Statistics for Business and Economics – James T. McClave, P. George Benson, Terry Sincich – 10th Edition

Description

This best-selling 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 are covered extensively, as needed, to evaluate the reported results of statistical studies and to make good decisions. Numerous case studies, examples, and exercises draw on real business situations and recent events. Assumes a background in basic algebra.

The text incorporates the following American Statistical Association (ASA) guidelines developed from both the Making Statistics More Effective in Schools of Business (MSMESB) conferences and Guidelines for Assessment and Instruction in Statistics Education (GAISE) Project:

  • Students are most effectively motivated by seeing statistics at work in real applications, problems, cases, and projects.
  • Students should be provided with the opportunity to work with real data and make use of technology for statistical computations.
  • 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.
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  • 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 Hypothesi
    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 β (Optional)
    6.7 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 Dsign
    8.3 Multiple Comparisons of Mean
    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 σ2
    10.5 Making Inferences About the Slope β1
    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 β-Parameters
    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 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 χ2
    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 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
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