Business Statistics by Example

by Sincich, Terry L.

• ISBN13: 9780024104410
• ISBN10: 0024104418
• Edition: 5th
• Format: Paperback
• Copyright: 1995-11-22
• Publisher: Pearson

Summary

Gathering and interpreting statistics to diagnose problems and evaluate results in the business environment is an important skill that is more easily learned in the context of real applications, cases, and projects. This thorough introduction to business statistics introduces every topic with real examples and data sets from today's business world, and features menu-driven, user-friendly ASP statistical software that performs all necessary statistical computations, allowing readers to concentrate on interpretations. An abundance of practice exercises focuses on both mechanics applications.Comprehensive coverage surveys thefull rangeof statistical topics and provides a thorough exploration of statistical computing -- with computer printouts for SAS, MINITAB, and SPSS, integrated throughout, and optional computer applications for each chapter -- with SAS, MINITAB, and SPSS commands. Real data sets include appraisals and sale prices for residential property sales;Business Week's executive compensation scoreboard, 1994; characteristics of HMO physicians in a managed-care system; Federal Trade Commission rankings of 372 cigarette brands. In addition to the new accompanying software, theFifth Editionprovides updated case studies, examples, and exercises based on studies reported in recent magazine, journal and news articles.For business personnel who want a comprehensive, applied introduction to business statistics.

Table of Contents

Preface	xiii
1 INTRODUCTION: STATISTICS AND DATA	1	(26)
1.1 What Is Statistics?	2	(1)
1.2 Types of Data	3	(6)
1.3 Fundamental Elements of a Statistical Analysis	9	(7)
1.4 Collecting Data	16	(9)
CASE STUDY 1.1 The Executive Compensation Scoreboard	25	(1)
CASE STUDY 1.2 HMO Physicians: Cost-effective?	26	(1)
2 GRAPHICAL METHODS FOR DESCRIBING DATA SETS	27	(53)
2.1 The Objective of Data Description	28	(1)
2.2 Graphical Descriptions of Qualitative Data	28	(11)
2.3 Graphical Descriptions of Quantitative Data: Stem-and-Leaf Displays	39	(11)
2.4 Graphical Descriptions of Quantitative Data: Histograms	50	(28)
CASE STUDY 2.1 Warning: Cigarette Smoke Is Hazardous to Your Health	78	(1)
CASE STUDY 2.2 Statistical Quality Control: Fudging the Data	79	(1)
3 NUMERICAL METHODS FOR DESCRIBING QUANTITATIVE DATA	80	(70)
3.1 Why We Need Numerical Descriptive Measures	81	(1)
3.2 Types of Numerical Descriptive Measures	81	(1)
3.3 Summation Notation	82	(3)
3.4 Measures of Central Tendency	85	(10)
3.5 Measures of Data Variation	95	(5)
3.6 Interpreting the Standard Deviation	100	(14)
3.7 Measures of Relative Standing	114	(7)
3.8 Methods for Detecting Outliers	121	(9)
3.9 Numerical Descriptive Measures for Populations	130	(14)
CASE STUDY 3.1 Are CEOs Really Worth Their Pay?	144	(2)
CASE STUDY 3.2 Bid Collusion in the Highway Contracting Industry	146	(4)
4 PROBABILITY: BASIC CONCEPTS	150	(52)
4.1 The Role of Probability in Statistics	151	(1)
4.2 Experiments, Events, and the Probability of an Event	151	(5)
4.3 Mutually Exclusive Events	156	(14)
4.4 Conditional Probability and Independence	170	(8)
4.5 The Additive and Multiplicative Laws of Probability (Optional)	178	(19)
CASE STUDY 4.1 Lottery Buster!	197	(3)
CASE STUDY 4.2 To Switch or Not to Switch?	200	(2)
5 DESCRETE PROBABILITY DISTRIBUTIONS	202	(53)
5.1 Random Variables	203	(1)
5.2 Probability Models for Discrete Random Variables	204	(4)
5.3 The Expected Value and Variance of a Discrete Random Variable	208	(4)
5.4 The Binomial Probability Distribution	212	(11)
5.5 The Poisson Probability Distribution	223	(8)
5.6 The Hypergeometric Probability Distribution (Optional)	231	(7)
5.7 The Geometric Probability Distribution (Optional)	238	(13)
CASE STUDY 5.1 Commitment to the Firm: Stayers Versus Leavers	251	(2)
CASE STUDY 5.2 The Chevalier's Dilemma (Optional)	253	(2)
6 CONTINUOUS PROBABILITY DISTRIBUTIONS	255	(52)
6.1 Probability Models for Continuous Random Variables	256	(1)
6.2 The Normal Probability Distribution	257	(16)
6.3 Descriptive Methods for Assessing Normality	273	(8)
6.4 The Normal Approximation to the Binomial Distribution	281	(5)
6.5 The Uniform Probability Distribution (Optional)	286	(4)
6.6 The Exponential Probability Distribution (Optional)	290	(10)
CASE STUDY 6.1 A Random Walk Down Wall Street	300	(5)
CASE STUDY 6.2 Break-Even Analysis-When to Market a New Product	305	(2)
7 SAMPLING AND SAMPLING DISTRIBUTIONS	307	(34)
7.1 Why the Method of Sampling Is Important	308	(3)
7.2 Sampling Distributions	311	(8)
7.3 The Sampling Distribution of x; the Central Limit Theorem	319	(18)
CASE STUDY 7.1 TV Telephone Polls-Dial 900 to Register Your Opinion	337	(1)
CASE STUDY 7.2 A Decision Problem for Financial Managers: When to Investigate Cost Variances	338	(3)
8 ESTIMATION OF POPULATION PARAMETERS: CONFIDENCE INTERVALS	341	(81)
8.1 Introduction	342	(1)
8.2 Estimation of a Population Mean: Normal (z) Statistic	342	(12)
8.3 Estimation of a Population Mean: Student's t Statistic	354	(10)
8.4 Estimation of a Population Proportion	364	(5)
8.5 Estimation of the Difference Between Two Population Means: Independent Samples	369	(13)
8.6 Estimation of the Difference Between Two Population Means: Matched Pairs	382	(8)
8.7 Estimation of the Difference Between Two Population Proportions	390	(6)
8.8 Choosing the Sample Size	396	(7)
8.9 Estimation of a Population Variance (Optional)	403	(15)
CASE STUDY 8.1 An Aspirin a Day Keeps the Heart Doctor Away	418	(2)
CASE STUDY 8.2 Public Opinion Polls: How Accurate Are They?	420	(2)
9 INTRODUCTION TO SAMPLE SURVEY METHODS (OPTIONAL)	422	(47)
9.1 Why Sample Survey Methods Are Useful	423	(1)
9.2 Terminology	423	(2)
9.3 A Finite Population Correction Factor	425	(1)
9.4 Estimation: Simple Random Sampling	426	(4)
9.5 Choosing the Sample Size: Simple Random Sampling	430	(2)
9.6 Other Types of Samples: Stratified, Cluster, and Systematic Samples	432	(3)
9.7 Estimation: Stratified Random Sampling	435	(9)
9.8 Choosing the Sample Size: Stratified Random Sampling	444	(4)
9.9 Estimation: Cluster Sampling	448	(7)
9.10 Choosing the Sample Size: Cluster Sampling	455	(3)
9.11 Problems of Nonresponse and Invalid Responses	458	(7)
CASE STUDY 9.1 The New Hite Report-Controversy over the Numbers	465	(2)
CASE STUDY 9.2 Increasing Survey Response Rates: The Foot and the Face Techniques	467	(2)
10 COLLECTING EVIDENCE TO SUPPORT A THEORY: GENERAL CONCEPTS OF HYPOTHESIS TESTING	469	(41)
10.1 The Relationship Between Statistical Tests of Hypothesis and Confidence Intervals	470	(1)
10.2 Formulation of Hypotheses	471	(4)
10.3 Decisions and Consequences for a Hypothesis Test	475	(4)
10.4 Test Statistics and Rejection Regions	479	(12)
10.5 Reporting Test Results: p-Values	491	(5)
10.6 Calculating the Probability of a Type II Error and the Power of a Test (Optional)	496	(11)
CASE STUDY 10.1 Drug Screening: A Statistical Decision Problem	507	(2)
CASE STUDY 10.2 Schlitz Versus Budweiser-Mug-to-Mug	509	(1)
11 HYPOTHESIS TESTING: APPLICATIONS	510	(70)
11.1 Diagnosing a Hypothesis Test: Determining the Target Parameter	511	(2)
11.2 Testing a Population Mean	513	(12)
11.3 Testing a Population Proportion	525	(4)
11.4 Testing the Difference Between Two Population Means: Independent Samples	529	(13)
11.5 Testing the Difference Between Two Population Means: Matched Pairs	542	(8)
11.6 Testing the Difference Between Two Population Proportions	550	(7)
11.7 Testing a Population Variance (Optional)	557	(5)
11.8 Testing the Ratio of Two Population Variances (Optional)	562	(16)
CASE STUDY 11.1 Comparing Low-Bid Prices to DOT Estimates in Highway Construction	578	(1)
CASE STUDY 11.2 Analyzing the Commitment Data: Stayers Versus Leavers	579	(1)
12 SIMPLE LINEAR REGRESSION AND CORRELATION	580	(80)
12.1 Bivariate Relationships	581	(1)
12.2 Straight-Line Probabilistic Models	581	(4)
12.3 Estimating the Model Parameters: The Method of Least Squares	585	(17)
12.4 Assumptions	602	(1)
12.5 Measuring Variability Around the Least Squares Line	603	(7)
12.6 Making Inferences About the Slope β1	610	(9)
12.7 The Coefficient of Correlation	619	(8)
12.8 The Coefficient of Determination	627	(6)
12.9 Using the Model for Estimation and Prediction	633	(9)
12.10 Simple Linear Regression: A Complete Example	642	(14)
CASE STUDY 12.1 The SOB Effect Among College Administrators	656	(2)
CASE STUDY 12.2 Top Corporate Executives and Their Pay-Another Look	658	(2)
13 MULTIPLE REGRESSION AND MODEL BUILDING	660	(124)
13.1 Introduction: The General Linear Model	660	(2)
13.2 Model Assumptions	662	(1)
13.3 Fitting the Model and Interpreting the β Estimates	662	(7)
13.4 Estimating and Interpreting σ2	669	(1)
13.5 Estimating and Testing Hypotheses About the β Parameters	670	(4)
13.6 The Coefficient of Determination	674	(3)
13.7 Testing Whether the Model Is Useful for Predicting y	677	(4)
13.8 Using the Model for Estimation and Prediction	681	(14)
13.9 Model Building: Interaction Models	695	(12)
13.10 Model Building: Second-Order (Quadratic) Models	707	(13)
13.11 Model Building: Qualitative (Dummy) Variables	720	(8)
13.12 Model Building: Comparing Nested Models	728	(10)
13.13 Residual Analysis	738	(21)
13.14 Multicollinearity	759	(20)
CASE STUDY 13.1 The Salary Race: Males Versus Females	779	(2)
CASE STUDY 13.2 Building a Model for the Sale Price of a Residential Property	781	(3)
14 ANALYSIS OF VARIANCE	784	(88)
14.1 Introduction	785	(1)
14.2 Experimental Design: Terminology	785	(3)
14.3 The Logic Behind an Analysis of Variance	788	(3)
14.4 Completely Randomized Designs	791	(14)
14.5 Randomized Block Designs	805	(13)
14.6 Factorial Experiments	818	(16)
14.7 Follow-Up Analysis: Multiple Comparisons of Means	834	(10)
14.8 The Relationship Between Analysis of Variance and Regression	844	(5)
14.9 Checking ANOVA Assumptions	849	(3)
14.10 Calculation Formulas for ANOVA (Optional)	852	(17)
CASE STUDY 14.1 Reluctance to Transmit Bad News: The MUM Effect	869	(2)
CASE STUDY 14.2 Comparing the Costs of HMO Physician Case Managers	871	(1)
15 INTRODUCTION TO PROCESS AND QUALITY CONTROL	872	(52)
15.1 Total Quality Management	873	(1)
15.2 Variable Control Charts	873	(8)
15.3 Control Chart for Means: x -Chart	881	(11)
15.4 Control Chart for Process Variation: R-Chart	892	(4)
15.5 Detecting Trends in a Control Chart: Runs Analysis	896	(3)
15.6 Control Chart for Percent Defectives: p-Chart	899	(7)
15.7 Control Chart for Number of Defects per Item: c-Chart	906	(6)
15.8 Tolerance Limits (Optional)	912	(9)
CASE STUDY 15.1 Applying Quality Concepts to Control the Manufacture of Steel Rods	921	(2)
CASE STUDY 15.2 Heading Off Trouble: The Theory of Runs	923	(1)
16 TIME SERIES ANALYSIS AND FORECASTING	924	(88)
16.1 Introduction	925	(1)
16.2 Time Series Components	925	(4)
16.3 Index Numbers	929	(14)
16.4 Smoothing Methods	943	(10)
16.5 Forecasting Using Smoothing Techniques	953	(9)
16.6 Forecasting Using Regression	962	(8)
16.7 Time Series Forecasting Models	970	(1)
16.8 Testing for Autocorrelatiun: The Durbin-Watson Test	971	(11)
16.9 Forecasting Using Autoregressive Error Models	982	(11)
16.10 Forecasting Using Lagged Values of the Dependent Variable	993	(12)
CASE STUDY 16.1 Analyzing the Price of Your Favorite Stock	1005	(1)
CASE STUDY 16.2 Modeling Peak Electricity Demands at Florida Power Corporation	1006	(6)
17 CATEGORICAL DATA ANALYSIS	1012	(49)
17.1 Categorical Data and the Multinomial Experiment	1013	(1)
17.2 Testing Category Probabilities: One-Way Table	1014	(9)
17.3 Testing Category Probabilities: Two-Way (Contingency) Table	1023	(16)
17.4 Modeling Category Probabilities: Logistic Regression (Optional)	1039	(20)
CASE STUDY 17.1 Birth Order and the Car Salesman: Is Last Best?	1059	(1)
CASE STUDY 17.2 Reanalyzing the Commitment Data: Stayers Versus Leavers	1060	(1)
18 NONPARAMETRIC STATISTICS	1061	(68)
18.1 Introduction: Distribution-Free Tests	1062	(1)
18.2 Testing for Location of a Single Population	1063	(9)
18.3 Comparing Two Populations: Independent Random Samples	1072	(11)
18.4 Comparing Two Populations: Matched-Pairs Design	1083	(7)
18.5 Comparing Three or More Populations: Completely Randomized Design	1090	(9)
18.6 Comparing Three or More Populations: Randomized Block Design	1099	(7)
18.7 Nonparametric Regression	1106	(20)
CASE STUDY 18.1 Cost-Effectiveness of Case Manager Physicians	1126	(1)
CASE STUDY 18.2 Deadly Exposure: Agent Orange and Vietnam Vets	1127	(2)
19 ELEMENTS OF DECISION ANALYSIS	1129	(51)
19.1 Introduction: Decision-Making Under Uncertainty	1130	(1)
19.2 Elements Common to Decision Problems	1131	(2)
19.3 Illustrating a Decision Problem: The Payoff Table and Opportunity Loss Table	1133	(7)
19.4 Illustrating a Decision Problem: The Decision Tree	1140	(4)
19.5 Solving a Decision Problem: Expected Values	1144	(8)
19.6 Solving a Decision Problem: Maximax, Maximin, and Minimax Criteria	1152	(5)
19.7 Bayes' Rule (Optional)	1157	(4)
19.8 Solving a Decision Problem Using Sample Information (Optional)	1161	(15)
CASE STUDY 19.1 Decision Making Under Uncertainty in the Petroleum Industry	1176	(2)
CASE STUDY 19.2 Choosing the Right Fork of a Decision Tree	1178	(2)
Appendix A DATA SETS	1180	(8)
Appendix B STATISTICAL TABLES	1188	(32)
Appendix C SAS TUTORIAL	1220	(1)
Appendix D SPSS TUTORIAL	1220	(1)
Appendix E NIMITAB TUTORIAL	1220	(1)
Appendix F ASP TUTORIAL	1220	(1)
Answers to Odd Numbered Exercises	1221	(30)
Index	1251