Statistics for Business & Economics

James T. McClave, Info Tech, Inc./ University of Florida P. Goerge Benson, Terry College of Business, University of Georgia Terry Sincich, University of South Florida

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 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 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 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