Statistics for Business and Economics, 7/e
James T. McClave, University of Florida
P. George Benson, Rutgers University
Terry Sincich, University of South Florida
Published November, 1997 by Prentice Hall Engineering/Science/Mathematics
Copyright 1998, 1067 pp.
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Introduction to Business Statistics (Two Semester)-Decision Science
Introduction to Business Statistics-Statistics
For a one/two-term business statistics course. Designed
for students with a background in basic algebra, this best-selling
introduction to statistics for business and economics emphasizes inference
with extensive coverage of data collection and analysis as needed
to evaluate the reported results of statistical studies and make good
business decisions. It stresses the development of statistical thinking,
the assessment of credibility and value of the inferences made from
data both by those who consume and those who produce them
and features numerous case studies, examples, and exercises
all drawing on real business situations and recent economic events.
NEWIntegrates computer output throughout:
NEWChapter 1 emphasizes the
science of statistics and its role in business decisions.
Includes revised/expanded/new sections on:
- In selected examples and in exercise sets
to give students practice in interpretation with the output
of statistical software packages such as SPSS, Minitab, SAS and the
spreadsheet package, EXCEL.
NEWChapter 2 expands coverage
of descriptive analytical tools that are useful in examining
assumptions about data. Includes revised/expanded/new sections on:
- Process and quality control.
- Data collection from published sources, designed experiments,
surveys, and observations.
- Statistical thinking.
NEWChapter 7 provides more emphasis
on confidence interval estimation procedures and their interpretation.
- Both graphical and numerical methods for summarizing
- Time series plots and the graphing of bivariate data
NEWChapter 8 features more balanced
treatment of p-values and critical values in interpreting testing
- The approach to small-sample confidence intervals is
motivated by pharmaceutical testing requirements.
- Adds new optional sections on the finite population correction
and survey designs.
NEWChapter 9 reorganized to present
inference in the context of the experimental design used for data
- New examples make greater use of computer solutions rather
NEWChapter 10 includes expanded
coverage of correlation which continues the emphasis,
from Ch. 2, on bivariate linear relationships.
NEWChapter 11 incorporates more
extensive computer output and inferences about the beta parameters
and includes new material on confidence intervals.
- Where appropriate, shifts the emphasis from formulas
to the interpretation of computer output, including Excel spreadsheets.
NEWChapter 12 offers new/revised
coverage of model-building:
NEWChapter 15 contains a separate
section on multiple comparisons with emphasis on computer
output rather than formulas.
- The consequences of choosing the wrong model i.e.,
the errors in prediction.
- Models with more than two quantitative variables.
NEWFeatures Statistics in Action boxes
two or three per chapter that examine high profile, contemporary,
controversial business, economic, government, or entertainment issues
that involve statistics.
- Presents examples of both Bonferroni's and Tukey's method.
NEWFeatures six extensive business problem-solving
cases Show Cases with real data and assignments.
- Focus questions prompt students to form their own
conclusions and to think through the statistical issues involved.
NEWContains six Internet Labs designed
to help students retrieve and download raw data from the internet
NEWContains 60% new or revised examples
and exercises featuring real business data from 1990 to
NEWAdds a Chapter-end Review to each
Offers a choice in level of coverage of probability.
- Quick Review provides a list of key terms
and formulas with page number references.
- Language Lab helps students learn the language
of statistics through pronunciation guides, descriptions of symbols,
- Supplementary Exercises review all of the
important topics covered in the chapter.
Features extensive coverage of regression analysis
with three chapters covering simple regression, multiple regression,
and model building.
- Unlike other introductory texts which mix probability
and counting rules, this text includes the counting rules in a separate
and optional section at the end of the chapter on probability.
- All exercises that require the use of counting rules
are marked with an asterisk.
Provides an abundance of exercises (1400) labeled
by type and illustrating applications in almost all areas of research.
All exercise sections are divided into two parts:
- Content is understandable, usable, and much more comprehensive
than the presentations in other introductory statistics texts.
- Discusses the major types of inferences that can be derived
from a regression analysis showing how these results appear
in computer printouts and selecting multiple regression models to
be used in an analysis.
Highlights important information in colored boxes
Definitions, Strategies, Key Formulas and other important information.
- Learning the Mechanics straightforward applications
of new concepts to test students' ability to comprehend a concept
or a definition.
- Applying the Concepts based on applications
and real data taken from a wide variety of journals, newspapers, magazines,
and other sources published since 1990. These exercises develop students'
skills at comprehending real world problems that describe situations
to which the techniques may be applied.
Provides We're We've Been . . . We're Going
chapter openers with quick reviews of how information learned
previously applies to the chapter at hand and how the chapter topics
fit into students' growing understanding of statistical inference.
Includes Footnotes that allow additional flexibility
in the mathematical and theoretical level at which the material is
- Explain the role of calculus in various derivations.
- Cover some of the theory underlying certain results.
Each chapter concludes with a Quick Review.
1. Statistics, Data, and Statistical Thinking.
The Science of Statistics. Types of Statistical Applications
in Business. Fundamental Elements of Statistics. Processes (Optional).
Types of Data. STATISTICS IN ACTION: Quality Improvement: U.S. Firms
Respond to the Challenge from Japan. Collecting Data. The Role of
Statistics in Managerial Decision-Making. STATISTICS IN ACTION: A
20/20 View of Survey Results: Fact or Fiction.
2. Methods for Describing Sets of Data.
Describing Qualitative Data. STATISTICS IN ACTION: Pareto
Analysis. Graphical Methods for Describing Quantitative Data. The
Time Series Plot (Optional). Summation Notation. Numerical Measures
of Central Tendency. Numerical Measures of Variability. Interpreting
the Standard Deviation. Numerical Measures of Relative Standing. Quartiles
and Box Plots (Optional). Graphing Bivariate Relationships (Optional).
Distorting the Truth with Descriptive Techniques. STATISTICS IN ACTION:
Car and Driver's Road Test Digest. Quick Review.
SHOWCASE: The Kentucky Milk Case Part I. INTERNET LAB: Accessing
and Summarizing Business and Economics Data Maintained by the U.S.
Events, Sample Spaces, and Probability. STATISTICS IN ACTION:
Game Show Strategy: To Switch or Not to Switch. Unions and Intersections.
Complementary Events. The Additive Rule and Mutually Exclusive Events.
Conditional Probability. The Multiplicative Rule and Independent Events.
Random Sampling. STATISTICS IN ACTION: Lottery Buster.
4. Discrete Random Variables.
Two Types of Random Variables. Probability Distributions
for Discrete Random Variables. Expected Values of Discrete Random
Variables. STATISTICS IN ACTION: Portfolio Selection. The Space Shuttle
Challenger: Catastrophe in Space. The Binomial Random Variable.
The Poisson Random Variable (Optional).
5. Continuous Random Variables.
Continuous Probability Distributions. The Uniform Distribution
(Optional). The Normal Distribution. STATISTICS IN ACTION: IQ, Economic
Mobility , and the Bell Curve Approximating a Binomial Distribution
with a Normal Distribution. The Exponential Distribution (Optional).
STATISTICS IN ACTION: Queuing Theory.
6. Sampling Distributions.
The Concept of Sampling Distributions. Properties of Sampling
Distributions: Unbiased and Minimum Variance. STATISTICS IN ACTION:
Reducing Investment Risk Through Diversification. The Sampling Distribution
of the Sample Mean. STATISTICS IN ACTION: The Insomnia Pill. Quick
Review. SHOWCASE: The Furniture Fire Case. INTERNET LAB: Analyzing
Monthly Business Start-ups.
7. Inferences Based on a Single Sample: Estimation with
Large-Sample Confidence Interval for a Population Mean.
Small-Sample Confidence Interval for a Population Mean. STATISTICS
IN ACTION: Scallops, Sampling, and the Law. Large-Sample Confidence
Interval for a Population Proportion. Determining the Sample Size.
STATISTICS IN ACTION: Is Caffeine Addictive? Finite Population Correction
for Simple Random Sampling (Optional). Sample Survey Designs. STATISTICS
IN ACTION: Sampling Error versus Nonsampling Error.
8. Inferences Based on a Single Sample: Tests of Hypothesis.
The Elements of a Test of Hypothesis. STATISTICS IN ACTION:
Statistics Is Murder! Large-Sample Test of Hypothesis About a Population
Mean. STATISTICS IN ACTION: Statistical Quality Control, Part I. Observed
Significance Levels: p-values. Small Sample Test of Hypothesis
About a Population Mean. Large-Sample Test of Hypothesis About a Population
Proportion. STATISTICS IN ACTION: Statistical Quality Control, Part
II. Calculating Type II Error Probabilities. More about b (Optional).
9. Inferences Based an Two Samples: Confidence Intervals
and Tests of Hypotheses.
Comparing Two Population Means: Independent Sampling. STATISTICS
IN ACTION: The Effect of Self-Managed Work Teams on Family Life. Comparing
Two Population Means: Paired Difference Experiments. Comparing Two
Population Proportions: Independent Sampling. Determining the Sample
Size for Comparing Means and Proportions. STATISTICS IN ACTION: Unpaid
Overtime and the Fair Labor Standards Act. Comparing Two Population
Variances: Independent Sampling... SHOWCASE: The Kentucky Milk Case
Part II. INTERNET LAB: Choosing Between Economic Indicators.
10. Simple Linear Regression.
Probabilistic Models. Fitting the Model: The Least Squares
Approach. Model Assumptions. An Estimator of o^2. Assessing
the Utility of the Model: Making Inferences about the Slope b1.
The Coefficient of Correlation. STATISTICS IN ACTION: New Jersey Banks
Serving Minorities? The Coefficient of Determination. Using
the Model for Estimation and Prediction. STATISTICS IN ACTION: Statistical
Assessment of Damage to Bronx Bricks. Simple Linear Regression: An
11. Multiple Regression.
Multiple Regression: The Model and the Procedure. Fitting
the Model: The Least Squares Approach. Model Assumptions. Inferences
About the b Parameters. Checking the Usefulness of a Model:
R^2 and the Analysis of Variance F-Test. Using the
Model for Estimation and Prediction. Multiple Regression: An Example.
Residual Analysis: Checking the Regression Assumptions. STATISTICS
IN ACTION: Predicting the Price of Vintage Red Bordeaux Wine. Some
Pitfalls: Estimability, Multicollinearity, and Extrapolation. STATISTICS
IN ACTION: Wringing The Bell Curve.
12. Model Building.
Introduction. The Two Types of Independent Variables: Quantitative
and Qualitative. Models with a Single Quantitative Independent Variable.
Models with Two or More Quantitative Independent Variables. Testing
Portions of a Model. Models with One Qualitative Independent Variable.
Comparing the Slopes of Two or More Lines. Comparing Two or More Response
Curves. STATISTICS IN ACTION: Forecasting Peak Hour Traffic Volume.
Stepwise Regression. Quick Review. SHOWCASE: The Cando Sales Case.
INTERNET LAB: Using the Consumer Price Index in Business Forecasts
of Labor, Wages, and Compensation.
13. Methods for Quality Improvement.
Quality, Processes, and Systems. STATISTICS IN ACTION: Deming's
14 Points. Statistical Control. The Logic of Control Charts. A Control
Chart for Monitoring the Mean of a Process: The x-Chart. A
Control Chart for Monitoring the Variation of a Process: The R-Chart.
A Control Chart for Monitoring the Proportion of Defectives Generated
by a Process: The p-Chart. Diagnosing the Causes of Variation
(Optional). STATISTICS IN ACTION: Quality Control in a Service Operation.
14. Time Series: Descriptive Analyses, Models, and Forecasting.
Descriptive Analysis: Index Numbers. STATISTICS IN ACTION:
The Consumer Price Index: CPI-U and CPI-W. Descriptive Analysis: Exponential
Smoothing. Time Series Components. Forecasting: Exponential Smoothing.
Forecasting Trends: The Holt-Winters Model (Optional). Measuring Forecast
Accuracy: MAD and RMSE. Forecasting Trends: Simple Linear Regression.
STATISTICS IN ACTION: Forecasting the Demand for Emergency Room Services.
Seasonal Regression Models. Autocorrelation and the Durbin-Watston
test. Quick Review. SHOWCASE: The Gasket Manufacturing Case. INTERNET
LAB: Quality Management Outside of the Manufacturing Operation.
15. Design of Experiments and Analysis of Variance.
Elements of a Designed Experiment. The Completely Randomized
Design: Single Factor. Multiple Comparisons of Means. STATISTICS IN
ACTION: Is Therapy the New Diet Pill for Binge Eaters? Factorial
Experiments. STATISTICS IN ACTION: Improving a Ground Meat Canning
Process Through Experimental Design. Using Regression for ANOVA (Optional).
16. Nonparametric Statistics.
Introduction. Single Population Inferences: The Sign Test.
Comparing Two Populations: The Wilcoxon Rank Sum Test for Independent
Samples. Comparing Two Populations: The Wilcoxon Signed Rank Test
for the Paired Difference Experiment. STATISTICS IN ACTION: Reanalyzing
the Scallop Weight Data. The Kruskal-Wallis H-Test for a Completely
Randomized Design. STATISTICS IN ACTION: Taxpayers versus the IRS:
Selecting the Trial Court. Spearman's Rank Correlation Coefficient.
17. The Chi-Square Test and the Analysis of Contingency
One-Dimensional Count Data: The Multinomial Distribution.
Contingency Tables. STATISTICS IN ACTION: Ethics in Computer Technology
and Use. A Word of Caution About Chi-Square Tests. SHOWCASE: Discrimination
in the Workplace. INTERNET LAB: Sampling and Analyzing NYSE Stock
18. Decision Analysis.
Introduction. Three Types of Decision Problems. Decision-Making
Under Uncertainty. Basic Concepts. Two Ways of Expressing Outcomes:
Payoffs and Opportunity Losses. Characterizing the Uncertainty in
Decision-Problems. Solving the Decision Problem Using the Expected
Payoff Criterion. STATISTICS IN ACTION: Evaluating Uncertainty in
Research and Development. The Expected Utility Criterion. Classifying
Decision-Makers by Their Utility Functions. Revising State of Nature
Probabilities: Bayes' Rule. Solving Decision Problems Using Posterior
Probalilitics. The Expected Value of Sample Information: Preposterior
Analysis. STATISTICS IN ACTION: Hurricanes: To Seed or Not to Seed?
Appendix A: Basic Counting Rules.
Appendix B: Tables.
Appendix C: Calculation Formulas for Analysis of Variance.
Answers to Selected Exercises.