Holdings: Business statistics for contemporary decision making

Business statistics for contemporary decision making:

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Bibliographic Details
Main Author:	Black, Ken (Author)
Format:	Book
Language:	English
Published:	Hoboken, NJ Wiley 2012
Edition:	7. ed.
Subjects:	Betriebswirtschaftliche Statistik Lehrbuch
Online Access:	Inhaltsverzeichnis
Physical Description:	XXVII, 850 S. Ill., graph. Darst.
ISBN:	9780470931462 9781118024119

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adam_text	и IMITAI li UNIT IVI INTRODUCTION 1 Introduction to Statistics 02 2 Charts and Graphs 18 3 Descriptive Statistics 52 4 Probability 98 DISTRIBUTIONS AND SAMPLING 5 Discrete Distributions 142 6 Continuous Distributions 184 7 Sampling and Sampling Distributions 222 MAKING INFERENCES ABOUT POPULATION PARAMETERS 8 Statistical Inference: Estimation for Single Populations 9 Statistical Inference: Hypothesis Testing for Single Populations 294 10 Statistical Inferences about Two Populations 348 11 Analysis of Variance and Design of Experiments 408 REGRESSION ANALYSIS AND FORECASTING 12 Simple Regression Analysis and Correlation 470 13 Multiple Regression Analysis 522 14 Building Multiple Regression Models 552 15 Time-Series Forecasting and Index Numbers 602 NONPARAMETRIC STATISTICS AND QUALITY 16 Analysis of Categorical Data 658 17 Nonparametric Statistics 684 18 Statistical Quality Control 734 APPENDICES A Tables 779 В Answers to Selected Odd-Numbered Quantitative Problems 819 GLOSSARY 829 INDEX 839 256 19 Supplement 1 Supplement 2 Supplement 3 The following materials are available at www.wiley.com/college/black Decision Analysis C19-2 Summation Notation Sl-l Derivation of Simple Regression Formulas for Slope and у Intercept S2-1 Advanced Exponential Smoothing S3-1 viii CONTENTS Preface xvii About the Author xxvii U N I T;* I INTRODUCTION 1 Introduction to Statistics 02 Decision Dilemma: Statistics Describe the State of Business in India s Countryside 03 1.1 Statistics in Business 04 1.2 Basic Statistical Concepts 05 1.3 Variables and Data 07 1.4 Data Measurement 07 Nominal Level 08 Ordinal Level 08 Interval Level 09 Ratio Level 09 Comparison of the Four Levels of Data 10 Statistical Analysis Using the Computer: Excel and Jviinitab 11 Summary 13 Key Terms 13 Supplementary Problems 13 Analyzing the Databases 14 Case; DiGiorno Pizza: Introducing a Frozen Pizza to Compete with Carry-Out 16 2 Charts and Graphs 18 Decision Dilemma: Container Shipping Companies 19 21 Frequency Distributions 20 Class Midpoint 20 Relative Frequency 21 Cumulative Frequency 21 Quantitative Data Graphs 23 Histograms 23 Using Histograms to Get an Initial Overview of the Data 25 Frequency Polygons 25 Ogives 26 Dot Plots 27 Stem-and-Leaf Plots 27 Qualitative Data Graphs 31 Pie Charts 31 Bar Graphs 32 Pareto Charts 34 Charts and Graphs for Two Variables 38 Cross Tabulation 38 Scatter Plot 38 2.2 2.3 2.4 Summary 42 Key Terms 43 Supplementary Problems 43 Analyzing the Databases 47 Case: Soap Companies Do Battle 48 Using the Computer 49 3 Descriptive Statistics 52 Decision Dilemma: Laundry Statistics 53 3.1 Measures of Central Tendency: Ungrouped Data 53 Mode 54 Median 54 Mean 55 Percentiles 57 Steps in Determining the Location of a Percentile 57 Quartiles 58 3.2 Measures of Variability: Ungrouped Data 61 Range 61 Interquartile Range 62 Mean Absolute Deviation, Variance, and Standard Deviation 63 Mean Absolute Deviation 64 Variance 65 Standard Deviation 66 Meaning of Standard Deviation 66 Empirical Rule 66 Chebyshev s Theorem 68 Population Versus Sample Variance and Standard Deviation 69 Computational Formulas for Variance and Standard Deviation 70 z Scores 72 Coefficient of Variation 73 3.3 Measures of Central Tendency and Variability: Grouped Data 76 Measures of Central Tendency 76 Mean 77 Median 77 Mode 78 Measures of Variability 78 3.4 Measures of Shape 83 Skewness 83 Skewness and the Relationship of the Mean, Median, and Mode 84 Coefficient of Skewness 84 Kurtosis 84 Box-and-Whisker Plots and Five-Number Summary 85 ix X Contents 3.5 Descriptive Statistics on the Computer 87 Summary 89 Key Terms 90 Formulas 90 Supplementary Problems 91 Analyzing the Databases 95 Case: Coca-Cola Develops the African Market 95 Using the Computer 97 4 Probability 98 Decision Dilemma: Equity of the Sexes in the Workplace 99 4.1 Introduction to Probability 100 4.2 Methods of Assigning Probabilities 100 Classical Method of Assigning Probabilities 100 Relative Frequency of Occurrence 101 Subjective Probability 102 4.3 Structure of Probability 102 Experiment 102 Event 102 Elementary Events 102 Sample Space 103 Unions and Intersections 103 Mutually Exclusive Events 104 Independent Events 104 Collectively Exhaustive Events 105 Complementary Events 105 Counting the Possibilities 105 The mn Counting Rule 105 Sampling from a Population with Replacement 106 Combinations: Sampling from a Population Without Replacement 106 4.4 Marginal, Union, Joint, and Conditional Probabilities 107 4.5 Addition Laws 109 Joint Probability Tables 110 Complement of a Union 113 Special Law of Addition 114 4.6 Multiplication Laws 117 General Law of Multiplication 117 Special Law of Multiplication 119 4.7 Conditional Probability 122 Independent Events 125 4.8 Revision of Probabilities: Bayes Rule 129 Summary 134 Key Terms 134 Formulas 135 Supplementary Problems 135 Analyzing the Databases 139 Case: Colgate-Palmolive Makes a Total Effort 139 DISTRIBUTIONS AND SAMPLING 5 Discrete Distributions 142 Decision Dilemma: Life with a Cell Phone 143 5.1 Discrete Versus Continuous Distributions 144 5.2 Describing a Discrete Distribution 145 Mean, Variance, and Standard Deviation of Discrete Distributions 146 Mean or Expected Value 146 Variance and Standard Deviation of a Discrete Distribution 146 5.3 Binomial Distribution 149 Solving a Binomial Problem 150 Using the Binomial Table 153 Using the Computer to Produce a Binomial Distribution 154 Mean and Standard Deviation of a Binomial Distribution 155 Graphing Binomial Distributions 156 5.4 Poisson Distribution 160 Working Poisson Problems by Formula 162 Using the Poisson Tables 163 Mean and Standard Deviation of a Poisson Distribution 164 Graphing Poisson Distributions 165 Using the Computer to Generate Poisson Distributions 165 Approximating Binomial Problems by the Poisson Distribution 166 5.5 Hypergeometric Distribution 170 Using the Computer to Solve for Hypergeometric Distribution Probabilities 172 Summary 175 Key Terms 175 Formulas 176 Supplementary Problems 176 Analyzing the Databases 181 Case: Whole Foods Market Grows Through Mergers and Acquisitions 181 Using the Computer 182 6 Continuous Distributions 184 Decision Dilemma: The Cost of Human Resources 185 6.1 The Uniform Distribution 185 Determining Probabilities in a Uniform Distribution 187 Using the Computer to Solve for Uniform Distribution Probabilities 189 6.2 Normal Distribution 190 History of the Normal Distribution 191 Contents Xl Probability Density Function of the Normal Distribution 191 Standardized Normal Distribution 192 Solving Normal Curve Problems 193 Using the Computer to Solve for Normal Distribution Probabilities 200 6.3 Using the Normal Curve to Approximate Binomial Distribution Problems 202 Correcting for Continuity 204 6.4 Exponential Distribution 208 Probabilities of the Exponential Distribution 209 Using the Computer to Determine Exponential Distribution Probabilities 211 Summary 213 Key Terms 214 Formulas 214 Supplementary Problems 214 Analyzing the Databases 218 Case: Mercedes Goes After Younger Buyers 218 Using the Computer 219 7 Sampling and Sampling Distributions 222 Decision Dilemma: What Is the Attitude of Maquiladora Workers? 223 7.1 Sampling 223 Reasons for Sampling 224 Reasons for Taking a Census 224 Frame 225 Random Versus Nonrandom Sampling 226 Random Sampling Techniques 226 Simple Random Sampling 226 Stratified Random Sampling 228 Systematic Sampling 229 Cluster (or Area) Sampling 229 Nonrandom Sampling 230 Convenience Sampling 231 Judgment Sampling 232 Quota Sampling 232 Snowball Sampling 232 Sampling Error 233 Nonsampling Errors 233 7·2 Sampling Distribution of χ 234 Sampling from a Finite Population 241 7-3 Sampling Distribution of ρ 244 Summary 248 Key Terms 248 Formulas 248 Supplementary Problems 249 Analyzing the Databases 251 Case: Shell Attempts to Return to Premiere Status 251 Using the Computer 252 MAKING INFERENCES ABOUT POPULATION PARAMETERS 8 Statistical Inference: Estimation for Single Populations 256 Decision Dilemma: Compensation for Purchasing Managers 257 8.1 Estimating the Population Mean Using the ζ Statistic {σ Known) 259 Finite Correction Factor 262 Estimating the Population Mean Using the ζ Statistic when the Sample Size Is Small 263 Using the Computer to Construct ζ Confidence Intervals for the Mean 264 8.2 Estimating the Population Mean Using the t Statistic (σ Unknown) 266 The f Distribution 267 Robustness 267 Characteristics of the ŕ Distribution 267 Reading the ŕ Distribution Table 267 Confidence Intervals to Estimate the Population Mean Using the f Statistic 268 Using the Computer to Construct t Confidence Intervals for the Mean 270 8.3 Estimating the Population Proportion 273 Using the Computer to Construct Confidence Intervals of the Population Proportion 276 8.4 Estimating the Population Variance 277 8.5 Estimating Sample Size 281 Sample Size when Estimating μ 281 Determining Sample Size when Estimating ρ 283 Summary 286 Key Terms 286 Formulas 286 Supplementary Problems 287 Analyzing the Databases 290 Case: The Container Store 290 Using the Computer 291 9 Statistical Inference: Hypothesis Testing for Single Populations 294 Decision Dilemma: Word-of-Mouth Business Referrals and Influential 295 9.1 Introduction to Hypothesis Testing 296 Types of Hypotheses 297 Research Hypotheses 297 Xli Contents Statistical Hypotheses 298 Substantive Hypotheses 300 Using the HTAB System to Test Hypotheses 301 Rejection and Nonrejection Regions 303 Type I and Type II Errors 304 9.2 Testing Hypotheses About a Population Mean Using the z Statistic [σ Known) 305 Testing the Mean with a Finite Population 307 Using the p-Value to Test Hypotheses 308 Using the Critical Value Method to Test Hypotheses 309 Using the Computer to Test Hypotheses About a Population Mean Using the ζ Statistic 312 9.3 Testing Hypotheses About a Population Mean Using the {Statistic (σ Unknown) 314 Using the Computer to Test Hypotheses About a Population Mean Using the f Test 318 9.4 Testing Hypotheses About a Proportion 321 Using the Computer to Test Hypotheses About a Population Proportion 325 9.5 Testing Hypotheses About a Variance 327 9.6 Solving for Type II Errors 330 Some Observations About Type II Errors 335 Operating Characteristic and Power Curves 335 Effect of Increasing Sample Size on the Rejection Limits 337 Summary 340 Key Terms 341 Formulas 341 Supplementary Problems 341 Analyzing the Databases 344 Case: Frito-Lay Targets the Hispanic Market 344 Using the Computer 346 10 Statistical Inferences about Two Populations 348 Decision Dilemma: Online Shopping 349 10.1 Hypothesis Testing and Confidence Intervals About the Difference in Two Means Using the ζ Statistic (Population Variances Known) 352 Hypothesis Testing 353 Confidence Intervals 356 Using the Computer to Test Hypotheses About the Difference in Two Population Means Using the ζ Test 358 10.2 Hypothesis Testing and Confidence Intervals About the Difference in Two Means: Independent Samples and Population Variances Unknown 361 Hypothesis Testing 361 Using the Computer to Test Hypotheses and Construct Confidence Intervals about the Difference in Two Population Means Using the t Test 363 Confidence Intervals 366 10.3 Statistical Inferences for Two Related Populations 371 Hypothesis Testing 371 Using the Computer to Make Statistical Inferences about Two Related Populations 373 Confidence Intervals 376 10.4 Statistical Inferences About Two Population Proportions, ρλ - p2 381 Hypothesis Testing 381 Confidence Intervals 385 Using the Computer to Analyze the Difference in Two Proportions 386 10.5 Testing Hypotheses About Two Population Variances 388 Using the Computer to Test Hypotheses About Two Population Variances 392 Summary 397 Key Terms 397 Formulas 397 Supplementary Problems 398 Analyzing the Databases 403 Case: Seitz Corporation: Producing Quality Gear-Driven and Linear-Motion Products 403 Using the Computer 404 11 Analysis of Variance and Design of Experiments 408 Decision Dilemma: Job and Career Satisfaction of Foreign Self-Initiated Expatriates 409 11.1 Introduction to Design of Experiments 410 11.2 The Completely Randomized Design {One-Way ANOVA) 412 One-Way Analysis of Variance 413 Reading the F Distribution Table 417 Using the Computer for One-Way ANOVA 417 Comparison of Fand ŕ Values 418 11.3 Multiple Comparison Tests 424 Tukey s Honestly Significant Difference (HSD) Test: The Case of Equal Sample Sizes 424 Using the Computer to Do Multiple Comparisons 426 Tukey-Kramer Procedure: The Case of Unequal Sample Sizes 428 11.4 The Randomized Block Design 432 Using the Computer to Analyze Randomized Block Designs 436 11.5 A Factorial Design (Two-Way ANOVA) 442 Advantages of the Factorial Design 442 Factorial Designs with Two Treatments 443 Contents ХШ Applications 443 Statistically Testing the Factorial Design 444 Interaction 445 Using a Computer to Do a Two-Way ANOVA 450 Summary 459 Key Terms 460 Formulas 460 Supplementary Problems 461 Analyzing the Databases 464 Case: The Clarkson Company: A Division of Tyco International 465 Using the Computer 466 REGRESSION ANALYSIS AND FORECASTING 12 Simple Regression Analysis and Correlation 470 Decision Dilemma: Predicting International Hourly Wages by the Price of a Big Mac 471 12.1 12.2 12.3 12.4 12.5 12.6 12.7 Correlation 472 Introduction to Simple Regression Analysis 475 Determining the Equation of the Regression Line 476 Residual Analysis 483 Using Residuals to Test the Assumptions of the Regression Model 485 Using the Computer for Residual Analysis 486 Standard Error of the Estimate 490 Coefficient of Determination 493 Relationship Between rand r2 495 Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model 495 Testing the Slope 495 Testing the Overall Model 499 Estimation 500 Confidence Intervals to Estimate the Conditional Mean of y: μΫίχ 500 Prediction Intervals to Estimate a Single Value of y 501 Using Regression to Develop a Forecasting Trend Line 504 Determining the Equation of the Trend Line 505 Forecasting Using the Equation of the Trend Line 506 Alternate Coding for Time Periods 507 42.10 Interpreting the Output 510 Summary 514 Key Term с 12.8 12.9 Formulas 515 Supplementary Problems 515 Analyzing the Databases 519 Case: Delta Wire Uses Training as a Weapon 519 Using the Computer 521 13 Multiple Regression Analysis 522 Decision Dilemma: Are You Going to Hate Your New Job? 523 13.1 The Multiple Regression Model 524 Multiple Regression Model with Two Independent Variables (First-Order) 525 Determining the Multiple Regression Equation 526 A Multiple Regression Model 526 13.2 Significance Tests of the Regression Model and Its Coefficients 531 Testing the Overall Model 531 Significance Tests of the Regression Coefficients 533 13.3 Residuals, Standard Error of the Estimate, and R2 536 Residuals 536 SSE and Standard Error of the Estimate 537 Coefficient of Multiple Determination (fî2) 538 Adjusted R2 539 13.4 Interpreting Multiple Regression Computer Output 541 A Reexaminaron of the Multiple Regression Output 541 Summary 545 Key Terms 546 Formulas 546 Supplementary Problems 546 Analyzing the Databases 549 Case: Starbucks Introduces Debit Card 549 Using the Computer 550 14 Building Multiple Regression Models 552 Decision Dilemma: Determining Compensation for CEOs 553 14.1 Nonlinear Models: Mathematical Transformation 554 Polynomial Regression 554 Tukey s Ladder of Transformations 557 Regression Models with Interaction 558 Model Transformation 560 14.2 Indicator (Dummy) Variables 566 14.3 Model-Building: Search Procedures 572 Search Procedures 574 All Possible Regressions 574 XIV Contents Stepwise Regression 574 Forward Selection 578 Backward Elimination 578 14.4 Multicollinearity 582 14.5 Logistic Regression 584 An Example 584 The Logistic Regression Model 586 Interpreting the Output 587 Determining Logistic Regression Model Testing the Overall Model 588 Testing Individual Predictor Variables 589 Summary 593 Key Terms 594 Formulas 594 Supplementary Problems 595 Analyzing the Databases 598 Case: Virginia Semiconductor 598 Using the Computer 600 15 Time-Series Forecasting and Index Numbers 602 588 15.2 608 608 611 15.3 Decision Dilemma: Forecasting Air Pollution 603 15.1 Introduction to Forecasting 604 Time-Series Components 604 The Measurement of Forecasting Error 605 Error 605 Mean Absolute Deviation (MAD) 605 Mean Square Error (MSE) 606 Smoothing Techniques Naive Forecasting Models Averaging Models 609 Simple Averages 609 Moving Averages 609 Weighted Moving Averages Exponential Smoothing 613 Trend Analysis 618 Linear Regression Trend Analysis 618 Regression Trend Analysis Using Quadratic Models 620 Holt s Two-Parameter Exponential Smoothing Method 623 Seasonal Effects 625 Decomposition 625 Finding Seasonal Effects with the Computer 628 Winters Three-Parameter Exponential Smoothing Method 628 Autocorrelation and Autoregression Autocorrelation 630 Ways to Overcome the Autocorrelation Problem 633 Addition of Independent Variables 633 Transforming Variables 634 Autoregression 634 15.4 15.5 630 15.6 Index Numbers 637 Simple Index Numbers 638 Unweighted Aggregate Price Index Numbers 638 Weighted Aggregate Price Index Numbers 639 Laspeyres Price Index 640 Paasche Price Index 641 Summary 646 Key Terms 647 Formulas 647 Supplementary Problems 647 Analyzing the Databases 652 Case: Debourgh Manufacturing Company 653 Using the Computer 654 NONPAR AM ETRIC STATISTICS AND QUALITY 16 Analysis of Categorical Data 658 Decision Dilemma: Selecting Suppliers in the Electronics Industry 659 16.1 Chi-Square Goodness-of-Fit Test 660 Testing a Population Proportion by Using the Chi-Square Goodness-of-Fit Test as an Alternative Technique to the z Test 666 16.2 Contingency Analysis: Chi-Square Test of Independence 670 Summary 680 Key Terms 680 Formulas 680 Supplementary Problems 680 Analyzing the Databases 682 Case: Foot Locker in the Shoe Mix 682 Using the Computer 683 17 Nonparametric Statistics 684 Decision Dilemma: How Is the Doughnut Business? 685 17.1 Runs Test 687 Small-Sample Runs Test 688 Large-Sample Runs Test 689 17.2 Mann-Whitney U Test 692 Small-Sample Case 692 Large-Sample Case 694 17.3 Wilcoxon Matched-Pairs Signed Rank Test 700 Small-Sample Case (n s 15) 700 Large-Sample Case (n > 15) 702 17.4 Kruskal-Wallis Test 708 Contents XV 17.5 Friedman Test 713 17.6 Spearman s Rank Correlation 719 Summary 724 Key Terms 725 Formulas 725 Supplementary Problems 725 Analyzing the Databases 730 Case: Schwinn 731 Using the Computer 732 18 Statistical Quality Control 734 Decision Dilemma: Italy s Piaggio Makes a Comeback 735 18.1 Introduction to Quality Control 736 What Is Quality Control? 736 Total Quality Management 737 Deming s U Points 738 Quality Gurus 739 Six Sigma 739 Design for Six Sigma 741 Lean Manufacturing 741 Some Important Quality Concepts 741 Benchmarking 742 Just-in-Time Inventory Systems 742 Reengineering 743 Failure Mode and Effects Analysis 744 Poka-Yoke 745 Quality Circles and Six Sigma Teams 745 18.2 Process Analysis 747 Flowcharts 747 Pareto Analysis 748 Cause-and-Effect (Fishbone) Diagrams 749 Control Charts 750 Check Sheets or Checklists 751 Histogram 752 Scatter Chart or Scatter Diagram 752 18.3 Control Charts 753 Variation 754 Types of Control Charts 754 χ Chart 754 R Charts 758 p Charts 759 с Charts 762 Interpreting Control Charts 764 Summary 770 Key Terms 771 Formulas 771 Supplementary Problems 772 Analyzing the Databases 775 Case: Robotron-ELOTHERM 776 Using the Computer 777 APPENDICES A Tables 779 В Answers to Selected Odd-Numbered Quantitative Problems 819 GLOSSARY 829 INDEX 839 The following materials are available at www.wiley.com/college/black 19 Decision Analysis C19-2 Décision Dilemma: Decision Making at the CEO Level C19-3 19.1 The Decision Table and Decision Making Under Certainty C19-4 Decision Table C19-4 Decision Making Under Certainty C19-5 19.2 Decision Making Under Uncertainty C19-6 Maximax Criterion C19-6 Maximin Criterion C19-6 Hurwicz Criterion C19-7 Minimax Regret C19-9 19.3 Decision Making Under Risk C19-14 Decision Trees C19-14 Expected Monetary Value (EMV) C19-14 Expected Value of Perfect Information C19-18 Utility C19-19 19.4 Revising Probabilities in Light of Sample Information C19-22 Expected Value of Sample Information C19-25 Summary C19-32 Key Terms C19-33 Formula C19-33 Supplementary Problems C19-33 Analyzing the Databases C19-36 Case: Fletcher-Terry: On the Cutting Edge C19-36 SUPPLEMENTS 1 Summation Notation Sl-l 2 Derivation of Simple Regression Formulas for Slope and y Intercept S2-1 3 Advanced Exponential Smoothing S3-1 Exponential Smoothing with Trend Effects: Holt s Method S3-1 Exponential Smoothing with Both Trend and Seasonally: Winter s Method S3-2 Some Practice Problems S3-5
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spelling	Black, Ken Verfasser aut Business statistics for contemporary decision making Ken Black 7. ed. Hoboken, NJ Wiley 2012 XXVII, 850 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Betriebswirtschaftliche Statistik (DE-588)4006243-0 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Betriebswirtschaftliche Statistik (DE-588)4006243-0 s DE-604 Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024659897&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Black, Ken Business statistics for contemporary decision making Betriebswirtschaftliche Statistik (DE-588)4006243-0 gnd
subject_GND	(DE-588)4006243-0 (DE-588)4123623-3
title	Business statistics for contemporary decision making
title_auth	Business statistics for contemporary decision making
title_exact_search	Business statistics for contemporary decision making
title_full	Business statistics for contemporary decision making Ken Black
title_fullStr	Business statistics for contemporary decision making Ken Black
title_full_unstemmed	Business statistics for contemporary decision making Ken Black
title_short	Business statistics for contemporary decision making
title_sort	business statistics for contemporary decision making
topic	Betriebswirtschaftliche Statistik (DE-588)4006243-0 gnd
topic_facet	Betriebswirtschaftliche Statistik Lehrbuch
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