Applied Statistics in Business and Economics with St CD-ROM

by Doane, David P.

• ISBN13: 9780073215754
• ISBN10: 0073215759
• Edition: 1st
• Format: Hardcover w/CD
• Copyright: 2006-01-19
• Publisher: McGraw-Hill/Irwin

Summary

This new text offers an Excel focused approach to using statistics in business. All statistical concepts are illustrated with applied examples immediately upon introduction. Modern computing tools and applications are introduced, and the text maintains a strong focus on presenting statistical concepts as applied in business --as opposed to providing programming methods used to find a mathematical solution. Interpretation is heavily emphasized, enabling students to take full advantage of Excel to develop and drive problem-solving skills.

Author Biography

Lori E. Seward is a Senior Instructor of Systems in the Leeds School of Business at the University of Colorado - Boulder. David P. Doane is Professor of Quantitative Methods in Oakland University's Department of Decision and Information Sciences.

Table of Contents

Overview of Statistics	2	(20)
What is Statistics?	3	(1)
Why Study Statistics?	4	(1)
Communication	4	(1)
Computer Skills	4	(1)
Information Management	4	(1)
Technical Literacy	4	(1)
Career Advancement	4	(1)
Quality Improvement	4	(1)
Uses of Statistics	5	(1)
Auditing	5	(1)
Marketing	5	(1)
Health Care	5	(1)
Quality Control	5	(1)
Purchasing	6	(1)
Medicine	6	(1)
Forecasting	6	(1)
Product Warranty	6	(1)
Statistical Challenges	6	(3)
Working with Imperfect Data	6	(1)
Dealing with Practical Constraints	7	(1)
Upholding Ethical Standards	7	(1)
Using Consultants	7	(2)
Writing and Presenting Reports	9	(5)
Rules for ``Power'' Writing	9	(1)
Writing Style	9	(1)
Spelling and Grammar	10	(1)
Organizing a Technical Report	10	(1)
Writing an Executive Summary	10	(1)
Tables and Graphs	11	(1)
Rules for Presenting Oral Reports	11	(1)
The Three Ps	12	(2)
Statistical Pitfalls	14	(2)
Pitfall 1: Making Conclusions about a Large Population from a Small Sample	14	(1)
Pitfall 2: Making Conclusions from Nonrandom Samples	14	(1)
Pitfall 3: Attaching Importance to Rare Observations from Large Samples	14	(1)
Pitfall 4: Using Poor Survey Methods	14	(1)
Pitfall 5: Assuming a Causal Link Based Only on Observed Association	15	(1)
Pitfall 6: Making Generalizations about Individuals from Observations about Groups	15	(1)
Pitfall 7: Unconscious Bias	15	(1)
Pitfall 8: Attaching Practical Importance to Every Statistically Significant Study Result	15	(1)
Statistics: An Evolving Field	16	(6)
Chapter Summary	16	(6)
Data Collection	22	(36)
Definitions	23	(3)
Subjects, Variables, and Data Sets	23	(1)
Data Types	24	(2)
Level of Measurement	26	(4)
Nominal Measurement	26	(1)
Ordinal Measurement	27	(1)
Interval Measurement	27	(1)
Ratio Measurement	28	(1)
Changing Data by Recoding	29	(1)
Time-Series versus Cross-Sectional Data	30	(1)
Time-Series Data	30	(1)
Cross-Sectional Data	30	(1)
Sampling Concepts	31	(2)
Sample or Census?	31	(1)
Parameters and Statistics	32	(1)
Target Population	32	(1)
Finite or Infinite?	33	(1)
Sampling Methods	33	(9)
Simple Random Sample	33	(2)
Random Number Tables	35	(1)
Setting Up a Rule	35	(1)
With or Without Replacement?	35	(1)
Computer Methods	36	(1)
Row/Column Data Arrays	36	(1)
Randomizing a List	37	(1)
Systematic Sample	37	(2)
Stratified Sample	39	(1)
Applications of Stratified Sampling	39	(1)
Cluster Sample	39	(1)
Judgment Sample	40	(1)
Convenience Sample	40	(1)
Sample Size	41	(1)
Data Sources	42	(1)
Survey Research	43	(15)
Survey Types	43	(1)
Response Rates	43	(1)
Getting Advice	44	(1)
Questionnaire Design	44	(1)
Question Wording	44	(2)
Coding and Data Screening	46	(1)
Sources of Error	46	(1)
Data File Format	47	(2)
Chapter Summary	49	(9)
Describing Data Visually	58	(54)
Visual Description	59	(2)
Measurement	60	(1)
Sorting	60	(1)
Dot Plots	61	(4)
Small Sample: Home Prices	61	(2)
Comparing Groups	63	(2)
Frequency Distributions and Histograms	65	(7)
Bins and Bin Limits	65	(1)
Constructing a Frequency Distribution	65	(1)
Histograms	66	(1)
Excel Histograms	66	(2)
MegaStat Histograms	68	(1)
MINITAB Histograms	68	(1)
Modal Class	69	(1)
Shape	69	(3)
Line Charts	72	(4)
Simple Line Charts	72	(1)
Grid Lines	73	(1)
Log Scales	73	(2)
Tips for Effective Line Charts	75	(1)
Bar Charts	76	(4)
Plain Bar Charts	76	(1)
3-D and Novelty Bar Charts	76	(1)
Pareto Charts	77	(1)
Stacked Bar Chart	78	(1)
Bar Charts for Time-Series Data	78	(1)
Tips for Effective Bar Charts	79	(1)
Scatter Plots	80	(6)
Policy Making	82	(1)
Degree of Association	82	(2)
Making a Scatter Plot in Excel	84	(2)
Tables	86	(1)
Tips for Effective Tables	87	(1)
Pie Charts	87	(3)
An Oft-Abused Chart	87	(1)
Pie Chart Options	87	(3)
Effective Excel Charts	90	(4)
Chart Wizard	90	(1)
Embellished Charts	91	(3)
Maps and Pictograms	94	(1)
Spatial Variation and GIS	94	(1)
Pictograms	95	(1)
Deceptive Graphs	95	(17)
Error 1: Nonzero Origin	95	(1)
Error 2: Elastic Graph Proportions	96	(1)
Error 3: Dramatic Title	96	(1)
Error 4: Distracting Pictures	96	(1)
Error 5: Authority Figures	97	(1)
Error 6: 3-D and Rotated Graphs	97	(1)
Error 7: Missing Axis Demarcations	97	(1)
Error 8: Missing Measurement Units or Definitions	97	(1)
Error 9: Vague Source	97	(1)
Error 10: Complex Graphs	97	(1)
Error 11: Gratuitous Effects	98	(1)
Error 12: Estimated Data	98	(1)
Error 13: Area Trick	98	(1)
Final Advice	98	(1)
Further Challenges	99	(1)
Chapter Summary	99	(13)
Descriptive Statistics	112	(56)
Numerical Description	113	(6)
Preliminary Analysis	114	(1)
Sorting	114	(1)
Visual Displays	114	(2)
Descriptive Statistics in Excel	116	(1)
Descriptive Statistics in MegaStat	117	(2)
Central Tendency	119	(11)
Mean	119	(1)
Characteristics of the Mean	119	(1)
Median	120	(1)
Characteristics of the Median	121	(1)
Mode	121	(2)
Skewness	123	(4)
Geometric Mean	127	(1)
Growth Rates	127	(1)
Midrange	128	(1)
Trimmed Mean	128	(2)
Dispersion	130	(6)
Range	131	(1)
Variance	131	(1)
Standard Deviation	131	(1)
Calculating a Standard Deviation	132	(1)
Characteristics of the Standard Deviation	133	(1)
Coefficient of Variation	133	(1)
Mean Absolute Deviation	133	(2)
Central Tendency versus Dispersion: Manufacturing	135	(1)
Central Tendency and Dispersion: Job Performance	135	(1)
Standardized Data	136	(5)
Chebyshev's Theorem	136	(1)
The Empirical Rule	137	(1)
Unusual Observations	137	(1)
Defining a Standardized Variable	138	(1)
Outliers	139	(1)
Estimating Sigma	139	(2)
Percentiles and Quartiles	141	(4)
Percentiles	141	(1)
Quartiles	141	(1)
Method of Medians	142	(1)
Formula Method	143	(1)
Excel Quartiles	143	(1)
Dispersion Using Quartiles	144	(1)
Midhinge	144	(1)
Midspread (Interquartile Range)	145	(1)
Coefficient of Quartile Variation	145	(1)
Box Plots	145	(2)
Fences and Unusual Data Values	146	(1)
Grouped Data	147	(2)
Nature and Grouped Data	147	(1)
Mean and Standard Deviation	148	(1)
Accuracy Issues	148	(1)
Properties of Grouped Estimates	149	(1)
Skewness and Kurtosis	149	(19)
Skewness	149	(1)
Kurtosis	150	(2)
Chapter Summary	152	(16)
Probability	168	(40)
Random Experiments	169	(2)
Sample Space	169	(1)
Events	170	(1)
Probability	171	(3)
Definitions	171	(1)
What Is ``Probability''?	171	(1)
Empirical Approach	172	(1)
Law of Large Numbers	172	(1)
Practical Issues for Actuaries	172	(1)
Classical Approach	173	(1)
Subjective Approach	174	(1)
Rules of Probability	174	(6)
Complement of an Event	174	(1)
Odds of an Event	175	(1)
Union of Two Events	175	(1)
Intersection of Two Events	175	(1)
General Law of Addition	176	(1)
Mutually Exclusive Events	177	(1)
Special Law of Addition	177	(1)
Collectively Exhaustive Sets	177	(1)
Forced Dichotomy	177	(1)
Conditional Probability	178	(2)
Independent Events	180	(3)
Dependent Events	180	(1)
Actuaries Again	181	(1)
Multiplication Law for Independent Events	181	(1)
The Five Nines Rule	181	(1)
How Much Redundancy Is Needed?	182	(1)
Applications of Redundancy	182	(1)
Contingency Tables	183	(7)
What Is a Contingency Table?	183	(1)
Marginal Probabilities	184	(1)
Joint Probabilities	184	(1)
Conditional Probabilities	185	(1)
Independence	185	(1)
Relative Frequencies	186	(1)
How Do We Get a Contingency Table?	187	(3)
Tree Diagrams	190	(1)
What Is a Tree?	190	(1)
Bayes's Theorem (Optional)	191	(5)
How Bayes's Theorem Works	191	(1)
General Form of Bayes's Theorem	192	(4)
Counting Rules (Optional)	196	(12)
Fundamental Rule of Counting	196	(1)
Factorials	197	(1)
Permutations	198	(1)
Combinations	198	(2)
Chapter Summary	200	(8)
Discrete Distributions	208	(44)
Probability Models	209	(1)
Discrete Distributions	209	(6)
Random Variables	209	(1)
Probability Distributions	210	(1)
Expected Value	211	(1)
Application: Life Insurance	212	(1)
Application: Raffle Tickets	212	(1)
Actuarial Fairness	213	(1)
Variance and Standard Deviation	213	(1)
What Is a PDF or CDF?	214	(1)
Uniform Distribution	215	(4)
Characteristics of the Uniform Distribution	215	(2)
Application: Pumping Gas	217	(1)
Uniform Random Integers	218	(1)
Application: Copier Codes	218	(1)
Uniform Model in Learning Stats	219	(1)
Bernoulli Distribution	219	(1)
Bernoulli Experiments	219	(1)
Binomial Distribution	220	(8)
Characteristics of the Binomial Distribution	220	(1)
Binomial Shape	221	(1)
Application: Uninsured Patients	222	(1)
Using the Binomial Formula	222	(2)
Using Tables: Appendix A	224	(1)
Compound Events	224	(1)
Binomial Probabilities: Excel	225	(1)
Binomial Probabilities: MegaStat	225	(1)
Binomial Probabilities: Visual Statistics	225	(1)
Binomial Probabilities: LearningStats	225	(1)
Binomial Random Data	225	(1)
Recognizing Binomial Applications	225	(3)
Poisson Distribution	228	(7)
Poisson Processes	228	(1)
Characteristics of the Poisson Distribution	229	(1)
Using the Poisson Formula	230	(2)
Compound Events	232	(1)
Poisson Probabilities: Tables (Appendix B)	232	(1)
Poisson Probabilities: Excel	232	(1)
Poisson Probabilities: Visual Statistics	232	(1)
Recognizing Poisson Applications	233	(1)
Poisson Approximation to Binomial (Optional)	234	(1)
Hypergeometric Distribution	235	(5)
Characteristics of the Hypergeometric Distribution	235	(1)
Using the Hypergeometric Formula	236	(1)
Hypergeometric Probabilities: Excel	237	(1)
Hypergeometric Probabilities: Visual Statistics	237	(1)
Hypergeometric Probabilities: LearningStats	238	(1)
Recognizing Hypergeometric Applications	238	(1)
Binomial Application to the Hypergeometric (Optional)	239	(1)
Geometric Distribution (Optional)	240	(2)
Characteristics of the Geometric Distribution	240	(1)
Using LearningStats	241	(1)
Transformations of Random Variables (Optional)	242	(10)
Linear Transformation	242	(1)
Application: Exam Scores	242	(1)
Application: Total Cost	242	(1)
Sums of Random Variables	243	(1)
Application: Gasoline Expenses	243	(1)
Application: Project Scheduling	243	(1)
Chapter Summary	244	(8)
Continuous Distributions	252	(40)
Continuous Variables	253	(1)
Events as Intervals	253	(1)
Describing a Continuous Distribution	253	(2)
PDFs and CDFs	253	(1)
Probabilities as Areas	254	(1)
Expected Value and Variance	255	(1)
Oh My, Calculus?	255	(1)
Uniform Continuous Distribution	255	(3)
Characteristics of the Uniform Distribution	255	(2)
Special Case: Unit Rectangular	257	(1)
Uses of the Uniform Model	258	(1)
Normal Distribution	258	(3)
Characteristics of the Normal Distribution	258	(2)
What Is Normal?	260	(1)
Standard Normal Distribution	261	(12)
Characteristics of the Standard Normal	261	(1)
Normal Areas from Appendix C-1	262	(1)
Basis for the Empirical Rule	263	(1)
Normal Areas from Appendix C-2	264	(1)
Finding z for a Given Area	265	(2)
Finding Normal Areas with Excel	267	(1)
Finding Areas by Using Standardized Variables	267	(2)
Inverse Normal	269	(1)
Using Excel Without Standardizing	270	(1)
Normal Random Data (Optional)	271	(2)
Normal Approximation to the Binomial (Optional)	273	(3)
When Is Approximation Needed?	273	(3)
Normal Approximation to the Poisson (Optional)	276	(1)
When Is Approximation Needed?	276	(1)
Exponential Distribution	277	(5)
Characteristics of the Exponential Distribution	277	(2)
Inverse Exponential	279	(1)
Mean Time Between Events	280	(1)
Using Excel	281	(1)
Triangular Distribution (Optional)	282	(10)
Characteristics of the Triangular Distribution	282	(2)
Special Case: Symmetric Triangular	284	(1)
Uses of the Triangular	284	(1)
Chapter Summary	284	(8)
Sampling Distributions and Estimation	292	(54)
Sampling Variation	293	(2)
Estimators and Sampling Distributions	295	(3)
Some Terminology	295	(1)
Sampling Distributions	295	(1)
Bias	295	(2)
Efficiency	297	(1)
Consistency	297	(1)
Sample Mean and the Central Limit Theorem	298	(8)
Central Limit Theorem for a Mean	299	(1)
Symmetric Population: Uniform Distribution	299	(1)
Skewed Population: Waiting Time	300	(2)
Range of Sample Means	302	(1)
Illustration: GMAT Scores	303	(1)
Sample Size and Standard Error	304	(1)
Illustration: All Possible Samples from a Uniform Population	304	(2)
Confidence Interval for a Mean (μ) with Known σ	306	(3)
What Is a Confidence Interval?	306	(2)
Choosing a Confidence Interval	308	(1)
Is σ Ever Known?	308	(1)
Confidence Interval for a Mean (μ) with Unknown σ	309	(8)
Student's t Distribution	309	(1)
Degrees of Freedom	310	(1)
Comparison of z and t	310	(3)
Confidence Interval Width	313	(1)
A ``Good'' Sample?	313	(1)
More Analysis Needed	314	(1)
Messy Data?	314	(1)
Using Appendix D	315	(1)
Using Excel	315	(1)
Using MegaStat	316	(1)
Using MINITAB	316	(1)
Confidence Interval for a Proportion (π)	317	(9)
Illustration: Internet Hotel Reservations	318	(1)
Applying the CLT	318	(2)
When Is It Safe to Assume Normality?	320	(1)
Standard Error of the Proportion	320	(1)
Confidence Interval for π	321	(1)
Narrowing the Interval?	322	(1)
Using Excel and MegaStat	323	(1)
Small Samples: MINITAB	323	(1)
Polls and Margin of Error	324	(1)
Rule of Three	324	(1)
Very Quick Rule	325	(1)
Advice on Proportions	325	(1)
Sample Size Determination for a Mean	326	(3)
A Myth	326	(1)
Sample Size to Estimate μ	326	(1)
How to Estimate σ	327	(1)
Using LearningStats	328	(1)
Using MegaStat	328	(1)
Caution 1: Units of Measure	328	(1)
Caution 2: Using z	328	(1)
Caution 3: Larger n Is Better	328	(1)
Sample Size Determination for a Proportion	329	(2)
Alternatives	330	(1)
Practical Advice	330	(1)
Using LearningStats	331	(1)
Caution 1: Units of Measure	331	(1)
Caution 2: Finite Population	331	(1)
Confidence Interval for the Difference of Two Means, μ1 -- μ2 (Optional)	331	(3)
Should Sample Sizes Be Equal?	333	(1)
Confidence Interval for the Difference of Two Proportions, π1 -- π2 (Optional)	334	(1)
Confidence Interval for a Population Variance, σ2 (Optional)	335	(11)
Chi-Square Distribution	335	(1)
Confidence Interval for σ	336	(1)
Using LearningStats	336	(1)
Caution: Assumption of Normality	336	(1)
Chapter Summary	337	(9)
One-Sample Hypothesis Tests	346	(48)
Logic of Hypothesis Testing	347	(10)
Process of Science	347	(1)
Who Tests Hypotheses?	348	(1)
Good News	348	(1)
Hypothesis Formulation	348	(1)
Can Hypotheses Be Proved?	348	(1)
Role of Evidence	349	(1)
Types of Error	349	(1)
Statistical Hypothesis Testing	350	(1)
One-Sided Tests	351	(1)
When to Use a One-Sided Test	352	(1)
Decision Rule	352	(1)
Type I Error	352	(2)
Type II Error	354	(1)
Power of a Test	354	(1)
Relationship Between α and β	355	(1)
Consequences of Type II Error	355	(1)
Choice of α	355	(1)
Statistical Significance versus Practical Importance	355	(2)
Testing a Proportion	357	(10)
Critical Value	358	(1)
p-Value Method	359	(1)
Interpreting a p-Value	360	(1)
Two-Tailed Test	360	(1)
Calculating a p-Value for a Two-Tailed Test	361	(1)
Effect of α	362	(1)
Using the p-Value	363	(1)
Effect of a Larger Sample	364	(2)
Small Samples and Non-Normality (Optional)	366	(1)
Testing a Mean: Known Population Variance	367	(5)
Test Statistic	367	(1)
One-Tailed Test	368	(1)
p-Value Method	369	(1)
Two-Tailed Test	369	(1)
Using the p-Value	370	(1)
Analogy to Confidence Intervals	371	(1)
Significance versus Importance	371	(1)
Testing a Mean: Unknown Population Variance	372	(5)
Using Student's t	372	(1)
Sensitivity to α	373	(1)
Using the p-Value	373	(1)
Significance versus Importance	374	(1)
Normality Assumption	374	(1)
Confidence Interval versus Hypothesis Test	374	(1)
Using MegaStat	375	(1)
Large Samples	375	(2)
Power Curves and OC Curves (Optional)	377	(7)
Power Curve for a Mean: An Example	377	(1)
Calculating Power	378	(2)
Effect of Sample Size	380	(1)
Relationship of the Power and OC Curves	380	(1)
Power Curve for Tests of a Proportion	381	(2)
Using LearningStats	383	(1)
Using Visual Statistics	383	(1)
Tests for One Variance (Optional)	384	(10)
Using MegaStat	385	(1)
When to Use Tests for One Variance	386	(1)
Chapter Summary	387	(7)
Two-Sample Hypothesis Tests	394	(44)
Two-Sample Tests	395	(2)
What Is a Two Sample Test?	395	(1)
Basis of Two-Sample Tests	396	(1)
Test Procedure	396	(1)
Comparing Two Proportions	397	(9)
Testing for Zero Difference: π1 = π2	397	(1)
Sample Proportions	397	(1)
Pooled Proportion	397	(1)
Test Statistic	397	(2)
Using the p-Value	399	(1)
Checking Normality	399	(1)
Small Samples	400	(1)
Must Sample Sizes Be Equal?	400	(1)
Using Software for Calculations	400	(1)
Analogy to Confidence Intervals	400	(1)
Separate Confidence Intervals	401	(2)
Testing for Nonzero Difference (Optional)	403	(1)
Test Statistic	403	(1)
Using the p-Value	404	(2)
Comparing Two Means: Independent Samples	406	(8)
Format of Hypotheses	406	(1)
Test Statistic	406	(1)
Case 1: Known Variances	407	(1)
Case 2: Unknown Variances, Assumed Equal	407	(1)
Case 3: Unknown Variances, Assumed Unequal	407	(4)
Which Assumption is Best?	411	(1)
Must Sample Sizes Be Equal?	411	(1)
Large Samples	411	(1)
Caution: Three Issues	411	(3)
Comparing Two Means: Paired Samples	414	(6)
Paired Data	414	(1)
Paired t Test	414	(2)
Excel's Paired Difference Test	416	(1)
Analogy to Confidence Interval	416	(1)
Why Not Treat Paired Data As Independent Samples?	417	(3)
Comparing Two Variances	420	(18)
Format of Hypotheses	420	(1)
The F Test	420	(1)
Critical Values	420	(1)
Illustration: Collision Damage	421	(1)
Comparison of Means	422	(1)
Comparison of Variances: Two-Tailed Test	422	(2)
Comparison of Variances: One-Tailed Test	424	(1)
Excels F Test	425	(1)
Assumptions of the F Test	425	(1)
Significance versus Importance	426	(1)
Chapter Summary	427	(11)
Analysis of Variance	438	(50)
Overview of ANOVA	439	(3)
The Goal: Explaining Variation	439	(1)
Illustration: Manufacturing Defect Rates	440	(1)
Illustration: Hospital Length of Stay	441	(1)
Illustration: Automobile Painting	441	(1)
ANOVA Calculations	441	(1)
ANOVA Assumptions	442	(1)
One-Factor ANOVA (Completely Randomized Model)	442	(8)
Data Format	442	(1)
Hypotheses to Be Tested	443	(1)
One-Factor ANOVA as a Linear Model	443	(1)
Group Means	443	(1)
Partitioned Sum of Squares	443	(2)
Test Statistic	445	(1)
Decision Rule	445	(3)
Using MINITAB	448	(2)
Multiple Comparisons	450	(2)
Tukey's Test	450	(1)
Using MegaStat	451	(1)
Tests for Homogeneity of Variances (Optional)	452	(4)
ANOVA Assumptions	452	(1)
Hartley's Fmax Test	452	(2)
Levene's Test	454	(2)
Two-Factor ANOVA without Replication (Randomized Block Model)	456	(8)
Data Format	456	(1)
Two-Factor ANOVA Model	457	(1)
Hypotheses to Be Tested	457	(1)
Randomized Block Model	457	(1)
Format of Calculation of Nonreplicated Two-Factor ANOVA	458	(2)
Using MegaStat	460	(1)
Multiple Comparisons	461	(1)
Limitations of Two-Factor ANOVA without Replication	461	(3)
Two-Factor ANOVA with Replication (Full Factorial Model)	464	(9)
What Does Replication Accomplish?	464	(1)
Format of Hypotheses	464	(1)
Format of Data	465	(1)
Sources of Variation	465	(3)
Using MegaStat	468	(1)
Interaction Effect	468	(2)
Tukey Tests of Pairs of Means	470	(1)
Significance versus Importance	470	(3)
General Linear Model (Optional)	473	(3)
Higher-Order ANOVA Models	473	(1)
What Is GLM?	474	(2)
Experimental Design: An Overview (Optional)	476	(12)
What Is Experimental Design?	476	(1)
2k Models	476	(1)
Fractional Factorial Designs	476	(1)
Nested or Hierarchical Design	477	(1)
Random Effects Models	477	(1)
Chapter Summary	477	(11)
Bivariate Regression	488	(70)
Visual Displays and Correlation Analysis	489	(11)
Visual Displays	489	(1)
Correlation Coefficient	490	(1)
Tests for Significance	490	(4)
Quick Rule for Significance	494	(1)
Role of Sample Size	494	(1)
Using Excel	494	(2)
Regression: The Next Step?	496	(3)
Autocorrelation	499	(1)
Bivariate Regression	500	(2)
What Is Bivariate Regression?	500	(1)
Model Form	500	(1)
Interpreting a Fitted Regression	501	(1)
Prediction Using Regression	501	(1)
Regression Terminology	502	(3)
Models and Parameters	502	(1)
Estimating a Regression Line by Eye	502	(1)
Fitting a Regression on a Scatter Plot in Excel	502	(1)
Illustration: Piper Cheyenne Fuel Consumption	503	(2)
Ordinary Least Squares Formulas	505	(6)
Slope and Intercept	505	(1)
Illustration: Exam Scores and Study Time	506	(1)
Assessing Fit	507	(1)
Coefficient of Determination	508	(3)
Tests for Significance	511	(6)
Standard Error of Regression	511	(1)
Confidence Intervals for Slope and Intercept	511	(1)
Hypothesis Tests	512	(1)
Test for Zero Slope: Exam Scores	512	(1)
Using Excel: Exam Scores	513	(1)
Using MegaStat: Exam Scores	513	(1)
Using MINITAB: Exam Scores	514	(2)
Using MegaStat: U.S. Income and Taxes	516	(1)
MegaStat's Confidence Intervals: U.S. Income and Taxes	516	(1)
Test for Zero Slope: Tax Data	516	(1)
Analysis of Variance: Overall Fit	517	(5)
Decomposition of Variance	517	(1)
F Statistic for Overall Fit	518	(4)
Confidence and Prediction Intervals for Y	522	(2)
How to Construct an Interval Estimate for Y	522	(1)
Two Illustrations: Exam Scores and Taxes	522	(2)
Quick Rules for Confidence and Prediction Intervals	524	(1)
Violations of Assumptions	524	(7)
Three Important Assumptions	524	(1)
Non-Normal Errors	524	(1)
Histogram of Residuals	524	(1)
Normal Probability Plot	525	(1)
What to Do About Non-Normality?	525	(1)
Heteroscedastic Errors (Nonconstant Variance)	526	(1)
Tests for Heteroscedasticity	526	(1)
What to Do about Heteroscedasticity?	527	(1)
Autocorrelated Errors	527	(1)
Runs Test for Autocorrelation	528	(1)
Durbin-Watson Test	528	(1)
What to Do about Autocorrelation?	529	(2)
Unusual Observations	531	(5)
Standardized Residuals: Excel	531	(1)
Studentized Residuals: MINITAB	531	(1)
Studentized Residuals: MegaStat	532	(1)
Leverage and Influence	532	(2)
Studentized Deleted Residuals	534	(2)
Other Regression Problems (Optional)	536	(22)
Outliers	536	(1)
Model Misspecification	536	(1)
Ill-Conditioned Data	536	(1)
Spurious Correlation	537	(1)
Model Form and Variable Transforms	538	(2)
Regression by Splines	540	(1)
Chapter Summary	541	(17)
Multiple Regression	558	(46)
Multiple Regression	559	(5)
Bivariate or Multivariate?	559	(1)
Regression Terminology	560	(1)
Data Format	560	(1)
Illustration: Home Prices	560	(1)
Logic of Variable Selection	561	(1)
Fitted Regression	561	(1)
Two-Predictor Model	562	(1)
One-Predictor Model	562	(1)
Common Misconceptions about Fit	562	(1)
Regression Modeling	563	(1)
Assessing Overall Fit	564	(2)
F Test for Significance	564	(1)
Coefficient of Determination (R2)	565	(1)
Adjusted R2	565	(1)
How Many Predictors?	565	(1)
Predictor Significance	566	(3)
Hypothesis Tests	566	(1)
Test Statistic	567	(2)
Confidence Intervals for Y	569	(3)
Standard Error	569	(1)
Approximate Confidence and Prediction Intervals for Y	569	(1)
Quick 95 Percent Prediction Interval for Y	570	(2)
Binary Predictors	572	(7)
What Is a Binary Predictor?	572	(1)
Effects of a Binary Predictor	572	(1)
Testing a Binary for Significance	573	(1)
More Than One Binary	574	(1)
What If I Forget to Exclude One Binary?	575	(2)
Regional Binaries	577	(2)
Tests for Nonlinearity and Interaction	579	(2)
Tests for Nonlinearity	579	(1)
Tests for Interaction	580	(1)
Multicollinearity	581	(5)
What Is Multicollinearity?	581	(1)
Variance Inflation	581	(1)
Correlation Matrix	582	(1)
Predictor Matrix Plots	582	(1)
Variance Inflation Factor (VIF)	583	(1)
Rules of Thumb	583	(1)
Are Coefficients Stable?	584	(2)
Violations of Assumptions	586	(4)
Non-Normal Errors	586	(1)
Nonconstant Variance (Heteroscedasticity)	586	(2)
Autocorrelation (Optional)	588	(1)
Unusual Observations	588	(2)
Other Regression Topics	590	(14)
Outliers: Causes and Cures	590	(1)
Missing Predictors	590	(1)
Ill-Conditioned Data	590	(1)
Significance in Large Samples	591	(1)
Model Specification Errors	591	(1)
Missing Data	591	(1)
Binary Dependent Variable	591	(1)
Stepwise and Best Subsets Regression	591	(1)
Chapter Summary	592	(12)
Time-Series Analysis	604	(52)
Time-Series Components	605	(5)
Time-Series Data	605	(1)
Stocks and Flows	606	(1)
Periodicity	607	(1)
Additive versus Multiplicative Models	607	(1)
A Graphical View	607	(1)
Trend	607	(2)
Cycle	609	(1)
Seasonal	609	(1)
Irregular	609	(1)
Trend Forecasting	610	(13)
Three Trend Models	610	(1)
Linear Trend Model	610	(1)
Illustration: Linear Trend	611	(1)
Linear Trend Calculations	611	(1)
Forecasting a Linear Trend	612	(1)
Linear Trend: Calculating R2	612	(1)
Exponential Trend Model	612	(1)
When to Use the Exponential Model	613	(1)
Illustration: Exponential Trend	613	(1)
Exponential Trend Calculations	614	(1)
Forecasting an Exponential Trend	615	(1)
Exponential Trend: Calculating R2	615	(1)
Quadratic Trend Model	615	(1)
Illustration: Quadratic Trend	616	(1)
Using Excel for Trend Fitting	617	(1)
Trend-Fitting Criteria	617	(6)
Assessing Fit	623	(2)
Five Measures of Fit	623	(2)
Moving Averages	625	(2)
Trendless or Erratic Data	625	(1)
Trailing Moving Average (TMA)	625	(1)
Centered Moving Average (CMA)	626	(1)
Using Excel for a TMA	626	(1)
Exponential Smoothing	627	(6)
Forecast Updating	627	(1)
Smoothing Constant (α)	628	(1)
Choosing the Value of α	628	(1)
Initializing the Process	628	(2)
Using MINITAB	630	(1)
Using Excel	631	(1)
Smoothing with Trend and Seasonality	631	(2)
Seasonality	633	(6)
When and How to Deseasonalize	633	(1)
Illustration of Calculations	633	(2)
Using MINITAB to Deseasonalize	635	(1)
Seasonal Forecasts Using Binary Predictors	636	(3)
Forecasting: Final Thoughts	639	(17)
Role of Forecasting	639	(1)
Behavioral Aspects of Forecasting	639	(1)
Forecasts Are Always Wrong	639	(1)
Chapter Summary	640	(16)
Chi-Square Tests	656	(42)
Chi-Square Test for Independence	657	(10)
Contingency Tables	657	(1)
Chi-Square Test	658	(1)
Chi-Square Distribution	658	(1)
Expected Frequencies	659	(1)
Illustration of the Chi-Square Calculations	660	(2)
Test of Two Proportions	662	(1)
Small Expected Frequencies	663	(1)
Cross-Tabulating Raw Data	663	(1)
3-Way Tables and Higher	664	(3)
Chi-Square Tests for Goodness-of-Fit	667	(2)
Purpose of the Test	667	(1)
Hypotheses for GOF	667	(1)
Test Statistic and Degrees of Freedom for GOF	667	(1)
Data-Generating Situations	668	(1)
Mixtures: A Problem	668	(1)
Eyeball Tests	668	(1)
Small Expected Frequencies	668	(1)
Uniform Goodness-of-Fit Test	669	(4)
Multinomial Distribution	669	(1)
Uniform Distribution	669	(1)
Uniform GOF Test: Grouped Data	669	(1)
Uniform GOF Test: Raw Data	670	(3)
Poisson Goodness-of-Fit Test	673	(6)
Poisson Data-Generating Situations	673	(1)
Poisson Goodness-of-Fit Test	674	(1)
Poisson GOF Test: Tabulated Data	674	(2)
Poisson GOF Test: Raw Data	676	(3)
Normal Chi-Square Goodness-of-Fit Test	679	(5)
Normal Data-Generating Situations	679	(1)
Method 1: Standardizing the Data	679	(1)
Method 2: Equal Bin Widths	679	(1)
Method 3: Equal Expected Frequencies	680	(1)
Application: Quality Management	680	(4)
ECDF Tests (Optional)	684	(14)
Kolmogorov-Smirnov and Lilliefors Tests	684	(1)
Illustrations: Lottery Numbers and Kiss Weights	684	(1)
Anderson-Darling Test	685	(1)
Chapter Summary	686	(12)
Nonparametric Tests	698	(32)
Why Use Nonparametric Tests?	699	(1)
One-Sample Runs Test	700	(2)
Application: Quality Inspection	700	(2)
Small Samples	702	(1)
Wilcoxon Signed-Rank Test	702	(4)
Application: Median versus Benchmark	703	(1)
Application: Paired Data	704	(2)
Mann-Whitney Test	706	(3)
Application: Restaurant Quality	706	(3)
Kruskal-Wallis Test for Independent Samples	709	(5)
Application: Employee Absenteeism	709	(5)
Friedman Test for Related Samples	714	(2)
Test Statistic	714	(1)
Application: Braking Effectiveness	714	(2)
Spearman Rank Correlation Test	716	(14)
Application: Calories and Fat	717	(1)
Correlation versus Causation	718	(2)
Chapter Summary	720	(10)
Quality Management	730	(86)
Quality and Variation	731	(2)
What Is Quality?	731	(1)
Productivity and Quality	732	(1)
Processes and Quality Metrics	732	(1)
Variance Reduction	732	(1)
Common Cause versus Special Cause	733	(1)
Role of Management	733	(1)
Role of Statisticians	733	(1)
Customer Orientation	733	(1)
Who Is a Customer?	733	(1)
Measuring Quality	734	(1)
Behavioral Aspects of Quality	734	(1)
Blame versus Solutions	734	(1)
Employee Involvement	735	(1)
Pioneers in Quality Management	735	(2)
Brief History of Quality Control	735	(1)
W. Edwards Deming	736	(1)
Other Influential Thinkers	737	(1)
Quality Improvement	737	(3)
Total Quality Management (TQM)	737	(1)
Business Process Redesign (BPR)	738	(1)
Statistical Quality Control (SQC)	738	(1)
Statistical Process Control (SPC)	738	(1)
Continuous Quality Improvement (CQI)	739	(1)
Control Charts: Overview	740	(1)
What Is a Control Chart?	740	(1)
Two Data Types	740	(1)
Three Common Control Charts	740	(1)
Control Charts for a Mean	741	(8)
x Charts: Bottle-Filling Example	741	(1)
Control Limits: Known μ and σ	741	(2)
Empirical Control Limits	743	(1)
Control Chart Factors	743	(1)
Detecting Abnormal Patterns	744	(2)
Histograms	746	(3)
Control Charts for a Range	749	(1)
Control Limits for the Range	749	(1)
Patterns In Control Charts	750	(2)
The Overadjustment Problem	750	(1)
Abnormal Patterns	750	(1)
Symptoms and Assignable Causes	751	(1)
Process Capability	752	(3)
Cp Index	752	(1)
Cpk Index	753	(2)
Bottle Filling Revisited	755	(1)
Other Control Charts	755	(5)
Attribute Data: p Charts	755	(2)
Application: Emergency Patients	757	(1)
Other Standard Control Charts (s, c, np, I, MR)	757	(2)
Ad Hoc Charts	759	(1)
Additional Quality Topics (Optional)	760	(14)
Acceptance Sampling	760	(1)
Supply-Chain Management	760	(1)
Quality and Design	761	(1)
Taguchi's Robust Design	761	(1)
Six Sigma and Lean Six Sigma	761	(1)
ISO 9000	762	(1)
Malcolm Baldrige Award	762	(1)
Advanced MINITAB Features	762	(1)
Future of Statistical Process Control	762	(1)
Chapter Summary	763	(11)
Simulation (On Student CD-ROM)
APPENDIXES
Exact Binomial Probabilities	774	(2)
Exact Poisson Probabilities	776	(3)
Standard Normal Areas	779	(1)
Cumulative Standard Normal Distribution	780	(2)
Student's t Critical Values	782	(1)
Chi-Square Critical Values	783	(1)
Critical Values of F	784	(8)
Solutions to Odd-Numbered Exercises	792	(24)
Photo Credits	816	(1)
Index	817