Practical business statistics:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Amsterdam [u.a.]
Elsevier, Academic Press
2012
|
| Edition: | 6. ed. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XIX, 619 S. graph. Darst. |
| ISBN: | 9780123852083 0123852080 |
Staff View
MARC
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| 245 | 1 | 0 | |a Practical business statistics |c Andrew F. Siegel |
| 250 | |a 6. ed. | ||
| 264 | 1 | |a Amsterdam [u.a.] |b Elsevier, Academic Press |c 2012 | |
| 300 | |a XIX, 619 S. |b graph. Darst. | ||
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Record in the Search Index
| _version_ | 1804145591963353088 |
|---|---|
| adam_text | Titel: Practical business statistics
Autor: Siegel, Andrew F.
Jahr: 2011
Contents
Preface xv Bivariate Data 20
About the Author xix Multivariate Data 20
2.2 Quantitative Data: Numbers 21
Discrete Quantitative Data 21
Part | Continuous Quantitative Data 21
Introduction and Watch out for Meaningless Numbers 22
. . 2.3 Qualitative Data: Categories 22
Descriptive Statistics Ordinal Qualitative Data 22
Nominal Qualitative Data 23
1. Introduction: Defining the Role of 2.4 Time-Series and Cross-Sectional Data 23
Statistics in Business 2.5 Sources of Data, Including the Internet 24
2.6 End-of-Chapter Materials 29
Summary 29
Key Words 30
Questions 30
Problems 30
34
Projects 34
1.1 Why Statistics?
Why Should You Learn Statistics? 3
Is Statistics Difficult? 4
Does Learning Statistics Decrease Your Problems
Decision-Making Flexibility? 4
Database Exercises
Histograms: Looking at the
1.2 What Is Statistics? 4
Statistics Looks at the Big Picture 4
Statistics Doesn t Ignore the Individual 4
Looking at Data 4
Statistics in Management 5
1.3 The Five Basic Activities of Statistics 5 Distribution of Data
Designing a Plan for Data Collection 5 3 -1 A List of Data 35
Exploring the Data 5 The Number Line 36
Modeling the Data 6 32 Using a Histogram to Display the
Estimating an Unknown Quantity 6 Frequencies 37
Hypothesis Testing 7 Histograms and Bar Charts 39
1.4 Data Mining 8 3 3 Norma| Distributions 40
1.5 What Is Probability? 14 34 skewed Distributions and
1.6 General Advice 14 Data Transformation 43
1.7 End-of-Chapter Materials 14 The Trouble with Skewness 45
Summary 14 Transformation to the Rescue 45
Keywords 15 Interpreting and Computing the Logarithm 46
Questions 15 3 5 Bimodal Distributions 47
Problems 16 Is It Really Bimodal? 47
Project 17 36 Outliers 49
Dealing with Outliers 49
Data Structures: Classifying the 3.7 Data Mining with Histograms 53
3.8 Histograms by Hand: Stem-and-Leaf 54
Various Types of Data Sets 3.9 End.of.chaptyer Materials 55
2.1 How Many Variables? 19 Summary 55
Univariate Data 19 Key Words 56
Questions 56 Problems 112
Problems 57 Database Exercises 120
Database Exercises 64 Projects 120
Project 64 Case 121
Case 64
4. Landmark Summaries: Interpreting Part II
Typical Values and Percentiles Probability
4.1 What Is the Most Typical Value? 65
The Average: A Typical Value for 6- Probability: Understanding Random
Quantitative Data 66
Situations
The Weighted Average: Adjusting for 6.1 An Example: Is It behind Door Number 1,
Importance 68 Door Number 2, or Door Number 3? 126
The Median: A Typical Value for 6-2 How Can You Analyze Uncertainty? 127
Quantitative and Ordinal Data 70 The Rancjom Experiment: A Precise
The Mode: A Typical Value Even for Definition of a Random Situation 127
Nominal Data 73
The Sample Space: A List of What
Which Summary Should You Use? 75 Might Happen 127
4.2 What Percentile Is It? 76 The Outcome: What Actually Happens 128
Extremes, Quartiles, and Box Plots 76 Events: Either They Happen or They Don t 128
The Cumulative Distribution Function 63 How Likely Is an Event? 129
Displays the Percentiles 80 Every Event Has a Probability 129
4.3 End-of-Chapter Materials 83 Where Do Probabilities Come From? 130
Summary 83 Relative Frequency and the Law
Key Words 84 of Large Numbers 130
Questions 84 Theoretical Probability 131
Problems 85 The Equally Likely Rule 131
Database Exercises 91 Subjective Probability 132
Projects 91 Bayesian and Non-Bayesian Analysis 132
Case 92 6.4 how Can You Combine Information
about More Than One Event? 133
5. Variability: Dealing with Diversity Venn Diagrams Help You See All
to 7 the Possibilities 133
5.1 The Standard Deviation: The Traditional Not an Event 133
Choice 96 The Complement (Not) Rule 134
Definition and Formula for the Standard One Event and Another 134
Deviation and the Variance 97 What If Both Events Can t Happen at
Using a Calculator or a Computer 98 Once? 134
Interpreting the Standard Deviation 98 The Intersection (and) Rule for Mutually
Interpreting the Standard Deviation for Exclusive Events 135
a Normal Distribution 99 One Event or Another 135
The Sample and the Population Standard The Union (or) Rule for Mutually
Deviations 106 Exclusive Events 135
5.2 The Range: Quick and Superficial 107 Finding or from and and Vice Versa 135
5.3 The Coefficient of Variation: One Event Given Another: Reflecting
A Relative Variability Measure 108 Current Information 136
5.4 Effects of Adding to or Rescaling the Data 109 The Rule for Finding a Conditional
5.5 End-of-Chapter Materials 110 Probability Given Certain Information 137
Summary 110 Conditional Probabilities for Mutually
KeyWords 112 Exclusive Events 138
Questions 112 Independent Events 138
The Intersection (and) Rule for Part III
independent Events 139 statistical Inference
The Relationship between Independent
and Mutually Exclusive Events 139
6.5 what s the Best Way to Solve Probability 8- Random Sampling: Planning Ahead
Problems? 139 for Data Gathering
Probability Trees 139 8-1 Populations and Samples 190
Rules for Probability Trees 141 Wnat Is a Representative Sample? 190
Joint Probability Tables 145 A Sample Statistic and a Population Parameter 191
6.6 End-of-Chapter Materials 145 82 The Random Sample 192
Summary 145 Selecting a Random Sample 192
Keywords 147 Sampling by Shuffling the Population 195
Questions 147 83 The Sampling Distribution and the Central
Problems 148 Limit Theorem 196
Database Exercises 153 8.4 A Standard Error Is an Estimated Standard
Projects 153 Deviation 200
Case 153 How Close Is the Sample Average to the
Population Mean? 201
Correcting for Small Populations 203
7. Random Variables: Working with The Standard Error of the Binomial
Uncertain Numbers Proportion 204
7.1 Discrete Random Variables 156 85 Other Sampling Methods 205
Finding the Mean and Standard Deviation 156 The Stratified Random Sample 205
7.2 The Binomial Distribution 158 The Systematic Sample 208
Definition of Binomial Distribution
8.6 End-of-Chapter Materials 209
and Proportion 159 Summary 209
Finding the Mean and Standard Deviation
Keywords 210
the Easy Way 161 Questions 211
Problems 211
Finding the Probabilities 162
7.3 The Normal Distribution 164
Database Exercises 217
Confidence Intervals: Admitting That
Estimates Are Not Exact
Visualize Probabilities as the Area under
the Case 218
the Curve 165
The Standard Normal Distribution Z and
Its Probabilities 166
Solving Word Problems for Normal
Probabilities 166
The Four Different Probability Calculations 172 9.1 The Confidence Interval for a Population
Be Careful: Things Need Not Be Normal! 173 Mean or a Population Percentage 220
7.4 The Normal Approximation to The t Table and the t Distribution 222
the Binomial 173 The Widely Used 95% Confidence Interval 225
7.5 Two Other Distributions: The Poisson Other Confidence Levels 228
and the Exponential 176 9.2 Assumptions Needed for Validity 230
The Poisson Distribution 176 Random Sampling 230
The Exponential Distribution 177 Normal Distribution 232
7.6 End-of-Chapter Materials 178 9.3 Interpreting a Confidence Interval 234
Summary 178 Which Event Has a 95% Probability? 234
Keywords 180 Your Lifetime Track Record 235
Questions 180 9.4 One-Sided Confidence Intervals 235
Problems 180 Be Careful! You Can t Always Use
Database Exercises 185 a One-Sided Interval 235
Projects 185 Computing the One-Sided Interval 235
Case 186 9.5 Prediction Intervals 237
9.6 End-of-Chapter Materials 238 Correlation Measures the Strength of
Summary 238 the Relationship 295
Key Words 239 The Formula for the Correlation 296
Questions 240 The Various Types of Relationships 297
Problems 240 Linear Relationship 297
Database Exercises 245 No Relationship 299
Projects 246 Nonlinear Relationship 301
Case 246 Unequal Variability 304
Clustering 307
10. Hypothesis Testing: Deciding Bivariate Outliers 308
between Reality and Coincidence Correlation Is Not Causation 309
11-2 Regression: Prediction of One Variable
10.1 Hypotheses Are Not Created Equal! 250
from Another 310
The Null Hypothesis 250
A Straight Line Summarizes a Linear
The Research Hypothesis 250
Relationship 310
Straight Lines 312
What Will the Result Tell You? 250
Examples of Hypotheses 251 Finding a Line Based on Data 312
10.2 Testmg the Popula ion Mean agamst a How Useful Is the Line? 315
Known Reference Value 252 The Standard Error of Estimate: How
Using Confidence Intervals: The Easy Way 252 Large Are the Prediction Errors? 315
The t Statistic: Another Way, Same Answer 257 R2: How Much Is Explained? 317
10.3 Interpreting a Hypothesis Test 258
Confidence Intervals and Hypothesis
Errors: Type I and Type II 259
Tests for Regression 317
Assumptions Needed for Validity 259
The Linear Model Assumption Defines
Hypotheses Have No Probabilities of
the Population 317
Being True or False 260 Standard Errors for the Slope and Intercept 318
Statistical Significance and Test Levels 260
Confidence Intervals for Regression
10.4 Onesided Testing 262 Conffidents 319
Testing Whether the Relationship Is
How to Perform the Test 263 Real or Coincidence 319
10.5 Testing Whether or Not a New
Other Methods of Testing the
Observation Comes from the
Significance of a Relationship 320
Computer Results for the Production
Cost Data 320
Same Population 267
10.6 Testing Two Samples 268
The Paired t Test 268 Other Tests of a Regression Coefficient 323
The Unpaired t Test 270
A New Observation: Uncertainty and the
10.7 End-of-Chapter Materials 274
Confidence Interval 324
The Mean of Y: Uncertainty and the
Key Words 276
Questions 276
Problems 277
Confidence Interval 325
Regression Can Be Misleading 326
The Linear Model May Be Wrong 326
Database Exercises 285
Predicting Intervention from Observed
Projects 286
Experience Is Difficult 327
The Intercept May Not Be Meaningful 328
Explaining Yfrom X versus Explaining Xfrom Y 328
Part IV A Hidden Third Factor May Be Helpful 328
Regression and Time Series 113 End-of-chapter Materials 329
Summary 329
Key Words 331
Questions 331
Problems 332
11.1 Exploring Relationships Using Database Exercises 344
Scatterplots and Correlations 292 Projects 345
The Scatterplot Shows You the Relationship 292 Case 345
11. Correlation and Regression: Measuring
and Predicting Relationships
12. Multiple Regression: Predicting One Questions 399
Variable from Several Others Problems 400
Database Exercises 414
12.1 Interpreting the Results of Project 415
a Multiple Regression 348 Case 415
Regression Coefficients and the
Regression Equation 349
Interpreting the Regression Coefficients 352 13- RePort Writing: Communicating the
Predictions and Prediction Errors 354 Results of a Multiple Regression
How Good Are the Predictions? 355 13 .1 How to Organize Your Report 418
Typical Prediction Error: Standard Error The Executive Summary Paragraph 418
of Estimate 355 The Introduction Section 419
Percent Variation Explained: R2 356 The Analysis and Methods Section 419
Inference in Multiple Regression 357 The Conclusion and Summary Section 420
Assumptions 357 Including References 420
Is the Model Significant? The F Test or The Appendix Section 421
r2 Test 358 13.2 Hints and Tips 421
Tables of Critical Values for Testing R 365 Think about Your Audience 421
Which Variables Are Significant? what to Write First? Next? Last? 421
A t Test for Each Coefficient 368 Other Sources 421
Other Tests for a Regression Coefficient 369 13. 3 Examp|e: A Quick Pricing Formula for
Which Variables Explain the Most? 369 Customer Inquiries 422
12.2 Pitfalls and Problems in Multiple 13 4 End-of-Chapter Materials 426
Regression 371 Summary 426
Multicollinearity: Are the Explanatory Kev Words 426
Variables Too Similar? 372
Questions 426
Variable Selection: Are You Using the Problems 427
Wrong Variables? 375 Database Exercises 428
Prioritizing the List of X Variables 376 Project 428
Automating the Variable Selection Process 376
Model Misspecification: Does the
Regression Equation Have the
14. Time Series: Understanding Changes
Wrong Form? 377 over Time
Exploring the Data to See Nonlinearity 14-1 An Overview of Time-Series Analysis 430
or Unequal Variability 378 14-2 Trend-Seasonal Analysis 436
Using the Diagnostic Plot to Decide Trend and Cydic: The Moving Average 437
If You Have a Problem 378 Seasonal Index: The Average Ratio-to-
Using Percent Changes to Model an Moving-Average Indicates Seasonal
Economic Time Series 381 Behavior 438
12.3 Dealing with Nonlinear Relationships Seasonal Adjustment: The Series Divided
and Unequal Variability 384 by the Seasonal Index 439
Transforming to a Linear Relationship: Long-Term Trend and Seasonally
Interpreting the Results 384 Adjusted Forecast: The Regression Line 441
Fitting a Curve with Polynomial Regression 388 Forecast: The Seasonalized Trend 442
Modeling Interaction between 14.3 Modeling Cyclic Behavior
Two X Variables 389 Using Box-Jenkins ARIMA Processes 445
12.4 Indicator Variables: Predicting from A Random Noise Process Has No
Categories 392 Memory: The Starting Point 448
Interpreting and Testing Regression An Autoregressive (AR) Process
Coefficients for Indicator Variables 394 Remembers Where It Was 448
Separate Regressions 397 A Moving-Average (MA) Process Has
12.5 End-of-Chapter Materials 397 a Limited Memory 450
Summary 397 The Autoregressive Moving-Average
Key Words 399 (ARMA) Process Combines AR and MA 451
Part V
Methods and Applications
A Pure Integrated (I) Process Remembers The Hypotheses 492
Where It Was and Then Moves at Random 453 The Assumption 493
The Autoregressive Integrated Moving- 16.2 Testing ior Differences in Paired Data 495
Average (ARIMA) Process Remembers Using the Sign Test on the Differences 495
Its Changes 454 The Hypotheses 496
14.4 End-of-Chapter Materials 455 The Assumption 496
Summary 455 16.3 Testing to See If Two Unpaired
Key Words 456 Samples Are Significantly Different 496
Questions 457 The Procedure Is Based on the Ranks
Problems 458 of AlI of the Data 497
Projects 464 The Hypotheses 497
The Assumptions 497
16.4 End-of-Chapter Materials 500
Summary 500
Key Words 502
Questions 502
15. Anova: Testing for Differences among Problems 502
Many Samples, and Much More Database Exercises 506
15.1 Using Box Plots to Look at Many Projects 506
Samples at Once 468
15.2 The F Test Tells You if the Averages 17. Chi-Squared Analysis: Testing for
Are significantly Different 470 Patterns in Qualitative Data
The Data Set and Sources of Variation 470
The Assumptions 470 17 1 Summarizing Qualitative Data by Using
The Hypotheses 471 Counts and Percentages 508
The F Statistic 471 17-2 Testing If Population Percentages Are
The F Table 472 Equal to Known Reference Values 509
The Result of the F Test 477 The Chi-Squared Test for Equality
Computer Output: The One-Way of Percentages 509
ANOVA Table 477 17.3 Testing for Association between Two
15.3 The Least-Significant-Difference Test:
Qualitative Variables 512
Which Pairs Are Different? 479 The Meaning of Independence 512
15.4 More Advanced ANOVA Designs 480 The Chi-Squared Test for Independence 512
Variety Is the Spice of Life 480 17-4 End-of-Chapter Materials 516
Two-Way ANOVA 480 Summary 516
Three-Way and More 481 Key Words 517
Analysis of Covariance (ANCOVA) 481 Questions 517
Multivariate Analysis of Variance
Problems 518
(MANOVA) 481 Database Exercises 521
How to Read an ANOVA Table 481 Projects 521
15.5 End-of-Chapter Materials 484
Summary 484 18. Quality Control: Recognizing and
Key Words 485 Managing Variation
Questions 485
Problems 485 1^ 1 Processes and Causes of Variation 524
Database Exercises 489 The Pareto Dia§ram Shows Where to
Focus Attention 525
18.2 Control Charts and How to Read Them 526
The Control Limits Show if a Single
Observation Is Out of Control 527
How to Spot Trouble Even within the
16.1 Testing the Median against Control Limits 528
a Known Reference Value 492 18.3 Charting a Quantitative Measurement
The Sign Test 492 with X and R Charts 528
Projects 490
16. Nonparametrics: Testing with Ordinal
Data or Nonnormal Distributions
18.4 Charting the Percent Defective 534 Appendix A Employee Database 545
18.5 End-of-Chapter Materials 536 Appendix B Donations Database 547
Summary 536 Appendix C Self Test: Solutions to Selected
KeyWords 536 Problems and Database Exercises 551
Questions 536 Appendix D Statistical Tables 565
Problems 537 Glossary 597
Projects 543 Index 605
|
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| author | Siegel, Andrew F. 1950- |
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| dewey-ones | 519 - Probabilities and applied mathematics |
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| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Wirtschaftswissenschaften |
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| publisher | Elsevier, Academic Press |
| record_format | marc |
| spelling | Siegel, Andrew F. 1950- Verfasser (DE-588)129753777 aut Practical business statistics Andrew F. Siegel 6. ed. Amsterdam [u.a.] Elsevier, Academic Press 2012 XIX, 619 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Wirtschaftsstatistik (DE-588)4066517-3 gnd rswk-swf Betriebswirtschaftliche Statistik (DE-588)4006243-0 gnd rswk-swf Industrial management / Statistical methods (DE-588)4123623-3 Lehrbuch gnd-content Wirtschaftsstatistik (DE-588)4066517-3 s DE-604 Betriebswirtschaftliche Statistik (DE-588)4006243-0 s 1\p DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022469494&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 | Siegel, Andrew F. 1950- Practical business statistics Wirtschaftsstatistik (DE-588)4066517-3 gnd Betriebswirtschaftliche Statistik (DE-588)4006243-0 gnd |
| subject_GND | (DE-588)4066517-3 (DE-588)4006243-0 (DE-588)4123623-3 |
| title | Practical business statistics |
| title_auth | Practical business statistics |
| title_exact_search | Practical business statistics |
| title_full | Practical business statistics Andrew F. Siegel |
| title_fullStr | Practical business statistics Andrew F. Siegel |
| title_full_unstemmed | Practical business statistics Andrew F. Siegel |
| title_short | Practical business statistics |
| title_sort | practical business statistics |
| topic | Wirtschaftsstatistik (DE-588)4066517-3 gnd Betriebswirtschaftliche Statistik (DE-588)4006243-0 gnd |
| topic_facet | Wirtschaftsstatistik Betriebswirtschaftliche Statistik Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022469494&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT siegelandrewf practicalbusinessstatistics |