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Statistics for Business and Economics

by
Edition:
9th
ISBN13:

9780324200829

ISBN10:
032420082X
Format:
Hardcover
Pub. Date:
1/6/2004
Publisher(s):
Cengage Learning

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Summary

This market leading book offers a proven, comprehensive, applications-oriented approach. Written by authors who are highly regarded in the field, the text provides sound methodological development. The discussion and development of each technique is presented in an application setting, with the statistical results providing insights to decisions and solutions to problems.

Table of Contents

Preface xxi
About the Authors xxvii
Chapter 1 Data and Statistics 1(22)
Statistics in Practice: Business Week
2(1)
1.1 Applications in Business and Economics
3(2)
Accounting
3(1)
Finance
3(1)
Marketing
4(1)
Production
4(1)
Economics
4(1)
1.2 Data
5(3)
Elements, Variables, and Observations
5(1)
Scales of Measurement
6(1)
Qualitative and Quantitative Data
7(1)
Cross-Sectional and Time Series Data
7(1)
1.3 Data Sources 8 Existing Sources
8(4)
Statistical Studies
9(3)
Data Acquisition Errors
12(1)
1.4 Descriptive Statistics
12(2)
1.5 Statistical Inference
14(2)
1.6 Computers and Statistical Analysis
16(1)
Summary
16(1)
Glossary
16(1)
Exercises
17(6)
Chapter 2 Descriptive Statistics: Tabular and Graphical Presentations 23(53)
Statistics in Practice: Colgate-Palmolive Company
24(1)
2.1 Summarizing Qualitative Data
25(6)
Frequency Distribution
25(1)
Relative Frequency and Percent Frequency Distributions
26(1)
Bar Graphs and Pie Charts
26(5)
2.2 Summarizing Quantitative Data
31(9)
Frequency Distribution
31(1)
Relative Frequency and Percent Frequency Distributions
32(1)
Dot Plot
32(1)
Histogram
33(1)
Cumulative Distributions
34(2)
Ogive
36(4)
2.3 Exploratory Data Analysis: The Stein-and-Leaf Display
40(5)
2.4 Crosstabulations and Scatter Diagrams
45(9)
Crosstabulation
45(2)
Simpson's Paradox
47(2)
Scatter Diagram and Trendline
49(5)
Summary
54(2)
Glossary
56(1)
Key Formulas
57(1)
Supplementary Exercises
57(6)
Case Problem Pelican Stores
63(1)
Appendix 2.1 Using Minitab for Tabular and Graphical Presentations
64(2)
Appendix 2.2 Using Excel for Tabular and Graphical Presentations
66(10)
Chapter 3 Descriptive Statistics: Numerical Measures 76(63)
Statistics in Practice: Small Fry Design
77(1)
3.1 Measures of Location
78(9)
Mean
78(1)
Median
79(1)
Mode
80(1)
Percentiles
81(1)
Quartiles
82(5)
3.2 Measures of Variability
87(7)
Range
87(1)
Interquartile Range
88(1)
Variance
88(2)
Standard Deviation
90(1)
Coefficient of Variation
91(3)
3.3 Measures of Distribution Shape, Relative Location, and Detecting
Outliers
94(1)
Distribution Shape
94(1)
z-Scores
94(2)
Chebyshev's Theorem
96(1)
Empirical Rule
97(1)
Detecting Outliers
98(3)
3.4 Exploratory Data Analysis
101(4)
Five-Number Summary
101(1)
Box Plot
102(3)
3.5 Measures of Association Between Two Variables
105(9)
Covariance
106(1)
Interpretation of the Covariance
107(3)
Correlation Coefficient
110(1)
Interpretation of the Correlation Coefficient
111(3)
3.6 The Weighted Mean and Working with Grouped Data
114(6)
Weighted Mean
115(1)
Grouped Data
116(4)
Summary
120(1)
Glossary
121(1)
Key Formulas
122(2)
Supplementary Exercises
124(5)
Case Problem 1 Pelican Stores
129(1)
Case Problem 2 National Health Care Association
130(1)
Case Problem 3 Business Schools of Asia-Pacific
131(2)
Appendix 3.1 Descriptive Statistics Using Minitab
133(2)
Appendix 3.2 Descriptive Statistics Using Excel
135(4)
Chapter 4 Introduction to Probability 139(45)
Statistics in Practice: Morton International
140(1)
4.1 Experiments, Counting Rules, and Assigning Probabilities
141(10)
Counting Rules, Combinations, and Permutations
142(4)
Assigning Probabilities
146(2)
Probabilities for the KP&L Project
148(3)
4.2 Events and Their Probabilities
151(4)
4.3 Some Basic Relationships of Probability
155(6)
Complement of an Event
155(1)
Addition Law
156(5)
4.4 Conditional Probability
161(8)
Independent Events
165(1)
Multiplication Law
165(4)
4.5 Bayes' Theorem
169(6)
Tabular Approach
173(2)
Summary
175(1)
Glossary
175(1)
Key Formulas
176(1)
Supplementary Exercises
177(4)
Case Problem Hamilton County Judges
181(3)
Chapter 5 Discrete Probability Distributions 184(39)
Statistics in Practice: Citibank
185(1)
5.1 Random Variables
185(3)
Discrete Random Variables
186(1)
Continuous Random Variables
187(1)
5.2 Discrete Probability Distributions
188(6)
5.3 Expected Value and Variance
194(4)
Expected Value
194(1)
Variance
194(4)
5.4 Binomial Probability Distribution
198(10)
A Binomial Experiment
199(1)
Martin Clothing Store Problem
200(4)
Using Tables of Binomial Probabilities
204(1)
Expected Value and Variance for the Binomial Distribution
205(3)
5.5 Poisson Probability Distribution
208(4)
An Example Involving Time Intervals
209(2)
An Example Involving Length or Distance Intervals
211(1)
5.6 Hypergeometric Probability Distribution
212(3)
Summary
215(1)
Glossary
216(1)
Key Formulas
217(1)
Supplementary Exercises
218(2)
Appendix 5.1 Discrete Probability Distributions with Minitab
220(1)
Appendix 5.2 Discrete Probability Distributions with Excel
221(2)
Chapter 6 Continuous Probability Distributions 223(34)
Statistics in Practice: Procter & Gamble
224(1)
6.1 Uniform Probability Distribution
225(4)
Area as a Measure of Probability
226(3)
6.2 Normal Probability Distribution
229(14)
Normal Curve
229(2)
Standard Normal Probability Distribution
231(6)
Computing Probabilities for Any Normal Distribution
237(1)
Grear Tire Company Problem
238(5)
6.3 Normal Approximation of Binomial Probabilities
243(3)
6.4 Exponential Probability Distribution
246(4)
Computing Probabilities for the Exponential Distribution
246(2)
Relationship Between the Poisson and Exponential Distributions
248(2)
Summary
250(1)
Glossary
250(1)
Key Formulas
250(1)
Supplementary Exercises
251(3)
Case Problem Specialty Toys
254(1)
Appendix 6.1 Continuous Probability Distributions with Minitab
255(1)
Appendix 6.2 Continuous Probability Distributions with Excel
256(1)
Chapter 7 Sampling and Sampling Distributions 257(40)
Statistics in Practice: MeadWestvaco Corporation
258(1)
7.1 The Electronics Associates Sampling Problem
259(1)
7.2 Simple Random Sampling
260(4)
Sampling from a Finite Population
260(1)
Sampling from an Infinite Population
261(3)
7.3 Point Estimation
264(3)
7.4 Introduction to Sampling Distributions
267(3)
7.5 Sampling Distribution of x
270(9)
Expected Value of x
270(1)
Standard Deviation of x
271(1)
Form of the Sampling Distribution of x
272(1)
Sampling Distribution of x for the EAI Problem
273(1)
Practical Value of the Sampling Distribution of x
274(1)
Relationship Between the Sample Size and the Sampling Distribution of x
275(4)
7.6 Sampling Distribution of p
279(5)
Expected Value of p
280(1)
Standard Deviation of p
280(1)
Form of the Sampling Distribution of p
281(1)
Practical Value of the Sampling Distribution of p
281(3)
7.7 Properties of Point Estimators
284(3)
Unbiased
285(1)
Efficiency
286(1)
Consistency
287(1)
7.8 Other Sampling Methods
287(3)
Stratified Random Sampling
287(1)
Cluster Sampling
288(1)
Systematic Sampling
288(1)
Convenience Sampling
289(1)
Judgment Sampling
289(1)
Summary
290(1)
Glossary
290(1)
Key Formulas
291(1)
Supplementary Exercises
292(2)
Appendix 7.1 The Expected Value and Standard Deviation of x
294(1)
Appendix 7.2 Random Sampling with Minitab
295(1)
Appendix 7.3 Random Sampling with Excel
296(1)
Chapter 8 Interval Estimation 297(39)
Statistics in Practice: Food Lion
298(1)
8.1 Population Mean: σ Known
299(6)
Margin of Error and the Interval Estimate
300(3)
Practical Advice
303(2)
8.2 Population Mean: σ Unknown
305(9)
Margin of Error and the Interval Estimate
306(3)
Practical Advice
309(1)
Using a Small Sample
309(2)
Summary of Interval Estimation Procedures
311(3)
8.3 Determining the Sample Size
314(3)
8.4 Population Proportion
317(5)
Determining the Sample Size
319(3)
Summary
322(1)
Glossary
323(1)
Key Formulas
324(1)
Supplementary Exercises
324(3)
Case Problem 1 Bock Investment Services
327(1)
Case Problem 2 Gulf Real Estate Properties
327(3)
Case Problem 3 Metropolitan Research, Inc.
330(1)
Appendix 8.1 Interval Estimation with Minitab
331(1)
Appendix 8.2 Interval Estimation Using Excel
332(4)
Chapter 9 Hypothesis Testing 336(57)
Statistics in Practice: John Morrell & Company
337(1)
9.1 Developing Null and Alternative Hypotheses
338(2)
Testing Research Hypotheses
338(1)
Testing the Validity of a Claim
338(1)
Testing in Decision-Making Situations
339(1)
Summary of Forms for Null and Alternative Hypotheses
339(1)
9.2 Type I and Type II Errors
340(3)
9.3 Population Mean: σ Known
343(15)
One-Tailed Test
343(6)
Two-Tailed Test
349(3)
Summary and Practical Advice
352(1)
Relationship Between Interval Estimation and Hypothesis Testing
353(5)
9.4 Population Mean: σ Unknown
358(7)
One-Tailed Test
358(2)
Two-Tailed Test
360(1)
Summary and Practical Advice
361(4)
9.5 Population Proportion
365(5)
Summary
367(3)
9.6 Hypothesis Testing and Decision Making
370(1)
9.7 Calculating the Probability of Type II Errors
371(5)
9.8 Determining the Sample Size for Hypothesis Test About a Population Mean
376(4)
Summary
380(1)
Glossary
380(1)
Key Formulas
381(1)
Supplementary Exercises
381(3)
Case Problem 1 Quality Associates, Inc.
384(1)
Case Problem 2 Unemployment Study
385(1)
Appendix 9.1 Hypothesis Testing with Minitab
386(2)
Appendix 9.2 Hypothesis Testing with Excel
388(5)
Chapter 10 Statistical Inference About Means and Proportions with Two Populations 393(39)
Statistics in Practice: Fisons Corporation
394(1)
10.1 Inferences About the Difference Between Two Population Means: σl and σ2, Known
395(7)
Interval Estimate of μ1-μ2
395(2)
Hypothesis Tests About μ1-μ2
397(2)
Practical Advice
399(3)
10.2 Inferences About the Difference Between Two Population Means: σl and σ2 Unknown
402(9)
Interval Estimation of μ1-μ2
402(1)
Hypothesis Tests About μ1-μ2
403(2)
Practical Advice
405(6)
10.3 Inferences About the Difference Between Two Population Means: Matched Samples
411(6)
10.4 Inferences About the Difference Between Two Population Proportions
417(5)
Interval Estimation of ρ1-ρ2
417(2)
Hypothesis Tests About μ1-μ2
419(3)
Summary
422(1)
Glossary
423(1)
Key Formulas
423(2)
Supplementary Exercises
425(2)
Case Problem Par, Inc.
427(1)
Appendix 10.1 Inferences About Two Populations Using Minitab
428(2)
Appendix 10.2 Inferences About Two Populations Using Excel
430(2)
Chapter 11 Inferences About Population Variances 432(25)
Statistics in Practice: U.S. General Accounting Office
433(1)
11.1 Inferences About a Population Variance
434(9)
Interval Estimation
434(4)
Hypothesis Testing
438(5)
11.2 Inferences About Two Population Variances
443(7)
Summary
450(1)
Key Formulas
450(1)
Supplementary Exercises
451(1)
Case Problem Air Force Training Program
452(2)
Appendix 11.1 Population Variances with Minitab
454(1)
Appendix 11.2 Population Variances with Excel
455(2)
Chapter 12 Tests of Goodness of Fit and Independence 457(33)
Statistics in Practice: United Way
458(1)
12.1 Goodness of Fit Test: A Multinomial Population
459(5)
12.2 Test of Independence
464(7)
12.3 Goodness of Fit Test: Poisson and Normal Distributions
471(10)
Poisson Distribution
472(3)
Normal Distribution
475(6)
Summary
481(1)
Glossary
481(1)
Key Formulas
481(1)
Supplementary Exercises
482(3)
Case Problem A Bipartisan Agenda for Change
485(1)
Appendix 12.1 Tests of Goodness of Fit and Independence Using Minitab
486(1)
Appendix 12.2 Tests of Goodness of Fit and Independence Using Excel
487(3)
Chapter 13 Analysis of Variance and Experimental Design 490(63)
Statistics in Practice: Burke Marketing Services, Inc.
491(1)
13.1 An Introduction to Analysis of Variance
491(4)
Assumptions for Analysis of Variance
493(1)
A Conceptual Overview
493(2)
13.2 Analysis of Variance: Testing for the Equality of k Population Means
495(10)
Between-Treatments Estimate of Population Variance
496(1)
Within-Treatments Estimate of Population Variance
497(1)
Comparing the Variance Estimates: The F Test
498(2)
ANOVA Table
500(1)
Computer Results for Analysis of Variance
500(5)
13.3 Multiple Comparison Procedures
505(6)
Fisher's LSD
506(2)
Type I Error Rates
508(3)
13.4 An Introduction to Experimental Design
511(2)
Data Collection
512(1)
13.5 Completely Randomized Designs
513(6)
Between-Treatments Estimate of Population Variance
514(1)
Within-Treatments Estimate of Population Variance
514(1)
Comparing the Variance Estimates: The F Test 514
ANOVA Table
515(1)
Pairwise Comparisons
515(4)
13.6 Randomized Block Design
519(7)
Air Traffic Controller Stress Test
520(1)
ANOVA Procedure
521(1)
Computations and Conclusions
522(4)
13.7 Factorial Experiments
526(8)
ANOVA Procedure
527(1)
Computations and Conclusions
528(6)
Summary
534(1)
Glossary
534(1)
Key Formulas
535(2)
Supplementary Exercises
537(8)
Case Problem 1 Wentworth Medical Center
545(1)
Case Problem 2 Compensation for ID Professionals
546(1)
Appendix 13.1 Analysis of Variance and Experimental Design with Minitab
547(1)
Appendix 13.2 Analysis of Variance and Experimental Design with Excel
548(5)
Chapter 14 Simple Linear Regression 553(82)
Statistics in Practice: Alliance Data Systems
554(1)
14.1 Simple Linear Regression Model
555(3)
Regression Model and Regression Equation
555(1)
Estimated Regression Equation
556(2)
14.2 Least Squares Method
558(11)
14.3 Coefficient of Determination
569(7)
Correlation Coefficient
572(4)
14.4 Model Assumptions
576(2)
14.5 Testing for Significance
578(9)
Estimate of σ2
578(1)
t Test
579(1)
Confidence Interval for β1
580(1)
F Test
581(2)
Some Cautions About the Interpretation of Significance Tests
583(4)
14.6 Using the Estimated Regression Equation for Estimation and Prediction
587(6)
Point Estimation
587(1)
Interval Estimation
587(1)
Confidence Interval for the Mean Value of y
588(1)
Prediction Interval for an Individual Value of y
589(4)
14.7 Computer Solution
593(5)
14.8 Residual Analysis: Validating Model Assumptions
598(9)
Residual Plot Against x
599(1)
Residual Plot Against y
600(1)
Standardized Residuals
600(3)
Normal Probability Plot
603(4)
14.9 Residual Analysis: Outliers and Influential Observations
607(7)
Detecting Outliers
607(2)
Detecting Influential Observations
609(5)
Summary
614(1)
Glossary
615(1)
Key Formulas
616(2)
Supplementary Exercises
618(5)
Case Problem 1 Spending and Student Achievement
623(2)
Case Problem 2 U.S. Department of Transportation
625(1)
Case Problem 3 Alumni Giving
626(2)
Case Problem 4 Major League Baseball Team Values
628(1)
Appendix 14.1 Calculus-Based Derivation of Least Squares Formulas
629(1)
Appendix 14.2 A Test for Significance Using Correlation
630(1)
Appendix 14.3 Regression Analysis with Minitab
631(1)
Appendix 14.4 Regression Analysis with Excel
632(3)
Chapter 15 Multiple Regression 635(68)
Statistics in Practice: International Paper
636(1)
15.1 Multiple Regression Model
637(1)
Regression Model and Regression Equation
637(1)
Estimated Multiple Regression Equation
637(1)
15.2 Least Squares Method
638(8)
An Example: Butler Trucking Company
639(2)
Note on Interpretation of Coefficients
641(5)
15.3 Multiple Coefficient of Determination
646(3)
15.4 Model Assumptions
649(1)
15.5 Testing for Significance
650(8)
F Test
651(3)
t Test
654(1)
Multicollinearity
654(4)
15.6 Using the Estimated Regression Equation for Estimation and Prediction
658(2)
15.7 Qualitative Independent Variables
660(8)
An Example: Johnson Filtration, Inc.
660(2)
Interpreting the Parameters
662(2)
More Complex Qualitative Variables
664(4)
15.8 Residual Analysis
668(7)
Detecting Outliers
669(1)
Studentized Deleted Residuals and Outliers
670(1)
Influential Observations
671(1)
Using Cook's Distance Measure to Identify Influential Observations
671(4)
15.9 Logistic Regression
675(12)
Logistic Regression Equation
676(1)
Estimating the Logistic Regression Equation
677(2)
Testing for Significance
679(1)
Managerial Use
680(1)
Interpreting the Logistic Regression Equation
680(3)
Logit Transformation
683(4)
Summary
687(1)
Glossary
687(1)
Key Formulas
688(2)
Supplementary Exercises
690(5)
Case Problem 1 Consumer Research, Inc.
695(1)
Case Problem 2 NFL Quarterback Rating
696(2)
Case Problem 3 Predicting Student Proficiency Test Scores
698(1)
Case Problem 4 Alumni Giving
699(1)
Appendix 15.1 Multiple Regression with Minitab
699(1)
Appendix 15.2 Multiple Regression with Excel
699(3)
Appendix 15.3 Logistic Regression with Minitab
702(1)
Chapter 16 Regression Analysis: Model Building 703(54)
Statistics in Practice: Monsanto Company
704(1)
16.1 General Linear Model
705(15)
Modeling Curvilinear Relationships
705(4)
Interaction
709(2)
Transformations Involving the Dependent Variable
711(4)
Nonlinear Models That Are Intrinsically Linear
715(5)
16.2 Determining When to Add or Delete Variables
720(7)
General Case
722(1)
Use of ρ-Values
723(4)
16.3 Analysis of a Larger Problem
727(3)
16.4 Variable Selection Procedures
730(7)
Stepwise Regression
731(1)
Forward Selection
732(1)
Backward Elimination
733(1)
Best-Subsets Regression
733(1)
Making the Final Choice
734(3)
16.5 Residual Analysis
737(7)
Autocorrelation and the Durbin-Watson Test
738(6)
16.6 Multiple Regression Approach to Analysis of Variance and Experimental Design
744(4)
Summary
748(1)
Glossary
748(1)
Key Formulas
749(1)
Supplementary Exercises
749(4)
Case Problem 1 Unemployment Study
753(2)
Case Problem 2 Fuel Economy for Cars
755(1)
Case Problem 3 Predicting Graduation Rates for Colleges and Universities
755(2)
Chapter 17 Index Numbers 757(21)
Statistics in Practice: U.S. Department of Labor, Bureau of Labor Statistics
758(1)
17.1 Price Relatives
759(1)
17.2 Aggregate Price Indexes
759(4)
17.3 Computing an Aggregate Price Index from Price Relatives
763(2)
17.4 Some Important Price Indexes
765(2)
Consumer Price Index
765(1)
Producer Price Index
765(1)
Dow Jones Averages
766(1)
17.5 Deflating a Series By Price Indexes
767(4)
17.6 Price Indexes: Other Considerations
771(1)
Selection of Items
771(1)
Selection of a Base Period
771(1)
Quality Changes
771(1)
17.7 Quantity Indexes
772(2)
Summary
774(1)
Glossary
774(1)
Key Formulas
774(1)
Supplementary Exercises
775(3)
Chapter 18 Forecasting 778(47)
Statistics in Practice: Nevada Occupational Health Clinic
779(1)
18.1 Components of a Time Series
780(3)
Trend Component
780(2)
Cyclical Component
782(1)
Seasonal Component
783(1)
Irregular Component
783(1)
18.2 Smoothing Methods
783(10)
Moving Averages
783(2)
Weighted Moving Averages
785(2)
Exponential Smoothing
787(6)
18.3 Trend Projection
793(6)
18.4 Trend and Seasonal Components
799(10)
Multiplicative Model
799(1)
Calculating the Seasonal Indexes
800(4)
Deseasonalizing the Time Series
804(1)
Using the Deseasonalized Time Series to Identify Trend
804(3)
Seasonal Adjustments
807(1)
Models Based on Monthly Data
807(1)
Cyclical Component
807(2)
18.5 Regression Analysis
809(2)
18.6 Qualitative Approaches
811(1)
Delphi Method
811(1)
Expert Judgment
812(1)
Scenario Writing
812(1)
Intuitive Approaches
812(1)
Summary
812(1)
Glossary
813(1)
Key Formulas
814(1)
Supplementary Exercises
814(5)
Case Problem 1 Forecasting Food and Beverage Sales
819(1)
Case Problem 2 Forecasting Lost Sales
820(1)
Appendix 18.1 Forecasting with Minitab
821(2)
Appendix 18.2 Forecasting with Excel
823(2)
Chapter 19 Nonparametric Methods 825(34)
Statistics in Practice: West Shell Realtors
826(2)
19.1 Sign Test
828(5)
Small-Sample Case
828(2)
Large-Sample Case
830(1)
Hypothesis Test About a Median
831(2)
19.2 Wilcoxon Signed-Rank Test
833(5)
19.3 Mann-Whitney-Wilcoxon Test
838(8)
Small-Sample Case
838(2)
Large-Sample Case
840(6)
19.4 Kruskal-Wallis Test
846(4)
19.5 Rank Correlation
850(4)
Test for Significant Rank Correlation
852(2)
Summary
854(1)
Glossary
855(1)
Key Formulas
855(1)
Supplementary Exercises
856(3)
Chapter 20 Statistical Methods for Quality Control 859(30)
Statistics in Practice: Dow Chemical U.S.A.
860(1)
20.1 Statistical Process Control
861(14)
Control Charts
862(1)
x Chart: Process Mean and Standard Deviation Known
863(2)
x Chart: Process Mean and Standard Deviation Unknown
865(2)
R Chart
867(2)
ρ Chart
869(3)
nρ Chart
872(1)
Interpretation of Control Charts
872(3)
20.2 Acceptance Sampling
875(9)
KALI, Inc.: An Example of Acceptance Sampling
876(1)
Computing the Probability of Accepting a Lot
877(3)
Selecting an Acceptance Sampling Plan
880(1)
Multiple Sampling Plans
881(3)
Summary
884(1)
Glossary
884(1)
Key Formulas
885(1)
Supplementary Exercises
886(2)
Appendix 20.1 Control Charts with Minitab
888(1)
Chapter 21 Decision Analysis 889(37)
Statistics in Practice: Ohio Edison Company
890(1)
21.1 Problem Formulation
891(2)
Payoff Tables
892(1)
Decision Trees
892(1)
21.2 Decision Making with Probabilities
893(8)
Expected Value Approach
893(2)
Expected Value of Perfect Information
895(6)
21.3 Decision Analysis with Sample Information
901(11)
Decision Tree
902(1)
Decision Strategy
903(3)
Expected Value of Sample Information
906(6)
21.4 Computing Branch Probabilities Using Bayes' Theorem
912(4)
Summary
916(1)
Glossary
917(1)
Key Formulas
918(1)
Case Problem Lawsuit Defense Strategy
918(1)
Appendix 21.1 Solving the PDC Problem with TreePlan
919
Chapter 22 Sample Survey On CD
Appendix A References and Bibliography 926(2)
Appendix B Tables 928(29)
Appendix C Summation Notation 957(2)
Appendix D Self-Test Solutions and Answers to Even-Numbered Exercises 959(51)
Index 1010


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