adjusted R2, and the Overall F test 000.
Coefficients of Multiple Determination
Test for the Significance of the overall Multiple Regression Model
Residual Analysis for the Multiple Regression Model.
Inferences Concerning the Population Regression Coefficients.
Test of Hypothesis
Confidence Interval Estimation
Testing Portions of the Multiple Regression Model.
Coefficient of Partial Determination
Using Dummy-Variables and Interaction Terms in Regression Models.
Interactions
Logistic Regression.
Summary.
Appendix 14. Using Software for Multiple Regression.
15. Multiple Regression Model Building .
Using Statistics: Predicting Standby Hours for Unionized Artists.
The Quadratic Regression Model.
Finding the Regression Coefficients and Predicting Y
Testing for the Significance of the Quadratic Effect
Testing the Quadratic Effect.
The Coefficient of Multiple Determination
Using Transformations in Regression Models.
The Square Root Transformation
The Log Transformation
Influence Analysis.
Collinearity.
Model Building .
The Stepwise Regression Approach to Model Building
The Best-Subsets Approach to Model Building
Model Validation
Pitfalls in Multiple Regression and Ethical Issues.
Pitfalls in Multiple Regression
Ethical Issues
Summary.
Appendix 15. Using Software for Multiple Regression Model Building .
16. Time-Series Forecasting and Index Numbers.
Using Statistics: Forecasting Revenues for Three Companies.
The Importance of Business Forecasting.
Component Factors of the Classical Multiplicative Time-Series Model.
Smoothing the Annual Time Series.
Moving Averages
Exponential Smoothing
Least-Squares Trend Fitting and Forecasting.
The Linear Trend Model
The Quadratic Trend Model
The Exponential Trend Model
The Holt-Winters Method for Trend-Fitting and Forecasting.
Autoregressive Modeling for Trend Fitting and Forecasting.
Choosing an Appropriate Forecasting Model.
Performing a Residual Analysis
Measuring the Magnitude of the Residual Error through Squared or Absolute Differences
Principle of Parsimony
Time-Series Forecasting of Monthly or Quarterly Data.
Least-Squares Forecasting with Monthly or Quarterly Data
Index Numbers.
The Price Index
Aggregate Price Indexes
Weighted Aggregate Price Indexes
Paasche Price Index
Some Common Price Indexes
Pitfalls Concerning Time-Series Analysis.
Summary.
Appendix 16. Using Software for Time-Series Forecasting and Index Numbers.
17 Decision Making.
Using Statistics: Selecting Stocks .
Payoff Tables and Decision Trees.
Criteria for Decision Making.
Expected Monetary Value
Expected Opportunity Loss
Return-to-Risk Ratio
Decision Making with Sample Information.
Utility.
Summary.
Appendix 17. Using Software for Decision Making.
18 Statistical Applications in Quality and Productivity Management.
Total Quality Management.
Six Sigma® Management.
The Theory of Control Charts.
Control Chart for the Proportion of Nonconforming Items–The p Chart.
The Red Bead Experiment: Understanding Process Variability.
Control Chart for an Area of Opportunity – the c Chart.
Control Charts for the Range and the Mean.
The R Chart: A Control Chart for Dispersion
The Chart
Process Capability.
Customer Satisfaction and Specification Limits
Capability Indices
CPL. CPU, Cpk
Summary.
Appendix 18. Using Software for Control Charts.
Answers to Self-Test Problems.
Answers to Even-Numbered Problems.
Appendices.
A. Review of Arithmetic and Algebra.
B. Summation Notation.
C. Statistical Symbols and Greek Alphabet.
D. CD-ROM Contents.
E. Tables.
F. Configuring and Customizing Microsoft Excel For Use With This Text.
G. PHStat2 User’s Guide.
Index.
CD-ROM Topics.
