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Statistics for business and economics

Author: James T McClave; P George Benson; Terry Sincich
Publisher: Upper Saddle, NJ : Pearson Prentice Hall, ©2008.
Edition/Format:   Book : English : 10th edView all editions and formats
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Additional Physical Format: Online version:
McClave, James T.
Statistics for business and economics.
Upper Saddle, NJ : Pearson Prentice Hall, c2008
(OCoLC)608113857
Document Type: Book
All Authors / Contributors: James T McClave; P George Benson; Terry Sincich
ISBN: 0132409364 9780132409360 0132409356 9780132409353 9780132069731 0132069733 0132409569 9780132409568
OCLC Number: 77520511
Notes: Includes index.
Description: xviii, 904, [16] p. : ill. (chiefly col.) ; 29 cm. + 1 CD-ROM (4 3/4 in.)
Details: System requirements: Pentium II 300 MHz processor or PowerPC G3 233 MHz; 64 MB RAM, 4.3-10 MB available hard drive space; Windows NT/2000/ME/XP or Mac OS 9.x/10.x; QuickTime 6.x; 800 x 600 resolution, 8x or faster CD-ROM drive, sound card.
Contents: Chapter 1 Statistics, Data, and Statistical Thinking 1.1 The Science of Statistics 1.2 Types of Statistical Applications 1.3 Fundamental Elements of Statistics 1,4 Processes (Optional) 1.5 Types of Data 1.6 Collecting Data 1.7 The Role of Statistics in Managerial Decision-Making Statistics in Action: A "20/20" View of Survey Results - Fact or Fiction? Using Technology: Creating and Listing Data in SPSS, MINITAB, and EXCEL Chapter 2 Methods for Describing Sets of Data 2.1 Describing Qualitative Data 2.2 Graphical Methods for Describing Quantitative Data 2.3 Summation Notation 2.4 Numerical Measures of Central Tendency 2.5 Numerical Measures of Variability 2.6 Interpreting the Standard Deviation 2.7 Numerical Measures of Relative Standing 2.8 Methods for Detecting Outliers (Optional) 2.9 Graphing Bivariate Relationships (Optional) 2.10 The Time Series Plot (Optional) 2.11 Distorting the Truth with Descriptive Techniques Statistics In Action: Characteristics of Physicians who Use or Refuse Ethics Consultation Using Technology: Describing Data using SPSS, MINITAB, and EXCEL/PHStat2 APPLYING STATISTICS TO THE REAL WORLD: THE KENTUCKY MILK CASE C PART I (A Case Covering Chapters 1 and 2) Chapter 3 Probability 3.1 Events, Sample Spaces, and Probability 3.2 Unions and Intersections 3.3 Complementary Events 3.4 The Additive Rule and Mutually Exclusive Events 3.5 Conditional Probability 3.6 The Multiplicative Rule and Independent Events 3.7 Random Sampling 3.8 Bayes' Rule (Optional) Statistics In Action: Lottery Buster! Using Technology: Generating a Random Sample Using SPSS, MINITAB, and EXCEL/PHStat2 Chapter 4 Random Variables and Probability Distributions 4.1 Two Types of Random Variables 4.2 Probability Distributions for Discrete Random Variables 4.3 The Binomial Random Variable 4.4 The Poisson Random Variable (Optional) 4.5 Probability Distributions for Continuous Random Variables 4.6 The Uniform Distribution (Optional) 4.7 The Normal Distribution 4.8 Descriptive Methods for Assessing Normality 4.9 Approximating a Binomial Distribution with a Normal Distribution (Optional) 4.10 Sampling Distributions 4.11 The Sampling Distribution of and the Central Limit Theorem Statistics in Action: Super Weapons Development -- Optimizing the Hit Ratio Using Technology: Binomial Probabilities, Normal Probabilities, and Simulated Sampling Distributions using SPSS, MINITAB, and EXCEL/PHStat2 APPLYING STATISTICS TO THE REAL WORLD: THE FURNITURE FIRE CASE (A Case Covering Chapters 3-4) Chapter 5 Inferences Based on a Single Sample: Estimation with Confidence Intervals 5.1 Identifying the Target Parameter 5.2 Large-Sample Confidence Interval for a Population Mean 5.3 Small-Sample Confidence Interval for a Population Mean 5.4 Large-Sample Confidence Interval for a Population Proportion 5.5 Determining the Sample Size 5.6 Finite Population Correction for Simple Random Sampling (Optional) 5.7 Sample survey Designs (Optional) Statistics in Action: Scallops, Sampling, and the Law Using Technology: Confidence Intervals using SPSS, MINITAB and EXCEL/PHStat2 Chapter 6 Inferences Based on a Single Sample: Tests of Hypothesis 6.1 The Elements of a Test of Hypothesis 6.2 Large-Sample Test of Hypothesis About a Population Mean 6.3 Observed Significance Levels: p-Values 6.4 Small-Sample Test of Hypothesis About a Population Mean 6.5 Large-Sample Test of Hypothesis About a Population Proportion 6.6 Calculating Type II Error Probabilities: More About beta (Optional) 8.7 Test of Hypothesis About a Population Variance (Optional) Statistics in Action: Diary of a Kleenex User Using Technology: Tests of Hypotheses using SPSS, MINITAB and EXCEL/PHStat2 Chapter 7 Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses 7.1 Identifying the Target Parameter 7.2 Comparing Two Population Means: Independent Sampling 7.3 Comparing Two Population Means: Paired Difference Experiments 7.4 Comparing Two Population Proportions: Independent Sampling 7.5 Determining the Sample Size 7.6 Comparing Two Population Variances: Independent Sampling Statistics in Action: The Effect of Self-Managed Work Teams on Family Life Using Technology: Two-Sample Inferences using SPSS, MINITAB and EXCEL/PHStat2 APPLYING STATISTICS TO THE REAL WORLD: THE KENTUCKY MILK CASE C PART II (A Case Covering Chapters 7-9) Chapter 8 Analysis of Variance: Comparing More the Two Means 8.1 Elements of a Designed Experiment 8.2 The Completely Randomized Design 8.3 Multiple Comparisons of Means 8.4 The Randomized Block Design (Optional) 8.5 Factorial Experiments Statistics in Action: The Ethics of Downsizing Using Technology: Analysis of Variance using SPSS, MINITAB and EXCEL/PHStat2 Chapter 9 The Chi-Square Test and the Analysis of Contingency Tables 9.1 Categorical Data and the Multinomial Distribution 9.2 Testing Category Probabilities: One-Way Table 9.3 Testing Category Probabilities: Two-Way (Contingency) Table 9.4 A Word of Caution About Chi-Square Tests Statistics in Action: A Study of Coupon Users -- Mail versus the Internet Using Technology: Chi-Square Analyses using SPSS, MINITAB and EXCEL/PHStat2 APPLYING STATISTICS TO THE REAL WORLD: DISCRIMINATION IN THE WORKPLACE (A Case Covering Chapters 8-9) Chapter 10 Simple Linear Regression 10.1 Probabilistic Models 10.2 Fitting the Model: The Least Squares Approach 10.3 Model Assumptions 10.4 An Estimator of sigma2 10.5 Making Inferences About the Slope beta1 10.6 The Coefficient of Correlation 10.7 The Coefficient of Determination 10.8 Using the Model for Estimation and Prediction 10.9 A Complete Example Statistics in Action: An MBA's Work-Life Balance Using Technology: Simple Linear Regression using SPSS, MINITAB and EXCEL/PHStat2 Chapter 11 Multiple Regression and ModelBuilding 11.1 Multiple Regression Models 11.2 The First-Order Model: Estimating and Interpreting the beta-Parameters 11.3 Inferences About the Individual beta Parameters and the Overall Model Utility 11.4 Using the Model for Estimation and Prediction 11.5 Model Building: Interaction Models 11.6 Model Building: Quadratic and other Higher-Order Models 11.7 Model Building: Qualitative (Dummy) Variable Models 11.8 Model Building: Models with both Quantitative and Qualitative Variables (Optional) 11.9 Model Building: Comparing Nested Models (Optional) 11.10 Model Building: Stepwise Regression (Optional) 11.11 Residual Analysis: Checking the Regression Assumptions 11.12 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation Statistics in Action: Bid-Rigging in the Highway construction Industry Using Technology: Multiple Regression using SPSS, MINITAB and EXCEL/PHStat2 APPLYING STATISTICS TO THE REAL WORLD: THE CONDO SALES CASE (A Case Covering Chapters 10-11) Chapter 12 Methods for Quality Improvement 12.1 Quality, Processes, and Systems 12.2 Statistical Control 12.3 The Logic of Control Charts 12.4 A Control Chart for Monitoring the Mean of a Process: The -Chart 12.5 A Control Chart for Monitoring the Variation of a Process: The R-Chart 12.6 A Control Chart for Monitoring the Proportion of Defectives Generated by a Process: The p-Chart 12.7 Diagnosing the Causes of Variation (Optional) 12.8 Capability Analysis (Optional) Statistics in Action: Testing Jet Fuel Additive for Safety Using Technology: Control Charts using SPSS, MINITAB and EXCEL/PHStat2 Chapter 13 Time Series: Descriptive Analyses, Models, and Forecasting (Available on CD) 13.1 Descriptive Analysis: Index Numbers 13.2 Descriptive Analysis: Exponential Smoothing 13.3 Time Series Components 13.4 Forecasting: Exponential Smoothing 13.5 Forecasting Trends: The Holt-Winters Model (Optional) 13.6 Measuring Forecast Accuracy: MAD and RMSE 13.7 Forecasting Trends: Simple Linear Regression 13.8 Seasonal Regression Models 13.9 Autocorrelation and the Durbin-Watson Test Statistics In Action: Forecasting the Monthly Sales of a New Cold Medicine Using Technology: Forecasting using SPSS, MINITAB and EXCEL/PHStat2 APPLYING STATISTICS TO THE REAL WORLD: THE GASKET MANUFACTURING CASE (A Case Covering Chapters 12-13) Chapter 14 Nonparametric Statistics (available on CD) 14.1 Single Population Inferences 14.2 Comparing Two Populations: Independent Samples 14.3 Comparing Two Populations: Paired Difference Experiment 14.4 Comparing Three or More Populations: Completely Randomized Design 14.5 Comparing Three or More Populations: Randomized Block Design (Optional) 14.6 Rank Correlation Statistics in Action: Deadly Exposure -- Agent Orange and Vietnam Vets Using Technology: Nonparametric Analyses using SPSS, MINITAB and EXCEL/PHStat2 Appendix A Basic Counting Rules Appendix B Tables Table I Random Numbers Table II Binomial Probabilities Table III Poisson Probabilities Table IV Normal Curve Areas Table V Critical Values of t Table VI Critical Values of chi2 Table VII Percentage Points of the F Distribution, alpha=.10 Table VIII Percentage Points of the F Distribution, alpha=.05 Table IX Percentage Points of the F Distribution, alpha=.025 Table X Percentage Points of the F Distribution, alpha=.01 Table XI Critical Values of TL and TU for the Wilcoxon Rank Sum Test: Independent Samples Table XII Critical Values of T0 in the Wilcoxon Paired Difference Signed Rank Test Table XIII Critical Values of Spearman's Rank Correlation Coefficient Appendix C Calculation Formulas for Analysis of Variance
Responsibility: James T. McClave, P. George Benson, Terry Sincich.

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