Applied statistics in business and economics:
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boston [u.a.]
McGraw-Hill/Irwin
c2007
|
| Series: | The McGraw-Hill/Irwin series operations and decision sciences
|
| Subjects: | |
| Online Access: | Table of contents only Publisher description Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | xxiii, 834 p. ill. (some col.) 1 CD-ROM (12 cm) |
| ISBN: | 9780072966930 9780073215754 0073215759 0072966939 0072966963 0072966947 |
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MARC
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| 100 | 1 | |a Doane, David P. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Applied statistics in business and economics |c David P. Doane, Lori E. Seward |
| 264 | 1 | |a Boston [u.a.] |b McGraw-Hill/Irwin |c c2007 | |
| 300 | |a xxiii, 834 p. |b ill. (some col.) |e 1 CD-ROM (12 cm) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a The McGraw-Hill/Irwin series operations and decision sciences | |
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Administración - Métodos estadísticos | |
| 650 | 4 | |a Economía - Métodos estadísticos | |
| 650 | 4 | |a Estadística | |
| 650 | 4 | |a Estadística comercial | |
| 650 | 4 | |a Statistik | |
| 650 | 4 | |a Wirtschaft | |
| 650 | 4 | |a Commercial statistics | |
| 650 | 4 | |a Management |x Statistical methods | |
| 650 | 4 | |a Economics |x Statistical methods | |
| 650 | 4 | |a Statistics | |
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| 700 | 1 | |a Seward, Lori E. |e Verfasser |4 aut | |
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Record in the Search Index
| _version_ | 1804136556442681344 |
|---|---|
| adam_text | BRIEF CONTEF
CHAPTER ONE
Overview of Statistics 2
CHAPTER TWO
Data Collection 22
CHAPTER THREE
Describing Data Visually 58
CHAPTER FOUR
Descriptive Statistics 112
CHAPTER FIVE
Probability 168
CHAPTER SIX
Discrete Distributions 208
CHAPTER SEVEN
Continuous Distributions 252
CHAPTER EIGHT
Sampling Distributions and Estimation 292
CHAPTER NINE
One Sample Hypothesis Tests 346
CHAPTER TEN
Two Sample Hypothesis Tests 394
CHAPTER ELEVEN
Analysis of Variance 438
CHAPTER TWELVE
Bivariate Regression 488
SITS
CHAPTER THIRTEEN
Multiple Regression 558
CHAPTER FOURTEEN
Time Series Analysis 604
CHAPTER FIFTEEN
Chi Square Tests 656
CHAPTER SIXTEEN
Nonparametric Tests 698
CHAPTER SEVENTEEN
Quality Management 730
CHAPTER EIGHTEEN
Simulation (On Student CD ROM)
APPENDIXES
A Exact Binomial Probabilities 774
B Exact Poisson Probabilities 776
C 1 Standard Normal Areas 779
C 2 Cumulative Standard Normal
Distribution 780
D Student s / Critical Values 782
E Chi Square Critical Values 783
F Critical Values of F 784
G Solutions to Odd Numbered Exercises 792
PHOTO CREDITS 816
INDEX 817
CHAPTER ONE
Overview of Statistics 2
1.1 What is Statistics? 3
1.2 Why Study Statistics? 4
Communication 4
Computer Skills 4
Information Management 4
Technical Literacy 4
Career Advancement 4
Quality Improvement 4
1.3 Uses of Statistics 5
Auditing 5
Marketing 5
Health Care 5
Quality Control 5
Purchasing 6
Medicine 6
Forecasting 6
Product Warranty 6
1.4 Statistical Challenges 6
Working with Imperfect Data 6
Dealing with Practical Constraints 7
Upholding Ethical Standards 7
Using Consultants 7
1.5 Writing and Presenting Reports 9
Rules for Power Writing 9
Writing Style 9
Spelling and Grammar 10
Organizing a Technical Report 10
Writing an Executive Summary 10
Tables and Graphs 11
Rules for Presenting Oral Reports 11
The Three Ps 12
1.6 Statistical Pitfalls 14
Pitfall 1: Making Conclusions about a Large
Population from a Small Sample 14
Pitfall 2: Making Conclusions
from Nonrandom Samples 14
Pitfall 3: Attaching Importance
to Rare Observations from Large Samples 14
Pitfall 4: Using Poor Survey Methods 14
Pitfall 5: Assuming a Causal Link
Based Only on Observed Association 15
Pitfall 6: Making Generalizations
about Individuals from Observations
about Groups 15
Pitfall 7: Unconscious Bias 15
Pitfall 8: Attaching Practical Importance
to Every Statistically Significant Study Result 15
1.7 Statistics: An Evolving Field 16
Chapter Summary 16
CHAPTER TWO
Data Collection 22
2.1 Definitions 23
Subjects, Variables, and Data Sets 23
Data Types 24
2.2 Level of Measurement 26
Nominal Measurement 26
Ordinal Measurement 27
Interval Measurement 27
Ratio Measurement 28
Changing Data by Recoding 29
2.3 Time Series versus Cross Sectional Data 30
Time Series Data 30
Cross Sectional Data 30
2.4 Sampling Concepts 31
Sample or Census? 31
Parameters and Statistics 32
Target Population 32
Finite or Infinite? 33
2.5 Sampling Methods 33
Simple Random Sample 33
Random Number Tables 35
Setting Up a Rule 35
With or Without Replacement? 35
Computer Methods 36
Row/Column Data Arrays 36
Randomizing a List 37
Systematic Sample 37
Stratified Sample 39
Applications of Stratified Sampling 39
Cluster Sample 39
Judgment Sample 40
Convenience Sample 40
Sample Size 41
2.6 Data Sources 42
2.7 Survey Research 43
Survey Types 43
Response Rates 43
Getting Advice 44
Questionnaire Design 44
Question Wording 44
Coding and Data Screening 46
Sources of Error 46
Data File Format 47
Chapter Summary 49
CHAPTER THREE
Describing Data Visually 58
3.1 Visual Description 59
Measurement 60
Sorting 60
3.2 Dot Plots 61
Small Sample: Home Prices 61
Comparing Groups 63
3.3 Frequency Distributions and Histograms 65
Bins and Bin Limits 65
Constructing a Frequency Distribution 65
Histograms 66
Excel Histograms 66
MegaStat Histograms 68
MINITAB Histograms 68
Modal Class 69
Shape 69
3.4 Line Charts 72
Simple Line Charts 72
Grid Lines 73
Log Scales 73
Tips for Effective Line Charts 75
3.5 Bar Charts 76
Plain Bar Charts 76
3 D and Novelty Bar Charts 76
Pareto Charts 77
Stacked Bar Chart 78
Bar Charts for Time Series Data 78
Tips for Effective Bar Charts 79
3.6 Scatter Plots 80
Policy Making 82
Degree of Association 82
Making a Scatter Plot in Excel 84
3.7 Tables 86
Tips for Effective Tables 87
3.8 Pie Charts 87
An Oft Abused Chart 87
Pie Chart Options 87
3.9 Effective Excel Charts 90
Chart Wizard 90
Embellished Charts 91
3.10 Maps and Pictograms 94
Spatial Variation and GIS 94
Pictograms 95
3.11 Deceptive Graphs 95
Error 1: Nonzero Origin 95
Error 2: Elastic Graph Proportions 96
Error 3: Dramatic Title 96
Error 4: Distracting Pictures 96
Error 5: Authority Figures 97
Error 6: 3 D and Rotated Graphs 97
Error 7: Missing Axis Demarcations 97
Error 8: Missing Measurement Units
or Definitions 97
Error 9: Vague Source 97
Error 10: Complex Graphs 97
Error 11: Gratuitous Effects 98
Error 12: Estimated Data 98
Error 13: Area Trick 98
Final Advice 98
Further Challenges 99
Chapter Summary 99
CHAPTER FOUR
Descriptive Statistics 112
4.1 Numerical Description 113
Preliminary Analysis 114
Sorting 114
Visual Displays 114
Descriptive Statistics in Excel 116
Descriptive Statistics in MegaStat 117
4.2 Central Tendency 119
Mean 119
Characteristics of the Mean 119
Median 120
Characteristics of the Median 121
Mode 121
Skewness 123
Geometric Mean 127
Growth Rates 127
Midrange 128
Trimmed Mean 128
4.3 Dispersion 130
Range 131
Variance 131
Standard Deviation 131
Calculating a Standard Deviation 132
Characteristics of the Standard
Deviation 133
Coefficient of Variation 133
Mean Absolute Deviation 133
Central Tendency versus Dispersion:
Manufacturing 135
Central Tendency and Dispersion:
Job Performance 135
4.4 Standardized Data 136
Chebyshev s Theorem 136
The Empirical Rule 137
Unusual Observations 137
Defining a Standardized Variable 138
Outliers 139
Estimating Sigma 139
4.5 Percentiles and Quartiles 141
Percentiles 141
Quartiles 141
Method of Medians 142
Formula Method 143
Excel Quartiles 143
Dispersion Using Quartiles 144
Midhinge 144
Midspread (Interquartile Range) 145
Coefficient ofQuartile Variation 145
4.6 Box Plots 145
Fences and Unusual Data Values 146
4.7 Grouped Data 147
Nature and Grouped Data 147
Mean and Standard Deviation 148
Accuracy Issues 148
Properties of Grouped Estimates 149
4.8 Skewness and Kurtosis 149
Skewness 149
Kurtosis 150
Chapter Summary 152
S CHAPTER FIVE
Probability 168
5.1 Random Experiments 169
Sample Space 169
Events 170
5.2 Probability 171
Definitions 171
What Is Probability ? 171
Empirical Approach 172
Law of Large Numbers 172
Practical Issues for Actuaries 172
Classical Approach 173
Subjective Approach 174
5.3 Rules of Probability 174
Complement of an Event I74
Odds of an Event 175
Union of Two Events 175
Intersection of Two Events 175
General Law of Addition 176
Mutually Exclusive Events 177
Special Law of Addition 177
Collectively Exhaustive Sets 177
Forced Dichotomy 177
Conditional Probability 178
5.4 Independent Events 180
Dependent Events 180
Actuaries Again 181
Multiplication Law for Independent Events 181
The Five Nines Rule 181
How Much Redundancy Is Needed? 182
Applications of Redundancy 182
5.5 Contingency Tables 183
What Is a Contingency Table? 183
Marginal Probabilities 184
Joint Probabilities 184
Conditional Probabilities 185
Independence 185
Relative Frequencies 186
How Do We Get a Contingency Table? 187
5.6 Tree Diagrams 190
What Is a Tree? 190
5.7 Bayes s Theorem (Optional) 191
How Bayes s Theorem Works 191
General Form of Bayes s Theorem 192
5.8 Counting Rules (Optional) 196
Fundamental Rule of Counting 196
Factorials 197
Permutations 198
Combinations 198
Chapter Summary 200
CHAPTER SIX
Discrete Distributions 208
6.1 Probability Models 209
6.2 Discrete Distributions 209
Random Variables 209
Probability Distributions 210
Expected Value 211
Application: Life Insurance 212
Application: Raffle Tickets 212
A ctuarial Fairness 213
Variance and Standard Deviation 213
What Is a PDF or CDF? 214
6.3 Uniform Distribution 215
Characteristics of the Uniform
Distribution 215
Application: Pumping Gas 217
Uniform Random Integers 218
Application: Copier Codes 218
Uniform Model in LearningStats 219
6.4 Bernoulli Distribution 219
Bernoulli Experiments 219
6.5 Binomial Distribution 220
Characteristics of the Binomial Distribution 220
Binomial Shape 221
Application: Uninsured Patients 222
Using the Binomial Formula 222
Using Tables: Appendix A 224
Compound Events 224
Binomial Probabilities: Excel 225
Binomial Probabilities: MegaStat 225
Binomial Probabilities: Visual Statistics 225
Binomial Probabilities: LearningStats 225
Binomial Random Data 225
Recognizing Binomial Applications 225
6.6 Poisson Distribution 228
Poisson Processes 228
Characteristics of the Poisson Distribution 229
Using the Poisson Formula 230
Compound Events 232
Poisson Probabilities: Tables (Appendix B) 232
Poisson Probabilities: Excel 232
Poisson Probabilities: Visual Statistics 232
Recognizing Poisson Applications 233
Poisson Approximation to Binomial
(Optional) 234
6.7 Hypergeometric Distribution 235
Characteristics of the Hypergeometric
Distribution 235
Using the Hypergeometric Formula 236
Hypergeometric Probabilities: Excel 237
Hypergeometric Probabilities: Visual
Statistics 237
Hypergeometric Probabilities: LearningStats 238
Recognizing Hypergeometric Applications 238
Binomial Application to the
Hypergeometric (Optional) 239
6.8 Geometric Distribution (Optional) 240
Characteristics of the Geometric Distribution 240
Using LearningStats 241
6.9 Transformations of Random Variables
(Optional) 242
Linear Transformation 242
Application: Exam Scores 242
Application: Total Cost 242
Sums of Random Variables 243
Application: Gasoline Expenses 243
Application: Project Scheduling 243
Chapter Summary 244
CHAPTER SEVEN
Continuous Distributions 252
7.1 Continuous Variables 253
Events as Intervals 253
7.2 Describing a Continuous Distribution 253
PDFs and CDFs 253
Probabilities as Areas 254
Expected Value and Variance 255
Oh My, Calculus? 255
7.3 Uniform Continuous Distribution 255
Characteristics of the Uniform Distribution 255
Special Case: Unit Rectangular 257
Uses of the Uniform Model 258
7.4 Normal Distribution 258
Characteristics of the Normal Distribution 258
What Is Normal? 260
7.5 Standard Normal Distribution 261
Characteristics of the Standard Normal 261
Normal Areas from Appendix C l 262
Basis for the Empirical Rule 263
Normal Areas from Appendix C 2 264
Finding zfor a Given Area 265
Finding Normal Areas with Excel 267
Finding Areas by Using Standardized
Variables 267
Inverse Normal 269
Using Excel Without Standardizing 270
Normal Random Data (Optional) 271
7.6 Normal Approximation to the Binomial
(Optional) 273
When Is Approximation Needed? 273
7.7 Normal Approximation to the Poisson
(Optional) 276
When Is Approximation Needed? 2 76
7.8 Exponential Distribution 277
Characteristics of the Exponential Distribution 277
Inverse Exponential 279
Mean Time Between Events 280
Using Excel 281
7.9 Triangular Distribution (Optional) 282
Characteristics of the Triangular Distribution 282
Special Case: Symmetric Triangular 284
Uses of the Triangular 284
Chapter Summary 284
I CHAPTER EIGHT
Sampling Distributions and Estimation 292
8.1 Sampling Variation 293
8.2 Estimators and Sampling Distributions 295
Some Terminology 295
Sampling Distributions 295
Bias 295
Efficiency 297
Consistency 297
8.3 Sample Mean and the Central Limit
Theorem 298
Central Limit Theorem for a Mean 299
Symmetric Population: Uniform Distribution 299
Skewed Population: Waiting Time 300
Range of Sample Means 302
Illustration: GMATScores 303
Sample Size and Standard Error 304
Illustration: All Possible Samples
from a Uniform Population 304
8.4 Confidence Interval for a Mean (fi) with
Known a 306
What Is a Confidence Interval? 306
Choosing a Confidence Interval 308
Is a Ever Known ? 3 08
8.5 Confidence Interval for a Mean (/z) with
Unknown a 309
Student s t Distribution 309
Degrees of Freedom 310
Comparison ofz and t 310
Confidence Interval Width 313
A Good Sample? 313
More A nalysis Needed 314
Messy Data? 314
Using Appendix D 315
Using Excel 315
Using MegaStat 316
Using MINITAB 316
8.6 Confidence Interval for a Proportion (n) 317
Illustration: Internet Hotel Reservations 318
Applying the CLT 318
When Is It Safe to Assume Normality? 320
Standard Error of the Proportion 320
Confidence Interval for n 321
Narrowing the Interval? 322
Using Excel and MegaStat 323
Small Samples: MINITAB 323
Polls and Margin of Error 324
Rule of Three 324
Very Quick Rule 325
Advice on Proportions 325
8.7 Sample Size Determination for a Mean 326
A Myth 326
Sample Size to Estimate n 326
How to Estimate a 327
Using LearningStats 328
Using MegaStat 328
Caution 1: Units of Measure 328
Caution 2: Using z 328
Caution 3: Larger n Is Better 328
8.8 Sample Size Determination for a Proportion 329
Alternatives 330
Practical Advice 330
Using LearningStats 331
Caution I: Units of Measure 331
Caution 2: Finite Population 331
8.9 Confidence Interval for the Difference of Two
Means, /j.l /i2 (Optional) 331
Should Sample Sizes Be Equal? 333
8.10 Confidence Interval for the Difference of Two
Proportions, jt, — tt2 (Optional) 334
8.11 Confidence Interval for a Population Variance,
o2 (Optional) 335
Chi Square Distribution 335
Confidence Interval for a 336
Using LearningStats 336
Caution: Assumption of Normality 336
Chapter Summary 337
CHAPTER NINE
One Sample Hypothesis Tests 346
9.1 Logic of Hypothesis Testing 347
Process of Science 347
Who Tests Hypotheses? 348
Good News 348
Hypothesis Formulation 348
Can Hypotheses Be Proved? 348
Role of Evidence 349
Types of Error 349
Statistical Hypothesis Testing 350
One Sided Tests 351
When to Use a One Sided Test 352
Decision Rule 352
Type I Error 352
Type II Error 354
Power of a Test 354
Relationship Between a and 8 355
Consequences of Type II Error 355
Choice of a 355
Statistical Significance versus Practical
Importance 355
9.2 Testing a Proportion 357
Critical Value 358
p Value Method 359
Interpreting a p Value 360
Two Tailed Test 360
Calculating a p Value for a Two Tailed Test 361
Effect of a 362
Using the p Value 363
Effect of a Larger Sample 364
Small Samples and Non Normality (Optional) 366
9.3 Testing a Mean: Known Population Variance 367
Test Statistic 367
One Tailed Test 368
p Value Method 369
Two Tailed Test 369
Using the p Value 3 70
Analogy to Confidence Intervals 371
Significance versus Importance 371
9.4 Testing a Mean: Unknown Population
Variance 372
Using Student s t 372
Sensitivity to a 373
Using the p Value 3 73
Significance versus Importance 374
Normality Assumption 3 74
Confidence Interval versus Hypothesis Test 374
Using MegaStat 375
Large Samples 3 75
9.5 Power Curves and OC Curves (Optional) 377
Power Curve for a Mean: An Example 377
Calculating Power 3 78
Effect of Sample Size 380
Relationship of the Power and OC Curves 380
Power Curve for Tests of a Proportion 381
Using LearningStats 383
Using Visual Statistics 383
9.6 Tests for One Variance (Optional) 384
Using MegaStat 385
When to Use Tests for One Variance 386
Chapter Summary 387
» CHAPTER TEN
Two Sample Hypothesis Tests 394
10.1 Two Sample Tests 395
What Is a Two Sample Test? 395
Basis of Two Sample Tests 396
Test Procedure 396
10.2 Comparing Two Proportions 397
Testing for Zero Difference: it, = n2 397
Sample Proportions 397
Pooled Proportion 397
Test Statistic 397
Using the p Value 399
Checking Normality 399
Small Samples 400
Must Sample Sizes Be Equal? 400
Using Software for Calculations 400
Analogy to Confidence Intervals 400
Separate Confidence Intervals 401
Testing for Nonzero Difference (Optional) 403
Test Statistic 403
Using thep Value 404
10.3 Comparing Two Means: Independent
Samples 406
Format of Hypotheses 406
Test Statistic 406
Case 1: Known Variances 407
Case 2: Unknown Variances, Assumed Equal 407
Case 3: Unknown Variances, Assumed Unequal 407
Which Assumption is Best? 411
Must Sample Sizes Be Equal? 411
Large Samples 411
Caution: Three Issues 411
10.4 Comparing Two Means: Paired Samples 414
Paired Data 414
Paired t Test 414
Excel s Paired Difference Test 416
Analogy to Confidence Interval 416
Why Not Treat Paired Data As Independent
Samples? 417
10.5 Comparing Two Variances 420
Format of Hypotheses 420
The F Test 420
Critical Values 420
Illustration: Collision Damage 421
Comparison of Means 422
Comparison of Variances: Two Tailed Test 422
Comparison of Variances: One Tailed Test 424
Excel s F Test 425
Assumptions of the F Test 425
Significance versus Importance 426
Chapter Summary 427
CHAPTER ELEVEN
Analysis of Variance 438
11.1 Overview of ANOVA 439
The Goal: Explaining Variation 439
Illustration: Manufacturing Defect Rates 440
Illustration: Hospital Length of Stay 441
Illustration: Automobile Painting 441
ANOVA Calculations 441
ANOVA Assumptions 442
11.2 One Factor ANOVA (Completely Randomized
Model) 442
Data Format 442
Hypotheses to Be Tested 443
One Factor ANOVA as a Linear Model 443
Group Means 443
Partitioned Sum of Squares 443
Test Statistic 445
Decision Rule 445
Using MINITAB 448
11.3 Multiple Comparisons 450
Tukey sTest 450
Using MegaStat 451
11.4 Tests for Homogeneity of Variances
(Optional) 452
ANOVA Assumptions 452
Hartley s Fmax Test 452
Levene s Test 454
11.5 Two Factor ANOVA without Replication
(Randomized Block Model) 456
Data Format 456
Two Factor ANOVA Model 457
Hypotheses to Be Tested 457
Randomized Block Model 45 7
Format of Calculation ofNonreplicated
Two Factor ANOVA 458
Using MegaStat 460
Multiple Comparisons 461
Limitations of Two Factor ANOVA
without Replication 461
11.6 Two Factor ANOVA with Replication
(Full Factorial Model) 464
What Does Replication Accomplish ? 464
Format of Hypotheses 464
Format of Data 465
Sources ofVariation 465
Using MegaStat 468
Interaction Effect 468
Tukey Tests of Pairs of Means 470
Significance versus Importance 470
11.7 General Linear Model (Optional) 473
Higher Order ANOVA Models 473
WhatlsGLM? 474
11.8 Experimental Design: An Overview (Optional) 476
What Is Experimental Design? 476
2k Models 476
Fractional Factorial Designs 4 76
Nested or Hierarchical Design 477
Random Effects Models 477
Chapter Summary 477
CHAPTER TWELVE
Bivariate Regression 488
12.1 Visual Displays and Correlation Analysis 489
Visual Displays 489
Correlation Coefficient 490
Tests for Significance 490
Quick Rule for Significance 494
Role of Sample Size 494
Using Excel 494
Regression: The Next Step? 496
Autocorrelation 499
12.2 Bivariate Regression 500
What Is Bivariate Regression ? 500
Model Form 500
Interpreting a Fitted Regression 501
Prediction Using Regression 501
12.3 Regression Terminology 502
Models and Parameters 502
Estimating a Regression Line by Eye 502
Fitting a Regression on a Scatter Plot in
Excel 502
Illustration: Piper Cheyenne Fuel
Consumption 503
12.4 Ordinary Least Squares Formulas 505
Slope and Intercept 505
Illustration: Exam Scores and Study Time 506
Assessing Fit 507
Coefficient of Determination 508
12.5 Tests for Significance 511
Standard Error of Regression 511
Confidence Intervals for Slope and Intercept 511
Hypothesis Tests 512
Test for Zero Slope: Exam Scores 512
Using Excel: Exam Scores 513
Using MegaStat: Exam Scores 513
Using MINITAB: Exam Scores 514
Using MegaStat: U.S. Income and Taxes 516
MegaStat s Confidence Intervals: U.S. Income
and Taxes 516
Test for Zero Slope: Tax Data 516
12.6 Analysis of Variance: Overall Fit 517
Decomposition of Variance 517
F Statistic for Overall Fit 518
12.7 Confidence and Prediction Intervals for Y 522
How to Construct an Interval Estimate for Y 522
Two Illustrations: Exam Scores and Taxes 522
Quick Rules for Confidence and Prediction
Intervals 524
12.8 Violations of Assumptions 524
Three Important Assumptions 524
Non Normal Errors 524
Histogram of Residuals 524
Normal Probability Plot 525
What to Do About Non Normality? 525
Heteroscedastic Errors (Nonconstant Variance) 526
Tests for Heteroscedasticity 526
What to Do about Heteroscedasticity? 527
A utocorrelated Errors 527
Runs Test for Autocorrelation 528
Durbin Watson Test 528
What to Do about Autocorrelation ? 529
12.9 Unusual Observations 531
Standardized Residuals: Excel 531
Studentized Residuals: MINITAB 531
Studentized Residuals: MegaStat 532
Leverage and Influence 532
Studentized Deleted Residuals 534
12.10 Other Regression Problems (Optional) 536
Outliers 536
Model Misspecification 536
Ill Conditioned Data 536
Spurious Correlation 537
Model Form and Variable Transforms 538
Regression by Splines 540
Chapter Summary 541
i CHAPTER THIRTEEN
Multiple Regression 558
13.1 Multiple Regression 559
Bivariate or Multivariate? 559
Regression Terminology 560
Data Format 560
Illustration: Home Prices 560
Logic of Variable Selection 561
Fitted Regression 561
Two Predictor Model 562
One Predictor Model 562
Common Misconceptions about Fit 562
Regression Modeling 563
13.2 Assessing Overall Fit 564
F Test for Significance 564
Coefficient of Determination (R2) 565
Adjusted R2 565
How Many Predictors? 565
13.3 Predictor Significance 566
Hypothesis Tests 566
Test Statistic 567
13.4 Confidence Intervals for Y 569
Standard Error 569
Approximate Confidence and Prediction
Intervals for Y 569
Quick 95 Percent Prediction Interval for Y 570
13.5 Binary Predictors 572
What Is a Binary Predictor? 572
Effects of a Binary Predictor 572
Testing a Binary for Significance 573
More Than One Binary 574
What If I Forget to Exclude One Binary 5 75
Regional Binaries 577
13.6 Tests for Nonlinearity and Interaction 579
Tests for Nonlinearity 579
Tests for Interaction 580
13.7 Multicollinearity 581
What Is Multicollinearity? 581
Variance Inflation 581
Correlation Matrix 582
Predictor Matrix Plots 582
Variance Inflation Factor (VIF) 583
Rules of Thumb 583
Are Coefficients Stable? 584
13.8 Violations of Assumptions 586
Non Normal Errors 586
Nonconstant Variance (Heteroscedasticity) 586
Autocorrelation (Optional) 588
Unusual Observations 588
13.9 Other Regression Topics 590
Outliers: Causes and Cures 590
Missing Predictors 590
Ill Conditioned Data 590
Significance in Large Samples 591
Model Specification Errors 591
Missing Data 591
Binary Dependent Variable 591
Stepwise and Best Subsets Regression 591
Chapter Summary 592
CHAPTER FOURTEEN
Time Series Analysis 604
14.1 Time Series Components 605
Time Series Data 605
Stocks and Flows 606
Periodicity 607
Additive versus Multiplicative Models 607
A Graphical View 607
Trend 607
Cycle 609
Seasonal 609
Irregular 609
14.2 Trend Forecasting 610
Three Trend Models 610
Linear Trend Model 610
Illustration: Linear Trend 611
Linear Trend Calculations 611
Forecasting a Linear Trend 612
Linear Trend: Calculating R2 612
Exponential Trend Model 612
When to Use the Exponential Model 613
Illustration: Exponential Trend 613
Exponential Trend Calculations 614
Forecasting an Exponential Trend 615
Exponential Trend: Calculating R2 615
Quadratic Trend Model 615
Illustration: Quadratic Trend 616
Using Excel for Trend Fitting 617
Trend Fitting Criteria 617
14.3 Assessing Fit 623
Five Measures of Fit 623
14.4 Moving Averages 625
Trendless or Erratic Data 625
Trailing Moving Average (TMA) 625
Centered Moving Average (CMA) 626
Using Excel for a TMA 626
14.5 Exponential Smoothing 627
Forecast Updating 627
Smoothing Constant (a) 628
Choosing the Value of a 628
Initializing the Process 628
Using MINITAB 630
Using Excel 631
Smoothing with Trend and Seasonality 631
14.6 Seasonality 633
When and How to Deseasonalize 633
Illustration of Calculations 633
Using MINITAB to Deseasonalize 635
Seasonal Forecasts Using Binary Predictors 636
14.7 Forecasting: Final Thoughts 639
Role of Forecasting 639
Behavioral Aspects of Forecasting 639
Forecasts Are Always Wrong 639
Chapter Summary 640
CHAPTER FIFTEEN
Chi Square Tests 656
15.1 Chi Square Test for Independence 657
Contingency Tables 657
Chi Square Test 658
Chi Square Distribution 658
Expected Frequencies 659
Illustration of the Chi Square Calculations 660
Test of Two Proportions 662
Small Expected Frequencies 663
Cross Tabulating Raw Data 663
3 Way Tables and Higher 664
15.2 Chi Square Tests for Goodness of Fit 667
Purpose of the Test 667
Hypotheses for GOF 667
Test Statistic and Degrees of Freedom for
GOF 667
Data Generating Situations 668
Mixtures: A Problem 668
Eyeball Tests 668
Small Expected Frequencies 668
15.3 Uniform Goodness of Fit Test 669
Multinomial Distribution 669
Uniform Distribution 669
Uniform GOF Test: Grouped Data 669
Uniform GOF Test: Raw Data 670
15.4 Poisson Goodness of Fit Test 673
Poisson Data Generating Situations 673
Poisson Goodness of Fit Test 674
Poisson GOF Test: Tabulated Data 674
Poisson GOF Test: Raw Data 676
15.5 Normal Chi Square Goodness of Fit Test 679
Normal Data Generating Situations 679
Method 1: Standardizing the Data 679
Method 2: Equal Bin Widths 6 79
Method 3: Equal Expected Frequencies 680
Application: Quality Management 680
15.6 ECDF Tests (Optional) 684
Kolmogorov Smirnov and Lilliefors Tests 684
Illustrations: Lottery Numbers and Kiss
Weights 684
Anderson Darling Test 685
Chapter Summary 686
i CHAPTER SIXTEEN
Nonparametric Tests 698
16.1 Why Use Nonparametric Tests? 699
16.2 One Sample Runs Test 700
Application: Quality Inspection 700
Small Samples 702
16.3 Wilcoxon Signed Rank Test 702
Application: Median versus Benchmark 703
Application: Paired Data 704
16.4 Mann Whitney Test 706
Application: Restaurant Quality 706
16.5 Kruskal Wallis Test for Independent Samples 709
Application: Employee Absenteeism 709
16.6 Friedman Test for Related Samples 714
Test Statistic 714
Application: Braking Effectiveness 714
16.7 Spearman Rank Correlation Test 716
Application: Calories and Fat 717
Correlation versus Causation 718
Chapter Summary 720
I CHAPTER SEVENTEEN
Quality Management 730
17.1 Quality and Variation 731
What Is Quality? 731
Productivity and Quality 732
Processes and Quality Metrics 732
Variance Reduction 732
Common Cause versus Special Cause 733
Role of Management 733
Role of Statisticians 733
17.2 Customer Orientation 733
Who Is a Customer? 733
Measuring Quality 734
17.3 Behavioral Aspects of Quality 734
Blame versus Solutions 734
Employee Involvement 735
17.4 Pioneers in Quality Management 735
Brief History of Quality Control 735
W. Edwards Deming 736
Other Influential Thinkers 73 7
17.5 Quality Improvement 737
Total Quality Management (TQM) 737
Business Process Redesign (BPR) 738
Statistical Quality Control (SQC) 738
Statistical Process Control (SPC) 738
Continuous Quality Improvement (CQI) 739
17.6 Control Charts: Overview 740
What Is a Control Chart? 740
Two Data Types 740
Three Common Control Charts 740
17.7 Control Charts for a Mean 741
x Charts: Bottle Filling Example 741
Control Limits: Known /u. and a 741
Empirical Control Limits 743
Control Chart Factors 743
Detecting Abnormal Patterns 744
Histograms 746
17.8 Control Charts for a Range 749
Control Limits for the Range 749
17.9 Patterns In Control Charts 750
The Overadjustment Problem 750
Abnormal Patterns 750
Symptoms and Assignable Causes 751
17.10 Process Capability 752
Cp Index 752
Cpt Index 753
Bottle Filling Revisited 755
17.11 Other Control Charts 755
A ttribute Data: p Charts 755
Application: Emergency Patients 757
Other Standard Control Charts (s, c, np, I, MR) 757
Ad Hoc Charts 759
17.12 Additional Quality Topics (Optional) 760
Acceptance Sampling 760
Supply Chain Management 760
Quality and Design 761
Taguchi s Robust Design 761
Six Sigma and Lean Six Sigma 761
ISO 9000 762
Malcolm Baldrige Award 762
Advanced MIN1TAB Features 762
Future of Statistical Process Control 762
Chapter Summary 763
CHAPTER EIGHTEEN
Simulation (On Student CD ROM)
APPENDIXES
A Exact Binomial Probabilities 774
B Exact Poisson Probabilities 776
C 1 Standard Normal Areas 779
C 2 Cumulative Standard Normal
Distribution 780
D Student s / Critical Values 782
E Chi Square Critical Values 783
F Critical Values of F 784
G Solutions to Odd Numbered Exercises 19 ,
i PHOTO CREDITS 816
INDEX 817
|
| adam_txt |
BRIEF CONTEF
CHAPTER ONE
Overview of Statistics 2
CHAPTER TWO
Data Collection 22
CHAPTER THREE
Describing Data Visually 58
CHAPTER FOUR
Descriptive Statistics 112
CHAPTER FIVE
Probability 168
CHAPTER SIX
Discrete Distributions 208
CHAPTER SEVEN
Continuous Distributions 252
CHAPTER EIGHT
Sampling Distributions and Estimation 292
CHAPTER NINE
One Sample Hypothesis Tests 346
CHAPTER TEN
Two Sample Hypothesis Tests 394
CHAPTER ELEVEN
Analysis of Variance 438
CHAPTER TWELVE
Bivariate Regression 488
SITS
CHAPTER THIRTEEN
Multiple Regression 558
CHAPTER FOURTEEN
Time Series Analysis 604
CHAPTER FIFTEEN
Chi Square Tests 656
CHAPTER SIXTEEN
Nonparametric Tests 698
CHAPTER SEVENTEEN
Quality Management 730
CHAPTER EIGHTEEN
Simulation (On Student CD ROM)
APPENDIXES
A Exact Binomial Probabilities 774
B Exact Poisson Probabilities 776
C 1 Standard Normal Areas 779
C 2 Cumulative Standard Normal
Distribution 780
D Student's / Critical Values 782
E Chi Square Critical Values 783
F Critical Values of F 784
G Solutions to Odd Numbered Exercises 792
PHOTO CREDITS 816
INDEX 817
CHAPTER ONE
Overview of Statistics 2
1.1 What is Statistics? 3
1.2 Why Study Statistics? 4
Communication 4
Computer Skills 4
Information Management 4
Technical Literacy 4
Career Advancement 4
Quality Improvement 4
1.3 Uses of Statistics 5
Auditing 5
Marketing 5
Health Care 5
Quality Control 5
Purchasing 6
Medicine 6
Forecasting 6
Product Warranty 6
1.4 Statistical Challenges 6
Working with Imperfect Data 6
Dealing with Practical Constraints 7
Upholding Ethical Standards 7
Using Consultants 7
1.5 Writing and Presenting Reports 9
Rules for "Power" Writing 9
Writing Style 9
Spelling and Grammar 10
Organizing a Technical Report 10
Writing an Executive Summary 10
Tables and Graphs 11
Rules for Presenting Oral Reports 11
The Three Ps 12
1.6 Statistical Pitfalls 14
Pitfall 1: Making Conclusions about a Large
Population from a Small Sample 14
Pitfall 2: Making Conclusions
from Nonrandom Samples 14
Pitfall 3: Attaching Importance
to Rare Observations from Large Samples 14
Pitfall 4: Using Poor Survey Methods 14
Pitfall 5: Assuming a Causal Link
Based Only on Observed Association 15
Pitfall 6: Making Generalizations
about Individuals from Observations
about Groups 15
Pitfall 7: Unconscious Bias 15
Pitfall 8: Attaching Practical Importance
to Every Statistically Significant Study Result 15
1.7 Statistics: An Evolving Field 16
Chapter Summary 16
CHAPTER TWO
Data Collection 22
2.1 Definitions 23
Subjects, Variables, and Data Sets 23
Data Types 24
2.2 Level of Measurement 26
Nominal Measurement 26
Ordinal Measurement 27
Interval Measurement 27
Ratio Measurement 28
Changing Data by Recoding 29
2.3 Time Series versus Cross Sectional Data 30
Time Series Data 30
Cross Sectional Data 30
2.4 Sampling Concepts 31
Sample or Census? 31
Parameters and Statistics 32
Target Population 32
Finite or Infinite? 33
2.5 Sampling Methods 33
Simple Random Sample 33
Random Number Tables 35
Setting Up a Rule 35
With or Without Replacement? 35
Computer Methods 36
Row/Column Data Arrays 36
Randomizing a List 37
Systematic Sample 37
Stratified Sample 39
Applications of Stratified Sampling 39
Cluster Sample 39
Judgment Sample 40
Convenience Sample 40
Sample Size 41
2.6 Data Sources 42
2.7 Survey Research 43
Survey Types 43
Response Rates 43
Getting Advice 44
Questionnaire Design 44
Question Wording 44
Coding and Data Screening 46
Sources of Error 46
Data File Format 47
Chapter Summary 49
CHAPTER THREE
Describing Data Visually 58
3.1 Visual Description 59
Measurement 60
Sorting 60
3.2 Dot Plots 61
Small Sample: Home Prices 61
Comparing Groups 63
3.3 Frequency Distributions and Histograms 65
Bins and Bin Limits 65
Constructing a Frequency Distribution 65
Histograms 66
Excel Histograms 66
MegaStat Histograms 68
MINITAB Histograms 68
Modal Class 69
Shape 69
3.4 Line Charts 72
Simple Line Charts 72
Grid Lines 73
Log Scales 73
Tips for Effective Line Charts 75
3.5 Bar Charts 76
Plain Bar Charts 76
3 D and Novelty Bar Charts 76
Pareto Charts 77
Stacked Bar Chart 78
Bar Charts for Time Series Data 78
Tips for Effective Bar Charts 79
3.6 Scatter Plots 80
Policy Making 82
Degree of Association 82
Making a Scatter Plot in Excel 84
3.7 Tables 86
Tips for Effective Tables 87
3.8 Pie Charts 87
An Oft Abused Chart 87
Pie Chart Options 87
3.9 Effective Excel Charts 90
Chart Wizard 90
Embellished Charts 91
3.10 Maps and Pictograms 94
Spatial Variation and GIS 94
Pictograms 95
3.11 Deceptive Graphs 95
Error 1: Nonzero Origin 95
Error 2: Elastic Graph Proportions 96
Error 3: Dramatic Title 96
Error 4: Distracting Pictures 96
Error 5: Authority Figures 97
Error 6: 3 D and Rotated Graphs 97
Error 7: Missing Axis Demarcations 97
Error 8: Missing Measurement Units
or Definitions 97
Error 9: Vague Source 97
Error 10: Complex Graphs 97
Error 11: Gratuitous Effects 98
Error 12: Estimated Data 98
Error 13: Area Trick 98
Final Advice 98
Further Challenges 99
Chapter Summary 99
\ CHAPTER FOUR
Descriptive Statistics 112
4.1 Numerical Description 113
Preliminary Analysis 114
Sorting 114
Visual Displays 114
Descriptive Statistics in Excel 116
Descriptive Statistics in MegaStat 117
4.2 Central Tendency 119
Mean 119
Characteristics of the Mean 119
Median 120
Characteristics of the Median 121
Mode 121
Skewness 123
Geometric Mean 127
Growth Rates 127
Midrange 128
Trimmed Mean 128
4.3 Dispersion 130
Range 131
Variance 131
Standard Deviation 131
Calculating a Standard Deviation 132
Characteristics of the Standard
Deviation 133
Coefficient of Variation 133
Mean Absolute Deviation 133
Central Tendency versus Dispersion:
Manufacturing 135
Central Tendency and Dispersion:
Job Performance 135
4.4 Standardized Data 136
Chebyshev's Theorem 136
The Empirical Rule 137
Unusual Observations 137
Defining a Standardized Variable 138
Outliers 139
Estimating Sigma 139
4.5 Percentiles and Quartiles 141
Percentiles 141
Quartiles 141
Method of Medians 142
Formula Method 143
Excel Quartiles 143
Dispersion Using Quartiles 144
Midhinge 144
Midspread (Interquartile Range) 145
Coefficient ofQuartile Variation 145
4.6 Box Plots 145
Fences and Unusual Data Values 146
4.7 Grouped Data 147
Nature and Grouped Data 147
Mean and Standard Deviation 148
Accuracy Issues 148
Properties of Grouped Estimates 149
4.8 Skewness and Kurtosis 149
Skewness 149
Kurtosis 150
Chapter Summary 152
S CHAPTER FIVE
Probability 168
5.1 Random Experiments 169
Sample Space 169
Events 170
5.2 Probability 171
Definitions 171
What Is "Probability "? 171
Empirical Approach 172
Law of Large Numbers 172
Practical Issues for Actuaries 172
Classical Approach 173
Subjective Approach 174
5.3 Rules of Probability 174
Complement of an Event I74
Odds of an Event 175
Union of Two Events 175
Intersection of Two Events 175
General Law of Addition 176
Mutually Exclusive Events 177
Special Law of Addition 177
Collectively Exhaustive Sets 177
Forced Dichotomy 177
Conditional Probability 178
5.4 Independent Events 180
Dependent Events 180
Actuaries Again 181
Multiplication Law for Independent Events 181
The Five Nines Rule 181
How Much Redundancy Is Needed? 182
Applications of Redundancy 182
5.5 Contingency Tables 183
What Is a Contingency Table? 183
Marginal Probabilities 184
Joint Probabilities 184
Conditional Probabilities 185
Independence 185
Relative Frequencies 186
How Do We Get a Contingency Table? 187
5.6 Tree Diagrams 190
What Is a Tree? 190
5.7 Bayes's Theorem (Optional) 191
How Bayes s Theorem Works 191
General Form of Bayes s Theorem 192
5.8 Counting Rules (Optional) 196
Fundamental Rule of Counting 196
Factorials 197
Permutations 198
Combinations 198
Chapter Summary 200
CHAPTER SIX
Discrete Distributions 208
6.1 Probability Models 209
6.2 Discrete Distributions 209
Random Variables 209
Probability Distributions 210
Expected Value 211
Application: Life Insurance 212
Application: Raffle Tickets 212
A ctuarial Fairness 213
Variance and Standard Deviation 213
What Is a PDF or CDF? 214
6.3 Uniform Distribution 215
Characteristics of the Uniform
Distribution 215
Application: Pumping Gas 217
Uniform Random Integers 218
Application: Copier Codes 218
Uniform Model in LearningStats 219
6.4 Bernoulli Distribution 219
Bernoulli Experiments 219
6.5 Binomial Distribution 220
Characteristics of the Binomial Distribution 220
Binomial Shape 221
Application: Uninsured Patients 222
Using the Binomial Formula 222
Using Tables: Appendix A 224
Compound Events 224
Binomial Probabilities: Excel 225
Binomial Probabilities: MegaStat 225
Binomial Probabilities: Visual Statistics 225
Binomial Probabilities: LearningStats 225
Binomial Random Data 225
Recognizing Binomial Applications 225
6.6 Poisson Distribution 228
Poisson Processes 228
Characteristics of the Poisson Distribution 229
Using the Poisson Formula 230
Compound Events 232
Poisson Probabilities: Tables (Appendix B) 232
Poisson Probabilities: Excel 232
Poisson Probabilities: Visual Statistics 232
Recognizing Poisson Applications 233
Poisson Approximation to Binomial
(Optional) 234
6.7 Hypergeometric Distribution 235
Characteristics of the Hypergeometric
Distribution 235
Using the Hypergeometric Formula 236
Hypergeometric Probabilities: Excel 237
Hypergeometric Probabilities: Visual
Statistics 237
Hypergeometric Probabilities: LearningStats 238
Recognizing Hypergeometric Applications 238
Binomial Application to the
Hypergeometric (Optional) 239
6.8 Geometric Distribution (Optional) 240
Characteristics of the Geometric Distribution 240
Using LearningStats 241
6.9 Transformations of Random Variables
(Optional) 242
Linear Transformation 242
Application: Exam Scores 242
Application: Total Cost 242
Sums of Random Variables 243
Application: Gasoline Expenses 243
Application: Project Scheduling 243
Chapter Summary 244
CHAPTER SEVEN
Continuous Distributions 252
7.1 Continuous Variables 253
Events as Intervals 253
7.2 Describing a Continuous Distribution 253
PDFs and CDFs 253
Probabilities as Areas 254
Expected Value and Variance 255
Oh My, Calculus? 255
7.3 Uniform Continuous Distribution 255
Characteristics of the Uniform Distribution 255
Special Case: Unit Rectangular 257
Uses of the Uniform Model 258
7.4 Normal Distribution 258
Characteristics of the Normal Distribution 258
What Is Normal? 260
7.5 Standard Normal Distribution 261
Characteristics of the Standard Normal 261
Normal Areas from Appendix C l 262
Basis for the Empirical Rule 263
Normal Areas from Appendix C 2 264
Finding zfor a Given Area 265
Finding Normal Areas with Excel 267
Finding Areas by Using Standardized
Variables 267
Inverse Normal 269
Using Excel Without Standardizing 270
Normal Random Data (Optional) 271
7.6 Normal Approximation to the Binomial
(Optional) 273
When Is Approximation Needed? 273
7.7 Normal Approximation to the Poisson
(Optional) 276
When Is Approximation Needed? 2 76
7.8 Exponential Distribution 277
Characteristics of the Exponential Distribution 277
Inverse Exponential 279
Mean Time Between Events 280
Using Excel 281
7.9 Triangular Distribution (Optional) 282
Characteristics of the Triangular Distribution 282
Special Case: Symmetric Triangular 284
Uses of the Triangular 284
Chapter Summary 284
I CHAPTER EIGHT
Sampling Distributions and Estimation 292
8.1 Sampling Variation 293
8.2 Estimators and Sampling Distributions 295
Some Terminology 295
Sampling Distributions 295
Bias 295
Efficiency 297
Consistency 297
8.3 Sample Mean and the Central Limit
Theorem 298
Central Limit Theorem for a Mean 299
Symmetric Population: Uniform Distribution 299
Skewed Population: Waiting Time 300
Range of Sample Means 302
Illustration: GMATScores 303
Sample Size and Standard Error 304
Illustration: All Possible Samples
from a Uniform Population 304
8.4 Confidence Interval for a Mean (fi) with
Known a 306
What Is a Confidence Interval? 306
Choosing a Confidence Interval 308
Is a Ever Known ? 3 08
8.5 Confidence Interval for a Mean (/z) with
Unknown a 309
Student s t Distribution 309
Degrees of Freedom 310
Comparison ofz and t 310
Confidence Interval Width 313
A ''Good" Sample? 313
More A nalysis Needed 314
Messy Data? 314
Using Appendix D 315
Using Excel 315
Using MegaStat 316
Using MINITAB 316
8.6 Confidence Interval for a Proportion (n) 317
Illustration: Internet Hotel Reservations 318
Applying the CLT 318
When Is It Safe to Assume Normality? 320
Standard Error of the Proportion 320
Confidence Interval for n 321
Narrowing the Interval? 322
Using Excel and MegaStat 323
Small Samples: MINITAB 323
Polls and Margin of Error 324
Rule of Three 324
Very Quick Rule 325
Advice on Proportions 325
8.7 Sample Size Determination for a Mean 326
A Myth 326
Sample Size to Estimate n 326
How to Estimate a 327
Using LearningStats 328
Using MegaStat 328
Caution 1: Units of Measure 328
Caution 2: Using z 328
Caution 3: Larger n Is Better 328
8.8 Sample Size Determination for a Proportion 329
Alternatives 330
Practical Advice 330
Using LearningStats 331
Caution I: Units of Measure 331
Caution 2: Finite Population 331
8.9 Confidence Interval for the Difference of Two
Means, /j.l /i2 (Optional) 331
Should Sample Sizes Be Equal? 333
8.10 Confidence Interval for the Difference of Two
Proportions, jt, — tt2 (Optional) 334
8.11 Confidence Interval for a Population Variance,
o2 (Optional) 335
Chi Square Distribution 335
Confidence Interval for a 336
Using LearningStats 336
Caution: Assumption of Normality 336
Chapter Summary 337
CHAPTER NINE
One Sample Hypothesis Tests 346
9.1 Logic of Hypothesis Testing 347
Process of Science 347
Who Tests Hypotheses? 348
Good News 348
Hypothesis Formulation 348
Can Hypotheses Be Proved? 348
Role of Evidence 349
Types of Error 349
Statistical Hypothesis Testing 350
One Sided Tests 351
When to Use a One Sided Test 352
Decision Rule 352
Type I Error 352
Type II Error 354
Power of a Test 354
Relationship Between a and 8 355
Consequences of Type II Error 355
Choice of a 355
Statistical Significance versus Practical
Importance 355
9.2 Testing a Proportion 357
Critical Value 358
p Value Method 359
Interpreting a p Value 360
Two Tailed Test 360
Calculating a p Value for a Two Tailed Test 361
Effect of a 362
Using the p Value 363
Effect of a Larger Sample 364
Small Samples and Non Normality (Optional) 366
9.3 Testing a Mean: Known Population Variance 367
Test Statistic 367
One Tailed Test 368
p Value Method 369
Two Tailed Test 369
Using the p Value 3 70
Analogy to Confidence Intervals 371
Significance versus Importance 371
9.4 Testing a Mean: Unknown Population
Variance 372
Using Student's t 372
Sensitivity to a 373
Using the p Value 3 73
Significance versus Importance 374
Normality Assumption 3 74
Confidence Interval versus Hypothesis Test 374
Using MegaStat 375
Large Samples 3 75
9.5 Power Curves and OC Curves (Optional) 377
Power Curve for a Mean: An Example 377
Calculating Power 3 78
Effect of Sample Size 380
Relationship of the Power and OC Curves 380
Power Curve for Tests of a Proportion 381
Using LearningStats 383
Using Visual Statistics 383
9.6 Tests for One Variance (Optional) 384
Using MegaStat 385
When to Use Tests for One Variance 386
Chapter Summary 387
» CHAPTER TEN
Two Sample Hypothesis Tests 394
10.1 Two Sample Tests 395
What Is a Two Sample Test? 395
Basis of Two Sample Tests 396
Test Procedure 396
10.2 Comparing Two Proportions 397
Testing for Zero Difference: it, = n2 397
Sample Proportions 397
Pooled Proportion 397
Test Statistic 397
Using the p Value 399
Checking Normality 399
Small Samples 400
Must Sample Sizes Be Equal? 400
Using Software for Calculations 400
Analogy to Confidence Intervals 400
Separate Confidence Intervals 401
Testing for Nonzero Difference (Optional) 403
Test Statistic 403
Using thep Value 404
10.3 Comparing Two Means: Independent
Samples 406
Format of Hypotheses 406
Test Statistic 406
Case 1: Known Variances 407
Case 2: Unknown Variances, Assumed Equal 407
Case 3: Unknown Variances, Assumed Unequal 407
Which Assumption is Best? 411
Must Sample Sizes Be Equal? 411
Large Samples 411
Caution: Three Issues 411
10.4 Comparing Two Means: Paired Samples 414
Paired Data 414
Paired t Test 414
Excel s Paired Difference Test 416
Analogy to Confidence Interval 416
Why Not Treat Paired Data As Independent
Samples? 417
10.5 Comparing Two Variances 420
Format of Hypotheses 420
The F Test 420
Critical Values 420
Illustration: Collision Damage 421
Comparison of Means 422
Comparison of Variances: Two Tailed Test 422
Comparison of Variances: One Tailed Test 424
Excel s F Test 425
Assumptions of the F Test 425
Significance versus Importance 426
Chapter Summary 427
CHAPTER ELEVEN
Analysis of Variance 438
11.1 Overview of ANOVA 439
The Goal: Explaining Variation 439
Illustration: Manufacturing Defect Rates 440
Illustration: Hospital Length of Stay 441
Illustration: Automobile Painting 441
ANOVA Calculations 441
ANOVA Assumptions 442
11.2 One Factor ANOVA (Completely Randomized
Model) 442
Data Format 442
Hypotheses to Be Tested 443
One Factor ANOVA as a Linear Model 443
Group Means 443
Partitioned Sum of Squares 443
Test Statistic 445
Decision Rule 445
Using MINITAB 448
11.3 Multiple Comparisons 450
Tukey'sTest 450
Using MegaStat 451
11.4 Tests for Homogeneity of Variances
(Optional) 452
ANOVA Assumptions 452
Hartley s Fmax Test 452
Levene 's Test 454
11.5 Two Factor ANOVA without Replication
(Randomized Block Model) 456
Data Format 456
Two Factor ANOVA Model 457
Hypotheses to Be Tested 457
Randomized Block Model 45 7
Format of Calculation ofNonreplicated
Two Factor ANOVA 458
Using MegaStat 460
Multiple Comparisons 461
Limitations of Two Factor ANOVA
without Replication 461
11.6 Two Factor ANOVA with Replication
(Full Factorial Model) 464
What Does Replication Accomplish ? 464
Format of Hypotheses 464
Format of Data 465
Sources ofVariation 465
Using MegaStat 468
Interaction Effect 468
Tukey Tests of Pairs of Means 470
Significance versus Importance 470
11.7 General Linear Model (Optional) 473
Higher Order ANOVA Models 473
WhatlsGLM? 474
11.8 Experimental Design: An Overview (Optional) 476
What Is Experimental Design? 476
2k Models 476
Fractional Factorial Designs 4 76
Nested or Hierarchical Design 477
Random Effects Models 477
Chapter Summary 477
CHAPTER TWELVE
Bivariate Regression 488
12.1 Visual Displays and Correlation Analysis 489
Visual Displays 489
Correlation Coefficient 490
Tests for Significance 490
Quick Rule for Significance 494
Role of Sample Size 494
Using Excel 494
Regression: The Next Step? 496
Autocorrelation 499
12.2 Bivariate Regression 500
What Is Bivariate Regression ? 500
Model Form 500
Interpreting a Fitted Regression 501
Prediction Using Regression 501
12.3 Regression Terminology 502
Models and Parameters 502
Estimating a Regression Line by Eye 502
Fitting a Regression on a Scatter Plot in
Excel 502
Illustration: Piper Cheyenne Fuel
Consumption 503
12.4 Ordinary Least Squares Formulas 505
Slope and Intercept 505
Illustration: Exam Scores and Study Time 506
Assessing Fit 507
Coefficient of Determination 508
12.5 Tests for Significance 511
Standard Error of Regression 511
Confidence Intervals for Slope and Intercept 511
Hypothesis Tests 512
Test for Zero Slope: Exam Scores 512
Using Excel: Exam Scores 513
Using MegaStat: Exam Scores 513
Using MINITAB: Exam Scores 514
Using MegaStat: U.S. Income and Taxes 516
MegaStat's Confidence Intervals: U.S. Income
and Taxes 516
Test for Zero Slope: Tax Data 516
12.6 Analysis of Variance: Overall Fit 517
Decomposition of Variance 517
F Statistic for Overall Fit 518
12.7 Confidence and Prediction Intervals for Y 522
How to Construct an Interval Estimate for Y 522
Two Illustrations: Exam Scores and Taxes 522
Quick Rules for Confidence and Prediction
Intervals 524
12.8 Violations of Assumptions 524
Three Important Assumptions 524
Non Normal Errors 524
Histogram of Residuals 524
Normal Probability Plot 525
What to Do About Non Normality? 525
Heteroscedastic Errors (Nonconstant Variance) 526
Tests for Heteroscedasticity 526
What to Do about Heteroscedasticity? 527
A utocorrelated Errors 527
Runs Test for Autocorrelation 528
Durbin Watson Test 528
What to Do about Autocorrelation ? 529
12.9 Unusual Observations 531
Standardized Residuals: Excel 531
Studentized Residuals: MINITAB 531
Studentized Residuals: MegaStat 532
Leverage and Influence 532
Studentized Deleted Residuals 534
12.10 Other Regression Problems (Optional) 536
Outliers 536
Model Misspecification 536
Ill Conditioned Data 536
Spurious Correlation 537
Model Form and Variable Transforms 538
Regression by Splines 540
Chapter Summary 541
i CHAPTER THIRTEEN
Multiple Regression 558
13.1 Multiple Regression 559
Bivariate or Multivariate? 559
Regression Terminology 560
Data Format 560
Illustration: Home Prices 560
Logic of Variable Selection 561
Fitted Regression 561
Two Predictor Model 562
One Predictor Model 562
Common Misconceptions about Fit 562
Regression Modeling 563
13.2 Assessing Overall Fit 564
F Test for Significance 564
Coefficient of Determination (R2) 565
Adjusted R2 565
How Many Predictors? 565
13.3 Predictor Significance 566
Hypothesis Tests 566
Test Statistic 567
13.4 Confidence Intervals for Y 569
Standard Error 569
Approximate Confidence and Prediction
Intervals for Y 569
Quick 95 Percent Prediction Interval for Y 570
13.5 Binary Predictors 572
What Is a Binary Predictor? 572
Effects of a Binary Predictor 572
Testing a Binary for Significance 573
More Than One Binary 574
What If I Forget to Exclude One Binary'' 5 75
Regional Binaries 577
13.6 Tests for Nonlinearity and Interaction 579
Tests for Nonlinearity 579
Tests for Interaction 580
13.7 Multicollinearity 581
What Is Multicollinearity? 581
Variance Inflation 581
Correlation Matrix 582
Predictor Matrix Plots 582
Variance Inflation Factor (VIF) 583
Rules of Thumb 583
Are Coefficients Stable? 584
13.8 Violations of Assumptions 586
Non Normal Errors 586
Nonconstant Variance (Heteroscedasticity) 586
Autocorrelation (Optional) 588
Unusual Observations 588
13.9 Other Regression Topics 590
Outliers: Causes and Cures 590
Missing Predictors 590
Ill Conditioned Data 590
Significance in Large Samples 591
Model Specification Errors 591
Missing Data 591
Binary Dependent Variable 591
Stepwise and Best Subsets Regression 591
Chapter Summary 592
CHAPTER FOURTEEN
Time Series Analysis 604
14.1 Time Series Components 605
Time Series Data 605
Stocks and Flows 606
Periodicity 607
Additive versus Multiplicative Models 607
A Graphical View 607
Trend 607
Cycle 609
Seasonal 609
Irregular 609
14.2 Trend Forecasting 610
Three Trend Models 610
Linear Trend Model 610
Illustration: Linear Trend 611
Linear Trend Calculations 611
Forecasting a Linear Trend 612
Linear Trend: Calculating R2 612
Exponential Trend Model 612
When to Use the Exponential Model 613
Illustration: Exponential Trend 613
Exponential Trend Calculations 614
Forecasting an Exponential Trend 615
Exponential Trend: Calculating R2 615
Quadratic Trend Model 615
Illustration: Quadratic Trend 616
Using Excel for Trend Fitting 617
Trend Fitting Criteria 617
14.3 Assessing Fit 623
Five Measures of Fit 623
14.4 Moving Averages 625
Trendless or Erratic Data 625
Trailing Moving Average (TMA) 625
Centered Moving Average (CMA) 626
Using Excel for a TMA 626
14.5 Exponential Smoothing 627
Forecast Updating 627
Smoothing Constant (a) 628
Choosing the Value of a 628
Initializing the Process 628
Using MINITAB 630
Using Excel 631
Smoothing with Trend and Seasonality 631
14.6 Seasonality 633
When and How to Deseasonalize 633
Illustration of Calculations 633
Using MINITAB to Deseasonalize 635
Seasonal Forecasts Using Binary Predictors 636
14.7 Forecasting: Final Thoughts 639
Role of Forecasting 639
Behavioral Aspects of Forecasting 639
Forecasts Are Always Wrong 639
Chapter Summary 640
CHAPTER FIFTEEN
Chi Square Tests 656
15.1 Chi Square Test for Independence 657
Contingency Tables 657
Chi Square Test 658
Chi Square Distribution 658
Expected Frequencies 659
Illustration of the Chi Square Calculations 660
Test of Two Proportions 662
Small Expected Frequencies 663
Cross Tabulating Raw Data 663
3 Way Tables and Higher 664
15.2 Chi Square Tests for Goodness of Fit 667
Purpose of the Test 667
Hypotheses for GOF 667
Test Statistic and Degrees of Freedom for
GOF 667
Data Generating Situations 668
Mixtures: A Problem 668
Eyeball Tests 668
Small Expected Frequencies 668
15.3 Uniform Goodness of Fit Test 669
Multinomial Distribution 669
Uniform Distribution 669
Uniform GOF Test: Grouped Data 669
Uniform GOF Test: Raw Data 670
15.4 Poisson Goodness of Fit Test 673
Poisson Data Generating Situations 673
Poisson Goodness of Fit Test 674
Poisson GOF Test: Tabulated Data 674
Poisson GOF Test: Raw Data 676
15.5 Normal Chi Square Goodness of Fit Test 679
Normal Data Generating Situations 679
Method 1: Standardizing the Data 679
Method 2: Equal Bin Widths 6 79
Method 3: Equal Expected Frequencies 680
Application: Quality Management 680
15.6 ECDF Tests (Optional) 684
Kolmogorov Smirnov and Lilliefors Tests 684
Illustrations: Lottery Numbers and Kiss
Weights 684
Anderson Darling Test 685
Chapter Summary 686
i CHAPTER SIXTEEN
Nonparametric Tests 698
16.1 Why Use Nonparametric Tests? 699
16.2 One Sample Runs Test 700
Application: Quality Inspection 700
Small Samples 702
16.3 Wilcoxon Signed Rank Test 702
Application: Median versus Benchmark 703
Application: Paired Data 704
16.4 Mann Whitney Test 706
Application: Restaurant Quality 706
16.5 Kruskal Wallis Test for Independent Samples 709
Application: Employee Absenteeism 709
16.6 Friedman Test for Related Samples 714
Test Statistic 714
Application: Braking Effectiveness 714
16.7 Spearman Rank Correlation Test 716
Application: Calories and Fat 717
Correlation versus Causation 718
Chapter Summary 720
I CHAPTER SEVENTEEN
Quality Management 730
17.1 Quality and Variation 731
What Is Quality? 731
Productivity and Quality 732
Processes and Quality Metrics 732
Variance Reduction 732
Common Cause versus Special Cause 733
Role of Management 733
Role of Statisticians 733
17.2 Customer Orientation 733
Who Is a Customer? 733
Measuring Quality 734
17.3 Behavioral Aspects of Quality 734
Blame versus Solutions 734
Employee Involvement 735
17.4 Pioneers in Quality Management 735
Brief History of Quality Control 735
W. Edwards Deming 736
Other Influential Thinkers 73 7
17.5 Quality Improvement 737
Total Quality Management (TQM) 737
Business Process Redesign (BPR) 738
Statistical Quality Control (SQC) 738
Statistical Process Control (SPC) 738
Continuous Quality Improvement (CQI) 739
17.6 Control Charts: Overview 740
What Is a Control Chart? 740
Two Data Types 740
Three Common Control Charts 740
17.7 Control Charts for a Mean 741
x Charts: Bottle Filling Example 741
Control Limits: Known /u. and a 741
Empirical Control Limits 743
Control Chart Factors 743
Detecting Abnormal Patterns 744
Histograms 746
17.8 Control Charts for a Range 749
Control Limits for the Range 749
17.9 Patterns In Control Charts 750
The Overadjustment Problem 750
Abnormal Patterns 750
Symptoms and Assignable Causes 751
17.10 Process Capability 752
Cp Index 752
Cpt Index 753
Bottle Filling Revisited 755
17.11 Other Control Charts 755
A ttribute Data: p Charts 755
Application: Emergency Patients 757
Other Standard Control Charts (s, c, np, I, MR) 757
Ad Hoc Charts 759
17.12 Additional Quality Topics (Optional) 760
Acceptance Sampling 760
Supply Chain Management 760
Quality and Design 761
Taguchi s Robust Design 761
Six Sigma and Lean Six Sigma 761
ISO 9000 762
Malcolm Baldrige Award 762
Advanced MIN1TAB Features 762
Future of Statistical Process Control 762
Chapter Summary 763
CHAPTER EIGHTEEN
Simulation (On Student CD ROM)
APPENDIXES
A Exact Binomial Probabilities 774
B Exact Poisson Probabilities 776
C 1 Standard Normal Areas 779
C 2 Cumulative Standard Normal
Distribution 780
D Student's / Critical Values 782
E Chi Square Critical Values 783
F Critical Values of F 784
G Solutions to Odd Numbered Exercises 19',
i PHOTO CREDITS 816
INDEX 817 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Doane, David P. Seward, Lori E. |
| author_facet | Doane, David P. Seward, Lori E. |
| author_role | aut aut |
| author_sort | Doane, David P. |
| author_variant | d p d dp dpd l e s le les |
| building | Verbundindex |
| bvnumber | BV022469742 |
| callnumber-first | H - Social Science |
| callnumber-label | HF1017 |
| callnumber-raw | HF1017 |
| callnumber-search | HF1017 |
| callnumber-sort | HF 41017 |
| callnumber-subject | HF - Commerce |
| classification_rvk | QH 240 |
| ctrlnum | (OCoLC)62118486 (DE-599)BVBBV022469742 |
| dewey-full | 330.015195 519.5 |
| dewey-hundreds | 300 - Social sciences 500 - Natural sciences and mathematics |
| dewey-ones | 330 - Economics 519 - Probabilities and applied mathematics |
| dewey-raw | 330.015195 519.5 |
| dewey-search | 330.015195 519.5 |
| dewey-sort | 3330.015195 |
| dewey-tens | 330 - Economics 510 - Mathematics |
| discipline | Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Mathematik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV022469742 |
| illustrated | Illustrated |
| index_date | 2024-07-02T17:44:05Z |
| indexdate | 2024-07-09T20:58:17Z |
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| isbn | 9780072966930 9780073215754 0073215759 0072966939 0072966963 0072966947 |
| language | English |
| lccn | 2005056294 |
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| physical | xxiii, 834 p. ill. (some col.) 1 CD-ROM (12 cm) |
| publishDate | 2007 |
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| publisher | McGraw-Hill/Irwin |
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| series2 | The McGraw-Hill/Irwin series operations and decision sciences |
| spelling | Doane, David P. Verfasser aut Applied statistics in business and economics David P. Doane, Lori E. Seward Boston [u.a.] McGraw-Hill/Irwin c2007 xxiii, 834 p. ill. (some col.) 1 CD-ROM (12 cm) txt rdacontent n rdamedia nc rdacarrier The McGraw-Hill/Irwin series operations and decision sciences Includes bibliographical references and index Administración - Métodos estadísticos Economía - Métodos estadísticos Estadística Estadística comercial Statistik Wirtschaft Commercial statistics Management Statistical methods Economics Statistical methods Statistics Wirtschaftswissenschaften (DE-588)4066528-8 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Anwendung (DE-588)4196864-5 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Statistik (DE-588)4056995-0 s Anwendung (DE-588)4196864-5 s Wirtschaftswissenschaften (DE-588)4066528-8 s 1\p DE-604 Seward, Lori E. Verfasser aut http://www.loc.gov/catdir/enhancements/fy0665/2005056294-t.html Table of contents only http://www.loc.gov/catdir/enhancements/fy0665/2005056294-d.html Publisher description HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015677251&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Doane, David P. Seward, Lori E. Applied statistics in business and economics Administración - Métodos estadísticos Economía - Métodos estadísticos Estadística Estadística comercial Statistik Wirtschaft Commercial statistics Management Statistical methods Economics Statistical methods Statistics Wirtschaftswissenschaften (DE-588)4066528-8 gnd Statistik (DE-588)4056995-0 gnd Anwendung (DE-588)4196864-5 gnd |
| subject_GND | (DE-588)4066528-8 (DE-588)4056995-0 (DE-588)4196864-5 (DE-588)4123623-3 |
| title | Applied statistics in business and economics |
| title_auth | Applied statistics in business and economics |
| title_exact_search | Applied statistics in business and economics |
| title_exact_search_txtP | Applied statistics in business and economics |
| title_full | Applied statistics in business and economics David P. Doane, Lori E. Seward |
| title_fullStr | Applied statistics in business and economics David P. Doane, Lori E. Seward |
| title_full_unstemmed | Applied statistics in business and economics David P. Doane, Lori E. Seward |
| title_short | Applied statistics in business and economics |
| title_sort | applied statistics in business and economics |
| topic | Administración - Métodos estadísticos Economía - Métodos estadísticos Estadística Estadística comercial Statistik Wirtschaft Commercial statistics Management Statistical methods Economics Statistical methods Statistics Wirtschaftswissenschaften (DE-588)4066528-8 gnd Statistik (DE-588)4056995-0 gnd Anwendung (DE-588)4196864-5 gnd |
| topic_facet | Administración - Métodos estadísticos Economía - Métodos estadísticos Estadística Estadística comercial Statistik Wirtschaft Commercial statistics Management Statistical methods Economics Statistical methods Statistics Wirtschaftswissenschaften Anwendung Lehrbuch |
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