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More copies of this ISBNThis title in other editionsStatistics for Business & Economicsby James T Mcclave
Synopses & ReviewsPublisher Comments:Classic, yet contemporary. Theoretical, yet applied. Statistics for Business and Economics, Eleventh Edition, gives you the best of both worlds. Using a rich array of applications from a variety of industries, McClave/Sincich/Benson clearly demonstrates how to use statistics effectively in a business environment. The book focuses on developing statistical thinking so the reader can better assess the credibility and value of inferences made from data. As consumers and future producers of statistical inferences, readers are introduced to a wide variety of data collection and analysis techniques to help them evaluate data and make informed business decisions. As with previous editions, this revision offers an abundance of applications with many new and updated exercises that draw on real business situations and recent economic events. The authors assume a background of basic algebra. Synopsis:Never HIGHLIGHT a Book Again! Virtually all testable terms, concepts, persons, places, and events are included. Cram101 Textbook Outlines gives all of the outlines, highlights, notes for your textbook with optional online practice tests. Only Cram101 Outlines are Textbook Specific. Cram101 is NOT the Textbook.
Synopsis:* Formal training in probability needs to be downplayed in favor of intuitive concepts of probability. * We need to reduce our emphasis on formal theory of statistics and increase emphasis on applications.
Synopsis:This bestselling introduction stresses the development of statistical thinking – the assessment of credibility and value of the inferences made from data – by both those who consume and those who produce the information. The authors emphasize inference; data collection and analysis are covered extensively, as needed, to evaluate the reported results of statistical studies and to make good business decisions. Numerous case studies, examples, and exercises draw on real business situations and recent economic events. Assumes a background in basic algebra.
About the AuthorJames T. McClave, Info Tech, Inc./ University of Florida P. Goerge Benson, Terry College of Business, University of Georgia Terry Sincich, University of South Florida Table of ContentsChapter 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 DecisionMaking 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 34)
Chapter 5 Inferences Based on a Single Sample: Estimation with Confidence Intervals 5.1 Identifying the Target Parameter 5.2 LargeSample Confidence Interval for a Population Mean 5.3 SmallSample Confidence Interval for a Population Mean 5.4 LargeSample 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 LargeSample Test of Hypothesis About a Population Mean
6.3 Observed Significance Levels: pValues 6.4 SmallSample Test of Hypothesis About a Population Mean 6.5 LargeSample Test of Hypothesis About a Population Proportion 6.6 Calculating Type II Error Probabilities: More About β (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 SelfManaged Work Teams on Family Life Using Technology: TwoSample Inferences using SPSS, MINITAB and EXCEL/PHStat2
APPLYING STATISTICS TO THE REAL WORLD: THE KENTUCKY MILK CASE C PART II (A Case Covering Chapters 79)
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 ChiSquare Test and the Analysis of Contingency Tables 9.1 Categorical Data and the Multinomial Distribution 9.2 Testing Category Probabilities: OneWay Table 9.3 Testing Category Probabilities: TwoWay (Contingency) Table 9.4 A Word of Caution About ChiSquare Tests Statistics in Action: A Study of Coupon Users — Mail versus the Internet Using Technology: ChiSquare Analyses using SPSS, MINITAB and EXCEL/PHStat2 APPLYING STATISTICS TO THE REAL WORLD: DISCRIMINATION IN THE WORKPLACE (A Case Covering Chapters 89)
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 σ^{2} 10.5 Making Inferences About the Slope β_{1} 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 WorkLife 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 FirstOrder Model: Estimating and Interpreting the βParameters 11.3 Inferences About the Individual β 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 HigherOrder 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: BidRigging 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 1011)
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 RChart 12.6 A Control Chart for Monitoring the Proportion of Defectives Generated by a Process: The pChart 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 HoltWinters 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 DurbinWatson 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 1213)
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 χ^{2} Table VII Percentage Points of the F Distribution, α=.10 Table VIII Percentage Points of the F Distribution, α=.05 Table IX Percentage Points of the F Distribution, α=.025 Table X Percentage Points of the F Distribution, α=.01 Table XI Critical Values of T_{L} and T_{U} for the Wilcoxon Rank Sum Test: Independent Samples Table XII Critical Values of T_{0} 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
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