Practical business statistics
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1997
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| Ausgabe: | 3. ed. |
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| 245 | 1 | 0 | |a Practical business statistics |c Andrew F. Siegel |
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Datensatz im Suchindex
| _version_ | 1804138152224358401 |
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| adam_text | Contents in Brief
13311111 Introduction and Descriptive Statistics 1
1 Introduction Defining the Role of Statistics in Business 3
2 Data Structures Classifying the Various Types of Data Sets 13
3 Histograms Looking at the Distribution of the Data 26
4 Landmark Summaries Interpreting Typical Values and Percentiles 64
5 Variability Dealing with Diversity 105
6 Probability Understanding Random Situations 143
7 Random Variables Working with Uncertain Numbers 187
flEZStlllHi Statistical Inference 231
8 Random Sampling Planning Ahead for Data Gathering 233
9 Confidence Intervals Admitting that Estimates Are Not Exact 275
10 Hypothesis Testing Deciding between Reality and Coincidence 311
BES!!I23!9llli Regression and Time Series 365
11 Correlation and Regression Measuring and Predicting Relationships 367
12 Multiple Regression Predicting One Factor from Several Others 435
13 Report Writing Communicating the Results of a Multiple Regression 512
14 Time Series Understanding Changes over Time 530
WKMSSBMIIi Methods and Applications 579
15 ANOVA Testing for Differences among Many Samples, and Much More 581
16 Nonparametrics Testing with Ordinal Data or Nonnormal Distributions 613
17 Chi-Squared Analysis Testing for Patterns in Qualitative Data 637
18 Quality Control Recognizing and Managing Variation 659
Appendix A Employee Database 687
Appendix B Self-Test: Solutions to Selected Problems
and Database Exercises 689
Appendix C Statistical Tables 703
Glossary 727
Index 739
Contents
^^^^^^B Introduction and Descriptive Statistics 1
1 Introduction Defining the Role of Statistics in Business 3
1.1 Why Statistics 3
Why Should You Learn Statistics?
Is Statistics Difficult?
Does Learning Statistics Decrease Your Decision-Making Flexibility?
1.2 What Is Statistics? 4
Statistics Looks at the Big Picture
Statistics Doesn t Ignore the Individual
EXAMPLE Data in Management 5
Looking at Data
Statistics in Management
1.3 The Four Basic Activities of Statistics 6
Designing a Plan for Data Collection
Exploring the Data
Estimating an Unknown Quantity
Hypothesis Testing
EXAMPLE Statistical Quality Control 8
1.4 What Is Probability? 9
1.5 General Advice 10
2 Data Structures Classifying the Various Types of Data Sets 13
2.1 How Many Variables? 13
Univariate Data
Bivariate Data
Multivariate Data
2.2 Quantitative Data: Numbers 15
Discrete Quantitative Data
Continuous Quantitative Data
¦ Contents xvii
Watch Out for Meaningless Numbers
EXAMPLE Alphabetical Order of States 17
2.3 Qualitative Data: Categories 17
Ordinal Qualitative Data
Nominal Qualitative Data
2.4 Time-Series and Cross-Sectional Data 18
3 Histograms Looking at the Distribution of the Data 26
3.1 A List of Data 26
EXAMPLE Performance of Regional Sales Managers 26
EXAMPLE Household Size 27
The Number Line
3.2 Using a Histogram to Display the Frequencies 28
EXAMPLE Mortgage Interest Rates 28
Histograms and Bar Charts
EXAMPLE Salary Offers to College Graduates 31
3.3 Normal Distributions 32
3.4 Skewed Distributions and Data Transformation 34
EXAMPLE Assets of Commercial Banks 35
EXAMPLE Populations of States 35
The Trouble with Skewness
Transformation to the Rescue
EXAMPLE Transforming State Populations 38
Interpreting the Logarithm
3.5 Bimodal Distributions 40
EXAMPLE Money Market Yields 40
Is It Really Bimodal?
EXAMPLE The Cost of a Day in the Hospital 41
3.6 Outliers 43
Dealing with Outliers
EXAMPLE Research and Development Expenses 44
EXAMPLE Changes in Television Advertising 45
3.7 Histograms by Hand: Stem-and-Leaf 49
EXAMPLE Employees in Food Services 49
Case Let s Control Waste in Production 62
4 Landmark Summaries Interpreting Typical Values and Percentiles 64
4.1 What Is the Most Typical Value? 64
The Average: A Typical Value for Quantitative Data
EXAMPLE How Much Will Consumers Spend? 66
EXAMPLE How Many Defective Parts? 67
The Weighted Average: Adjusting for Importance
EXAMPLE Your Grade Point Average 69
EXAMPLE The Firm s Cost of Capital 70
EXAMPLE Adjusting for Misrepresentation 71
The Median: A Typical Value for Quantitative and Ordinal Data
EXAMPLE The Crash of October 19, 1987: Stocks Drop at Opening 73
EXAMPLE Personal Incomes 74
EXAMPLE Stages of Completion of Inventory 76
xviii ¦ Contents
The Mode: A Typical Value Even for Nominal Data
EXAMPLE Voting 77
EXAMPLE Quality Control: Controlling Variation in Manufacturing 78
EXAMPLE Inventory Completion Stages Revisited 79
Which Summary Should You Use?
4.2 What Percentile Is It? 81
Extremes, Quartiles, and Box Plots
EXAMPLE Executive Compensation 83
The Cumulative Distribution Function Displays the Percentiles
EXAMPLE Business Failures 88
Case Managerial Projections for Production and Marketing, or
The Case of the Suspicious Customer 102
5 Variability Dealing with Diversity 105
5.1 The Standard Deviation: The Traditional Choice 106
Definition and Formula for the Standard Deviation and the Variance
Using a Calculator or a Computer
Interpreting the Standard Deviation
EXAMPLE The Advertising Budget 109
EXAMPLE Customer Diversity 110
Interpreting the Standard Deviation for a Normal Distribution
EXAMPLE A Quality Control Chart For Picture-Scanning Quality 112
EXAMPLE Stock Market Returns Vary from Day to Day 112
EXAMPLE The Stock Market Crash of 1987: 19 Standard Deviations! 116
EXAMPLE Market Volatility before and after the Crash 118
EXAMPLE Diversification in the Stock Market 119
The Sample and the Population Standard Deviations
5.2 The Range: Quick and Superficial 120
EXAMPLE Employee Salaries 121
EXAMPLE Duration of Hospital Stays 122
5.3 The Coefficient of Variation: A Relative Variability Measure 123
EXAMPLE Uncertainty in Portfolio Performance 124
EXAMPLE Employee Productivity in Telemarketing 124
5.4 Effects of Adding to or Rescaling the Data 125
EXAMPLE Uncertainty of Costs in Japanese Yen and in U.S. Dollars 125
EXAMPLE Total Cost and Units Produced 126
Case Should We Keep or Get Rid of This Supplier? 140
6 Probability Understanding Random Situations 143
6.1 An Example: Is It behind Door Number 1, Door Number 2, or
Door Number 3? 144
6.2 How Can You Analyze Uncertainty? 145
The Random Experiment: A Precise Definition of a Random Situation
The Sample Space: A List of What Might Happen
The Outcome: What Actually Happens
Events: Either They Happen or They Don t
6.3 How Likely Is an Event? 149
¦ Contents xix
Every Event Has a Probability
Where Do Probabilities Come From?
Relative Frequency and the Law of Large Numbers
EXAMPLE How Variable Is Today s Extra-High-Quality Production? 152
Theoretical Probability
The Equally Likely Rule
EXAMPLE Coin Tossing and Cards 153
EXAMPLE Gender and Hiring 154
EXAMPLE Defective Raw Materials 154
Subjective Probability
EXAMPLE Settling a Lawsuit 154
Bayesian and Non-Bayesian Analysis
6.4 How Can You Combine Information about More than One Event? 156
Venn Diagrams Help You See All the Possibilities
Not an Event
The Complement {not) Rule
One Event and Another
What if Both Events Can t Happen at Once?
The Intersection (and) Rule for Mutually Exclusive Events
One Event or Another
The Union (or) Rule for Mutually Exclusive Events
Finding or from and, and Vice Versa
One Event Given Another: Reflecting Current Information
The Rule for Finding a Conditional Probability Given Certain Information
Conditional Probabilities for Mutually Exclusive Events
Independent Events
EXAMPLE Women in Executive Positions 164
EXAMPLE Market Efficiency 165
The Intersection (and) Rule for Independent Events
EXAMPLE Risk Assessment for a Large Power Plant 166
The Relationship between Independent and Mutually Exclusive Events
6.5 What s the Best Way to Solve Probability Problems? 166
Probability Trees
EXAMPLE Managing Software Support 168
Rules for Probability Trees
EXAMPLE Drug Testing of Employees 168
EXAMPLE A Pilot Project Helps Predict Success of a Product Launch 171
EXAMPLE Solution to Is It behind Door Number 1, 2, or 3 174
Joint Probability Tables
Case Whodunit? Who, if Anyone, Is Responsible for the Recent Rise
in the Defect Rate? 185
7 Random Variables Working with Uncertain Numbers 187
7.1 Discrete Random Variables 188
EXAMPLE Profit under Various Economic Scenarios 188
Finding the Mean and Standard Deviation
EXAMPLE Evaluating Risk and Return 190
7.2 The Binomial Distribution 192
Definition of Binomial Distribution and Proportion
EXAMPLE How Many Orders are Placed?: The Hard Way to Compute 193
XX ¦ Contents
Finding the Mean and Standard Deviation the Easy Way
EXAMPLE Recalling Advertisements 196
Finding the Probabilities
EXAMPLE How Many Major Customers Will Call Tomorrow? 198
EXAMPLE How Many Logic Analyzers to Schedule for Manufacturing? 199
7.3 The Normal Distribution 201
Visualize Probabilities as the Area under the Curve
The Standard Normal Distribution Z and Its Probabilities
Solving Word Problems for Normal Probabilities
The Four Different Probability Calculations
Be Careful: Things Need Not Be Normal!
EXAMPLE A Lottery (or Risky Project) 210
7.4 The Normal Approximation to the Binomial 210
EXAMPLE High-and Low-Speed Microprocessors 212
EXAMPLE Polling the Electorate 214
7.5 Two Other Distributions: The Poisson and the Exponential 215
The Poisson Distribution
EXAMPLE How Many Warranty Returns? 216
EXAMPLE How Many Phone Calls? 218
The Exponential Distribution
Case The Option Value of an Oil Lease 229
BE333EBI Statistical Inference 231
8 Random Sampling Planning Ahead for Data Gathering 233
8.1 Populations and Samples 234
How Do You Choose a Representative Sample?
A Sample Statistic and a Population Parameter
8.2 The Random Sample 237
Selecting a Random Sample
Sampling by Shuffling the Population
EXAMPLE Auditing 240
EXAMPLE A Pilot Study of Large Paper and Forest Products Firms 241
8.3 The Sampling Distribution and the Central Limit Theorem 242
EXAMPLE How Much Do Shoppers Spend? 244
EXAMPLE Consistency in Bubble Gum Production 247
8.4 A Standard Error Is an Estimated Standard Deviation 248
How Close Is the Sample Average to the Population Mean?
EXAMPLE Shopping Trips 250
Correcting for Small Populations
EXAMPLE Quality of the Day s Production 253
The Standard Error of the Binomial Proportion
EXAMPLE A Consumer Survey 254
8.5 Other Sampling Methods 255
The Stratified Random Sample
EXAMPLE Adjusting for Sophistication of the Consumer 257
EXAMPLE The Price of a Typical Suit in a Department Store 259
The Systematic Sample
Case Can This Survey Be Saved? 273
¦ Contents xxi
9 Confidence Intervals Admitting that Estimates Are Not Exact 275
9.1 The Confidence Interval for a Population Mean or a Population Percentage 277
The t Table and the t Distribution
The Widely Used 95% Confidence Interval
EXAMPLE Controlling the Average Thickness of Paper 282
EXAMPLE Election Polling (a Binomial Situation) 283
Other Confidence Levels
EXAMPLE Average Selling Price as Determined through Rebates 286
EXAMPLE Yield of a Manufacturing Process 287
9.2 Assumptions Needed for Validity 287
Random Sampling
EXAMPLE Forecasting Interest Rates 289
Normal Distribution
9.3 Interpreting a Confidence Interval 292
Which Event Has a 95% Probability?
Your Lifetime Track Record
9.4 One-Sided Confidence Intervals 294
Be Careful! You Can t Always Use a One-Sided Interval
Computing the One-Sided Interval
EXAMPLE The Savings of a New System 296
EXAMPLE Travel Costs 296
9.5 Prediction Intervals 297
EXAMPLE How Long until Your Order Is Filled? 298
Case Promising Results from a Specialty Catalog Survey 310
10 Hypothesis Testing Deciding between Reality and Coincidence 311
10.1 Hypotheses Are Not Created Equal! 312
The Null Hypothesis
The Research Hypothesis
What Will the Result Tell You?
Examples of Hypotheses
10.2 Testing the Population Mean against a Known Reference Value 314
Using Confidence Intervals: The Easy Way
EXAMPLE Does the Yield-Increasing Additive Really Work? 316
EXAMPLE Employee Stock Ownership and Product Quality 319
EXAMPLE Pushing the Limits of Production (a Binomial Situation) 320
The t Statistic: Another Way, Same Answer
10.3 Interpreting a Hypothesis Test 324
Errors: Type I and Type II
Assumptions Needed for Validity
Hypotheses Have No Probabilities of Being True or False
Statistical Significance, Test Levels, p-Values, and Computer Output
10.4 One-Sided Testing 330
Using a One-Sided Confidence Interval
EXAMPLE Launching a New Product 332
How to Perform the Test
The One-Sided t Test: Another Way, Same Answer
EXAMPLE Launching a New Product, Revisited 333
How to Perform the Test
xxii ¦ Contents
EXAMPLE Will Costs Go Down? 335
EXAMPLE Can You Create Value by Changing Your Firm s Name? 338
10.5 Testing Whether or Not a New Observation Comes from the Same
Population 339
EXAMPLE Is This System under Control? 339
10.6 Testing Two Samples 340
The Paired t Test
EXAMPLE Reactions to Advertising 341
The Unpaired t Test
EXAMPLE Gender Discrimination and Salaries 344
EXAMPLE Your Productivity versus Theirs 346
Case So Many Ads, So Little Time 363
BE332I5BI Regression and Time Series 365
11 Correlation and Regression Measuring and Predicting Relationships 367
11.1 Exploring Relationships Using Scatterplots and Correlations 368
The Scatterplot Shows You the Relationship
Correlation Measures the Strength of the Relationship
The Formula for the Correlation
The Various Types of Relationships
Linear Relationship
EXAMPLE Nielsen TV Ratings and People Meters 372
EXAMPLE Mergers 373
EXAMPLE Mortgage Rates and Fees 374
No Relationship
EXAMPLE Momentum and the Stock Market 376
Nonlinear Relationship
EXAMPLE Index Options 378
EXAMPLE Yield and Temperature 381
Unequal Variability
EXAMPLE Optical Cable 382
Clustering
EXAMPLE Flower Bonds 384
Outliers
EXAMPLE Number Produced and Cost 386
Correlation Is Not Causation
EXAMPLE Apartment Rents and Vacancy Rates 388
11.2 Regression: Prediction of One Thing from Another 389
A Straight Line Summarizes a Linear Relationship
Straight Lines
Finding a Line Based on Data
EXAMPLE Fixed and Variable Costs 393
EXAMPLE Territory and Sales 394
How Useful Is the Line?
The Standard Error of Estimate: How Large Are the Prediction Errors?
R2: How Much Is Explained?
Confidence Intervals and Hypothesis Tests for Regression
The Linear Model Assumption Defines the Population
¦ Contents xxiii
Standard Errors for the Slope and Intercept
Confidence Intervals for Regression Coefficients
EXAMPLE Variable Costs of Production 403
Testing whether the Relationship Is Real or Coincidence
Other Methods of Testing the Significance of a Relationship
Computer Output for the Production Cost Data
EXAMPLE Momentum in the Stock Market Revisited 404
Other Tests of a Regression Coefficient
A New Observation: Uncertainty and the Confidence Interval
The Mean of Y: Uncertainty and the Confidence Interval
Regression Can Be Misleading
The Linear Model May Be Wrong
Predicting Intervention from Observed Experience Is Difficult
The Intercept May Not Be Meaningful
Explaining Y from X versus Explaining X from Y
A Hidden Third Factor May Be Helpful
Case Just One More Production Step: Is It Worthwhile? 434
12 Multiple Regression Predicting One Factor from Several Others 435
12.1 Interpreting the Results of a Multiple Regression 436
EXAMPLE Magazine Ads 437
Regression Coefficients and the Regression Equation
Interpreting the Regression Coefficients
Predictions and Prediction Errors
How Good Are the Predictions?
Typical Prediction Error: Standard Error of Estimate
Percent Variation Explained: R2
Inference in Multiple Regression
Assumptions
Is the Model Significant? The F Test or R2 Test
Tables of Critical Values for Testing R2
Which Variables Are Significant? A t Test for Each Coefficient
Other Tests for a Regression Coefficient
Which Variables Are Explaining the Most?
Comparing the Standardized Regression Coefficients
Comparing the Correlation Coefficients
12.2 Pitfalls and Problems in Multiple Regression 459
Multicollinearity: Are the Explanatory Variables Too Similar?
EXAMPLE Predicting Market Value from Sales, Employees, and Assets 462
Variable Selection: Are You Using the Wrong Variables?
Prioritizing the List of X Variables
Automating the Variable Selection Process
Model Misspecification: Does the Regression Equation Have the Wrong Form?
Exploring the Data to See Nonlinearity or Unequal Variability
Using the Diagnostic Plot to Decide if You Have a Problem
Using Percent Changes to Model an Economic Time Series
EXAMPLE Predicting Dividends from Sales of Nondurable and Durable Goods 477
12.3 Dealing with Nonlinear Relationships and Unequal Variability 478
Transforming to a Linear Relationship: Interpreting the results
EXAMPLE Magazine Ads Transformed and Interpreted 481
Fitting a Curve with Polynomial Regression
xxiv ¦ Contents
EXAMPLE Optimizing the Yield of a Production Process 484
Modeling Interaction between Two X Variables
12.4 Indicator Variables: Predicting from Categories 488
Interpreting and Testing Regression Coefficients for Indicator Variables
EXAMPLE Estimating the Impact of Gender on Salary after Adjusting
for Experience 489
Separate Regressions
Case Controlling Quality of Production 509
13 Report Writing Communicating the Results of a Multiple Regression 512
13.1 How to Organize Your Report 513
The Executive Summary Paragraph
The Introduction Section
The Analysis and Methods Section
The Conclusion and Summary Section
Including References
The Appendix Section
13.2 Hints and Tips 517
Think about Your Audience
What to Write First? Next? Last?
Other Sources
13.3 Example: A Quick Pricing Formula for Customer Inquiries 518
14 Time Series Understanding Changes Over Time 530
14.1 An Overview of Time-Series Analysis 531
EXAMPLE The Stock Market Is a Random Walk 532
EXAMPLE Aerospace Orders Showed Steady Growth 532
EXAMPLE Total Retail Sales Show Growth and Seasonal Variation 535
EXAMPLE Interest Rates 537
14.2 Trend-Seasonal Analysis 540
EXAMPLE Washington Water Power Company 541
Trend and Cyclic: The Moving Average
Seasonal Index: The Average Ratio-to-Moving-Average Indicates Seasonal
Behavior
Seasonal Adjustment: The Series Divided by the Seasonal Index
Long-Term Trend and Seasonally Adjusted Forecast: The Regression Line
Forecast: The Seasonalized Trend
14.3 Modeling Cyclic Behavior Using Box-Jenkins ARIMA Processes 551
A Random Noise Process Has No Memory: The Starting Point
An Autoregressive (AR) Process Remembers Where It Was
A Moving-Average (MA) Process Has a Limited Memory
The Autoregressive Moving-Average (ARMA) Process Combines AR and MA
EXAMPLE Forecasting the Unemployment Rate Using an ARMA Process 560
A Pure Integrated (I) Process Remembers Where It Was and Then Moves at Random
The Autoregressive Integrated Moving-Average (ARIMA) Process Remembers
Its Changes
¦ Contents xxv
IiJI»?* J*liH Methods and Applications 579
15 ANOVA Testing for Differences among Many Samples, and Much More 581
15.1 Using Box Plots to Look at Many Samples at Once 582
EXAMPLE Comparing the Quality of Your Suppliers Products 583
15.2 The F Test Tells You if the Averages Are Significantly Different 583
The Data Set and Sources of Variation
The Assumptions
The Hypotheses
The F Statistic
The F Table
The Result of the F Test
Computer Output: The One-Way ANOVA Table
15.3 The Least-Significant-Difference Test: Which Pairs Are Different? 595
15.4 More Advanced ANOVA Designs 598
Variety Is the Spice of Life
Two-Way ANOVA
Three-Way and More
Analysis of Covariance (ANCOVA)
Multivariate Analysis of Variance (MANOVA)
How to Read an ANOVA Table
EXAMPLE The Effect of Price Changes and Product Type on Grocery Sales 601
EXAMPLE Jokes in the Workplace 602
16 Nonparametrics Testing with Ordinal Data or Nonnormal Distributions 613
16.1 Testing the Median against a Known Reference Value 615
The Sign Test
The Hypotheses
The Assumption
EXAMPLE Comparing Local to National Family Income 619
16.2 Testing for Differences in Paired Data 619
Using the Sign Test on the Differences
The Hypotheses
The Assumption
EXAMPLE Rating Two Advertisements 621
16.3 Testing to See if Two Unpaired Samples Are Significantly Different 621
The Procedure Is Based on the Ranks of All of the Data
The Hypotheses
The Assumptions
EXAMPLE Fixed-Rate and Adjustable-Rate Mortgage Applicants 623
17 Chi-Squared Analysis Testing for Patterns in Qualitative Data 627
17.1 Summarizing Qualitative Data by Using Counts and Percentages 638
17.2 Testing if Population Percentages Are Equal to Known Reference Values 639
The Chi-Squared Test for Equality of Percentages
EXAMPLE Quality Problems Categorized by Their Causes 640
17.3 Testing for Association between Two Qualitative Variables 644
The Meaning of Independence
xxvi ¦ Contents
The Chi-Squared Test for Independence
EXAMPLE Is Your Market Segmented? 646
18 Quality Control Recognizing and Managing Variation 659
18.1 Processes and Causes of Variation 661
The Pareto Diagram Shows Where to Focus Attention
18.2 Control Charts and How to Read Them 664
The Control Limits Show if a Single Observation Is Out of Control
How to Spot Trouble Even within the Control Limits
18.3 Charting a Quantitive Measurement with X and R Charts 668
EXAMPLE Net Weight of Dishwasher Detergent 670
18.4 Charting the Percent Defective 672
EXAMPLE Filling Out Purchase Orders 674
Appendix A Employee Database 687
Appendix B Self-Test: Solutions to Selected Problems and Database
Exercises 689
Appendix C Statistical Tables 703
Glossary 725
Index 732
|
| adam_txt |
Contents in Brief
13311111 Introduction and Descriptive Statistics 1
1 Introduction Defining the Role of Statistics in Business 3
2 Data Structures Classifying the Various Types of Data Sets 13
3 Histograms Looking at the Distribution of the Data 26
4 Landmark Summaries Interpreting Typical Values and Percentiles 64
5 Variability Dealing with Diversity 105
6 Probability Understanding Random Situations 143
7 Random Variables Working with Uncertain Numbers 187
flEZStlllHi Statistical Inference 231
8 Random Sampling Planning Ahead for Data Gathering 233
9 Confidence Intervals Admitting that Estimates Are Not Exact 275
10 Hypothesis Testing Deciding between Reality and Coincidence 311
BES!!I23!9llli Regression and Time Series 365
11 Correlation and Regression Measuring and Predicting Relationships 367
12 Multiple Regression Predicting One Factor from Several Others 435
13 Report Writing Communicating the Results of a Multiple Regression 512
14 Time Series Understanding Changes over Time 530
WKMSSBMIIi Methods and Applications 579
15 ANOVA Testing for Differences among Many Samples, and Much More 581
16 Nonparametrics Testing with Ordinal Data or Nonnormal Distributions 613
17 Chi-Squared Analysis Testing for Patterns in Qualitative Data 637
18 Quality Control Recognizing and Managing Variation 659
Appendix A Employee Database 687
Appendix B Self-Test: Solutions to Selected Problems
and Database Exercises 689
Appendix C Statistical Tables 703
Glossary 727
Index 739
Contents
^^^^^^B Introduction and Descriptive Statistics 1
1 Introduction Defining the Role of Statistics in Business 3
1.1 Why Statistics 3
Why Should You Learn Statistics?
Is Statistics Difficult?
Does Learning Statistics Decrease Your Decision-Making Flexibility?
1.2 What Is Statistics? 4
Statistics Looks at the Big Picture
Statistics Doesn't Ignore the Individual
EXAMPLE Data in Management 5
Looking at Data
Statistics in Management
1.3 The Four Basic Activities of Statistics 6
Designing a Plan for Data Collection
Exploring the Data
Estimating an Unknown Quantity
Hypothesis Testing
EXAMPLE Statistical Quality Control 8
1.4 What Is Probability? 9
1.5 General Advice 10
2 Data Structures Classifying the Various Types of Data Sets 13
2.1 How Many Variables? 13
Univariate Data
Bivariate Data
Multivariate Data
2.2 Quantitative Data: Numbers 15
Discrete Quantitative Data
Continuous Quantitative Data
¦ Contents xvii
Watch Out for Meaningless Numbers
EXAMPLE Alphabetical Order of States 17
2.3 Qualitative Data: Categories 17
Ordinal Qualitative Data
Nominal Qualitative Data
2.4 Time-Series and Cross-Sectional Data 18
3 Histograms Looking at the Distribution of the Data 26
3.1 A List of Data 26
EXAMPLE Performance of Regional Sales Managers 26
EXAMPLE Household Size 27
The Number Line
3.2 Using a Histogram to Display the Frequencies 28
EXAMPLE Mortgage Interest Rates 28
Histograms and Bar Charts
EXAMPLE Salary Offers to College Graduates 31
3.3 Normal Distributions 32
3.4 Skewed Distributions and Data Transformation 34
EXAMPLE Assets of Commercial Banks 35
EXAMPLE Populations of States 35
The Trouble with Skewness
Transformation to the Rescue
EXAMPLE Transforming State Populations 38
Interpreting the Logarithm
3.5 Bimodal Distributions 40
EXAMPLE Money Market Yields 40
Is It Really Bimodal?
EXAMPLE The Cost of a Day in the Hospital 41
3.6 Outliers 43
Dealing with Outliers
EXAMPLE Research and Development Expenses 44
EXAMPLE Changes in Television Advertising 45
3.7 Histograms by Hand: Stem-and-Leaf 49
EXAMPLE Employees in Food Services 49
Case Let's Control Waste in Production 62
4 Landmark Summaries Interpreting Typical Values and Percentiles 64
4.1 What Is the Most Typical Value? 64
The Average: A Typical Value for Quantitative Data
EXAMPLE How Much Will Consumers Spend? 66
EXAMPLE How Many Defective Parts? 67
The Weighted Average: Adjusting for Importance
EXAMPLE Your Grade Point Average 69
EXAMPLE The Firm's Cost of Capital 70
EXAMPLE Adjusting for Misrepresentation 71
The Median: A Typical Value for Quantitative and Ordinal Data
EXAMPLE The Crash of October 19, 1987: Stocks Drop at Opening 73
EXAMPLE Personal Incomes 74
EXAMPLE Stages of Completion of Inventory 76
xviii ¦ Contents
The Mode: A Typical Value Even for Nominal Data
EXAMPLE Voting 77
EXAMPLE Quality Control: Controlling Variation in Manufacturing 78
EXAMPLE Inventory Completion Stages Revisited 79
Which Summary Should You Use?
4.2 What Percentile Is It? 81
Extremes, Quartiles, and Box Plots
EXAMPLE Executive Compensation 83
The Cumulative Distribution Function Displays the Percentiles
EXAMPLE Business Failures 88
Case Managerial Projections for Production and Marketing, or
"The Case of the Suspicious Customer" 102
5 Variability Dealing with Diversity 105
5.1 The Standard Deviation: The Traditional Choice 106
Definition and Formula for the Standard Deviation and the Variance
Using a Calculator or a Computer
Interpreting the Standard Deviation
EXAMPLE The Advertising Budget 109
EXAMPLE Customer Diversity 110
Interpreting the Standard Deviation for a Normal Distribution
EXAMPLE A Quality Control Chart For Picture-Scanning Quality 112
EXAMPLE Stock Market Returns Vary from Day to Day 112
EXAMPLE The Stock Market Crash of 1987: 19 Standard Deviations! 116
EXAMPLE Market Volatility before and after the Crash 118
EXAMPLE Diversification in the Stock Market 119
The Sample and the Population Standard Deviations
5.2 The Range: Quick and Superficial 120
EXAMPLE Employee Salaries 121
EXAMPLE Duration of Hospital Stays 122
5.3 The Coefficient of Variation: A Relative Variability Measure 123
EXAMPLE Uncertainty in Portfolio Performance 124
EXAMPLE Employee Productivity in Telemarketing 124
5.4 Effects of Adding to or Rescaling the Data 125
EXAMPLE Uncertainty of Costs in Japanese Yen and in U.S. Dollars 125
EXAMPLE Total Cost and Units Produced 126
Case Should We Keep or Get Rid of This Supplier? 140
6 Probability Understanding Random Situations 143
6.1 An Example: Is It behind Door Number 1, Door Number 2, or
Door Number 3? 144
6.2 How Can You Analyze Uncertainty? 145
The Random Experiment: A Precise Definition of a Random Situation
The Sample Space: A List of What Might Happen
The Outcome: What Actually Happens
Events: Either They Happen or They Don't
6.3 How Likely Is an Event? 149
¦ Contents xix
Every Event Has a Probability
Where Do Probabilities Come From?
Relative Frequency and the Law of Large Numbers
EXAMPLE How Variable Is Today's Extra-High-Quality Production? 152
Theoretical Probability
The Equally Likely Rule
EXAMPLE Coin Tossing and Cards 153
EXAMPLE Gender and Hiring 154
EXAMPLE Defective Raw Materials 154
Subjective Probability
EXAMPLE Settling a Lawsuit 154
Bayesian and Non-Bayesian Analysis
6.4 How Can You Combine Information about More than One Event? 156
Venn Diagrams Help You See All the Possibilities
Not an Event
The Complement {not) Rule
One Event and Another
What if Both Events Can't Happen at Once?
The Intersection (and) Rule for Mutually Exclusive Events
One Event or Another
The Union (or) Rule for Mutually Exclusive Events
Finding or from and, and Vice Versa
One Event Given Another: Reflecting Current Information
The Rule for Finding a Conditional Probability Given Certain Information
Conditional Probabilities for Mutually Exclusive Events
Independent Events
EXAMPLE Women in Executive Positions 164
EXAMPLE Market Efficiency 165
The Intersection (and) Rule for Independent Events
EXAMPLE Risk Assessment for a Large Power Plant 166
The Relationship between Independent and Mutually Exclusive Events
6.5 What's the Best Way to Solve Probability Problems? 166
Probability Trees
EXAMPLE Managing Software Support 168
Rules for Probability Trees
EXAMPLE Drug Testing of Employees 168
EXAMPLE A Pilot Project Helps Predict Success of a Product Launch 171
EXAMPLE Solution to "Is It behind Door Number 1, 2, or 3" 174
Joint Probability Tables
Case Whodunit? Who, if Anyone, Is Responsible for the Recent Rise
in the Defect Rate? 185
7 Random Variables Working with Uncertain Numbers 187
7.1 Discrete Random Variables 188
EXAMPLE Profit under Various Economic Scenarios 188
Finding the Mean and Standard Deviation
EXAMPLE Evaluating Risk and Return 190
7.2 The Binomial Distribution 192
Definition of Binomial Distribution and Proportion
EXAMPLE How Many Orders are Placed?: The Hard Way to Compute 193
XX ¦ Contents
Finding the Mean and Standard Deviation the Easy Way
EXAMPLE Recalling Advertisements 196
Finding the Probabilities
EXAMPLE How Many Major Customers Will Call Tomorrow? 198
EXAMPLE How Many Logic Analyzers to Schedule for Manufacturing? 199
7.3 The Normal Distribution 201
Visualize Probabilities as the Area under the Curve
The Standard Normal Distribution Z and Its Probabilities
Solving Word Problems for Normal Probabilities
The Four Different Probability Calculations
Be Careful: Things Need Not Be Normal!
EXAMPLE A Lottery (or Risky Project) 210
7.4 The Normal Approximation to the Binomial 210
EXAMPLE High-and Low-Speed Microprocessors 212
EXAMPLE Polling the Electorate 214
7.5 Two Other Distributions: The Poisson and the Exponential 215
The Poisson Distribution
EXAMPLE How Many Warranty Returns? 216
EXAMPLE How Many Phone Calls? 218
The Exponential Distribution
Case The Option Value of an Oil Lease 229
BE333EBI Statistical Inference 231
8 Random Sampling Planning Ahead for Data Gathering 233
8.1 Populations and Samples 234
How Do You Choose a Representative Sample?
A Sample Statistic and a Population Parameter
8.2 The Random Sample 237
Selecting a Random Sample
Sampling by Shuffling the Population
EXAMPLE Auditing 240
EXAMPLE A Pilot Study of Large Paper and Forest Products Firms 241
8.3 The Sampling Distribution and the Central Limit Theorem 242
EXAMPLE How Much Do Shoppers Spend? 244
EXAMPLE Consistency in Bubble Gum Production 247
8.4 A Standard Error Is an Estimated Standard Deviation 248
How Close Is the Sample Average to the Population Mean?
EXAMPLE Shopping Trips 250
Correcting for Small Populations
EXAMPLE Quality of the Day's Production 253
The Standard Error of the Binomial Proportion
EXAMPLE A Consumer Survey 254
8.5 Other Sampling Methods 255
The Stratified Random Sample
EXAMPLE Adjusting for Sophistication of the Consumer 257
EXAMPLE The Price of a Typical Suit in a Department Store 259
The Systematic Sample
Case Can This Survey Be Saved? 273
¦ Contents xxi
9 Confidence Intervals Admitting that Estimates Are Not Exact 275
9.1 The Confidence Interval for a Population Mean or a Population Percentage 277
The t Table and the t Distribution
The Widely Used 95% Confidence Interval
EXAMPLE Controlling the Average Thickness of Paper 282
EXAMPLE Election Polling (a Binomial Situation) 283
Other Confidence Levels
EXAMPLE Average Selling Price as Determined through Rebates 286
EXAMPLE Yield of a Manufacturing Process 287
9.2 Assumptions Needed for Validity 287
Random Sampling
EXAMPLE Forecasting Interest Rates 289
Normal Distribution
9.3 Interpreting a Confidence Interval 292
Which Event Has a 95% Probability?
Your Lifetime Track Record
9.4 One-Sided Confidence Intervals 294
Be Careful! You Can't Always Use a One-Sided Interval
Computing the One-Sided Interval
EXAMPLE The Savings of a New System 296
EXAMPLE Travel Costs 296
9.5 Prediction Intervals 297
EXAMPLE How Long until Your Order Is Filled? 298
Case Promising Results from a Specialty Catalog Survey 310
10 Hypothesis Testing Deciding between Reality and Coincidence 311
10.1 Hypotheses Are Not Created Equal! 312
The Null Hypothesis
The Research Hypothesis
What Will the Result Tell You?
Examples of Hypotheses
10.2 Testing the Population Mean against a Known Reference Value 314
Using Confidence Intervals: The Easy Way
EXAMPLE Does the "Yield-Increasing" Additive Really Work? 316
EXAMPLE Employee Stock Ownership and Product Quality 319
EXAMPLE Pushing the Limits of Production (a Binomial Situation) 320
The t Statistic: Another Way, Same Answer
10.3 Interpreting a Hypothesis Test 324
Errors: Type I and Type II
Assumptions Needed for Validity
Hypotheses Have No Probabilities of Being True or False
Statistical Significance, Test Levels, p-Values, and Computer Output
10.4 One-Sided Testing 330
Using a One-Sided Confidence Interval
EXAMPLE Launching a New Product 332
How to Perform the Test
The One-Sided t Test: Another Way, Same Answer
EXAMPLE Launching a New Product, Revisited 333
How to Perform the Test
xxii ¦ Contents
EXAMPLE Will Costs Go Down? 335
EXAMPLE Can You Create Value by Changing Your Firm's Name? 338
10.5 Testing Whether or Not a New Observation Comes from the Same
Population 339
EXAMPLE Is This System under Control? 339
10.6 Testing Two Samples 340
The Paired t Test
EXAMPLE Reactions to Advertising 341
The Unpaired t Test
EXAMPLE Gender Discrimination and Salaries 344
EXAMPLE Your Productivity versus Theirs 346
Case So Many Ads, So Little Time 363
BE332I5BI Regression and Time Series 365
11 Correlation and Regression Measuring and Predicting Relationships 367
11.1 Exploring Relationships Using Scatterplots and Correlations 368
The Scatterplot Shows You the Relationship
Correlation Measures the Strength of the Relationship
The Formula for the Correlation
The Various Types of Relationships
Linear Relationship
EXAMPLE Nielsen TV Ratings and People Meters 372
EXAMPLE Mergers 373
EXAMPLE Mortgage Rates and Fees 374
No Relationship
EXAMPLE "Momentum" and the Stock Market 376
Nonlinear Relationship
EXAMPLE Index Options 378
EXAMPLE Yield and Temperature 381
Unequal Variability
EXAMPLE Optical Cable 382
Clustering
EXAMPLE Flower Bonds 384
Outliers
EXAMPLE Number Produced and Cost 386
Correlation Is Not Causation
EXAMPLE Apartment Rents and Vacancy Rates 388
11.2 Regression: Prediction of One Thing from Another 389
A Straight Line Summarizes a Linear Relationship
Straight Lines
Finding a Line Based on Data
EXAMPLE Fixed and Variable Costs 393
EXAMPLE Territory and Sales 394
How Useful Is the Line?
The Standard Error of Estimate: How Large Are the Prediction Errors?
R2: How Much Is Explained?
Confidence Intervals and Hypothesis Tests for Regression
The Linear Model Assumption Defines the Population
¦ Contents xxiii
Standard Errors for the Slope and Intercept
Confidence Intervals for Regression Coefficients
EXAMPLE Variable Costs of Production 403
Testing whether the Relationship Is Real or Coincidence
Other Methods of Testing the Significance of a Relationship
Computer Output for the Production Cost Data
EXAMPLE Momentum in the Stock Market Revisited 404
Other Tests of a Regression Coefficient
A New Observation: Uncertainty and the Confidence Interval
The Mean of Y: Uncertainty and the Confidence Interval
Regression Can Be Misleading
The Linear Model May Be Wrong
Predicting Intervention from Observed Experience Is Difficult
The Intercept May Not Be Meaningful
Explaining Y from X versus Explaining X from Y
A Hidden "Third Factor" May Be Helpful
Case Just One More Production Step: Is It Worthwhile? 434
12 Multiple Regression Predicting One Factor from Several Others 435
12.1 Interpreting the Results of a Multiple Regression 436
EXAMPLE Magazine Ads 437
Regression Coefficients and the Regression Equation
Interpreting the Regression Coefficients
Predictions and Prediction Errors
How Good Are the Predictions?
Typical Prediction Error: Standard Error of Estimate
Percent Variation Explained: R2
Inference in Multiple Regression
Assumptions
Is the Model Significant? The F Test or R2 Test
Tables of Critical Values for Testing R2
Which Variables Are Significant? A t Test for Each Coefficient
Other Tests for a Regression Coefficient
Which Variables Are Explaining the Most?
Comparing the Standardized Regression Coefficients
Comparing the Correlation Coefficients
12.2 Pitfalls and Problems in Multiple Regression 459
Multicollinearity: Are the Explanatory Variables Too Similar?
EXAMPLE Predicting Market Value from Sales, Employees, and Assets 462
Variable Selection: Are You Using the Wrong Variables?
Prioritizing the List of X Variables
Automating the Variable Selection Process
Model Misspecification: Does the Regression Equation Have the Wrong Form?
Exploring the Data to See Nonlinearity or Unequal Variability
Using the Diagnostic Plot to Decide if You Have a Problem
Using Percent Changes to Model an Economic Time Series
EXAMPLE Predicting Dividends from Sales of Nondurable and Durable Goods 477
12.3 Dealing with Nonlinear Relationships and Unequal Variability 478
Transforming to a Linear Relationship: Interpreting the results
EXAMPLE Magazine Ads Transformed and Interpreted 481
Fitting a Curve with Polynomial Regression
xxiv ¦ Contents
EXAMPLE Optimizing the Yield of a Production Process 484
Modeling Interaction between Two X Variables
12.4 Indicator Variables: Predicting from Categories 488
Interpreting and Testing Regression Coefficients for Indicator Variables
EXAMPLE Estimating the Impact of Gender on Salary after Adjusting
for Experience 489
Separate Regressions
Case Controlling Quality of Production 509
13 Report Writing Communicating the Results of a Multiple Regression 512
13.1 How to Organize Your Report 513
The Executive Summary Paragraph
The Introduction Section
The Analysis and Methods Section
The Conclusion and Summary Section
Including References
The Appendix Section
13.2 Hints and Tips 517
Think about Your Audience
What to Write First? Next? Last?
Other Sources
13.3 Example: A Quick Pricing Formula for Customer Inquiries 518
14 Time Series Understanding Changes Over Time 530
14.1 An Overview of Time-Series Analysis 531
EXAMPLE The Stock Market Is a Random Walk 532
EXAMPLE Aerospace Orders Showed Steady Growth 532
EXAMPLE Total Retail Sales Show Growth and Seasonal Variation 535
EXAMPLE Interest Rates 537
14.2 Trend-Seasonal Analysis 540
EXAMPLE Washington Water Power Company 541
Trend and Cyclic: The Moving Average
Seasonal Index: The Average Ratio-to-Moving-Average Indicates Seasonal
Behavior
Seasonal Adjustment: The Series Divided by the Seasonal Index
Long-Term Trend and Seasonally Adjusted Forecast: The Regression Line
Forecast: The Seasonalized Trend
14.3 Modeling Cyclic Behavior Using Box-Jenkins ARIMA Processes 551
A Random Noise Process Has No Memory: The Starting Point
An Autoregressive (AR) Process Remembers Where It Was
A Moving-Average (MA) Process Has a Limited Memory
The Autoregressive Moving-Average (ARMA) Process Combines AR and MA
EXAMPLE Forecasting the Unemployment Rate Using an ARMA Process 560
A Pure Integrated (I) Process Remembers Where It Was and Then Moves at Random
The Autoregressive Integrated Moving-Average (ARIMA) Process Remembers
Its Changes
¦ Contents xxv
IiJI»?* J*liH Methods and Applications 579
15 ANOVA Testing for Differences among Many Samples, and Much More 581
15.1 Using Box Plots to Look at Many Samples at Once 582
EXAMPLE Comparing the Quality of Your Suppliers' Products 583
15.2 The F Test Tells You if the Averages Are Significantly Different 583
The Data Set and Sources of Variation
The Assumptions
The Hypotheses
The F Statistic
The F Table
The Result of the F Test
Computer Output: The One-Way ANOVA Table
15.3 The Least-Significant-Difference Test: Which Pairs Are Different? 595
15.4 More Advanced ANOVA Designs 598
Variety Is the Spice of Life
Two-Way ANOVA
Three-Way and More
Analysis of Covariance (ANCOVA)
Multivariate Analysis of Variance (MANOVA)
How to Read an ANOVA Table
EXAMPLE The Effect of Price Changes and Product Type on Grocery Sales 601
EXAMPLE Jokes in the Workplace 602
16 Nonparametrics Testing with Ordinal Data or Nonnormal Distributions 613
16.1 Testing the Median against a Known Reference Value 615
The Sign Test
The Hypotheses
The Assumption
EXAMPLE Comparing Local to National Family Income 619
16.2 Testing for Differences in Paired Data 619
Using the Sign Test on the Differences
The Hypotheses
The Assumption
EXAMPLE Rating Two Advertisements 621
16.3 Testing to See if Two Unpaired Samples Are Significantly Different 621
The Procedure Is Based on the Ranks of All of the Data
The Hypotheses
The Assumptions
EXAMPLE Fixed-Rate and Adjustable-Rate Mortgage Applicants 623
17 Chi-Squared Analysis Testing for Patterns in Qualitative Data 627
17.1 Summarizing Qualitative Data by Using Counts and Percentages 638
17.2 Testing if Population Percentages Are Equal to Known Reference Values 639
The Chi-Squared Test for Equality of Percentages
EXAMPLE Quality Problems Categorized by Their Causes 640
17.3 Testing for Association between Two Qualitative Variables 644
The Meaning of Independence
xxvi ¦ Contents
The Chi-Squared Test for Independence
EXAMPLE Is Your Market Segmented? 646
18 Quality Control Recognizing and Managing Variation 659
18.1 Processes and Causes of Variation 661
The Pareto Diagram Shows Where to Focus Attention
18.2 Control Charts and How to Read Them 664
The Control Limits Show if a Single Observation Is Out of Control
How to Spot Trouble Even within the Control Limits
18.3 Charting a Quantitive Measurement with X and R Charts 668
EXAMPLE Net Weight of Dishwasher Detergent 670
18.4 Charting the Percent Defective 672
EXAMPLE Filling Out Purchase Orders 674
Appendix A Employee Database 687
Appendix B Self-Test: Solutions to Selected Problems and Database
Exercises 689
Appendix C Statistical Tables 703
Glossary 725
Index 732 |
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| author | Siegel, Andrew F. 1950- |
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| edition | 3. ed. |
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| illustrated | Illustrated |
| index_date | 2024-07-02T22:31:34Z |
| indexdate | 2024-07-09T21:23:38Z |
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| isbn | 0256257396 0071146520 |
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| spelling | Siegel, Andrew F. 1950- Verfasser (DE-588)129753777 aut Practical business statistics Andrew F. Siegel 3. ed. Chicago <<[u.a.]>> Irwin 1997 XXVI, 752 S. zahlr. Ill. u. graph. Darst. txt rdacontent n rdamedia nc rdacarrier The Irwin series in statistics Wirtschaftsstatistik (DE-588)4066517-3 gnd rswk-swf Betriebswirtschaftliche Statistik (DE-588)4006243-0 gnd rswk-swf 1\p (DE-588)4123623-3 Lehrbuch gnd-content Wirtschaftsstatistik (DE-588)4066517-3 s DE-604 Betriebswirtschaftliche Statistik (DE-588)4006243-0 s 2\p DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016835123&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 2\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_exact_search_txtP | 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 |
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