Basic Business Statistics: Concepts and Applications

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.