Introduction to Statistics Through Resampling Methods and Microsoft Office Excel [推广有奖]

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Introduction to Statistics Through Resampling Methods and Microsoft Office Excel

Phillip I. Good

Wiley Statistics

pdf p.246 3.98M

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Preface xi
1. Variation (or What Statistics Is All About) 1
1.1. Variation 1
1.2. Collecting Data 2
1.3. Summarizing Your Data 3
1.3.1 Learning to Use Excel 4
1.4. Reporting Your Results: the Classroom Data 7
1.4.1 Picturing Data 10
1.4.2 Displaying Multiple Variables 10
1.4.3 Percentiles of the Distribution 15
1.5. Types of Data 20
1.5.1 Depicting Categorical Data 21
1.5.2 From Observations to Questions 23
1.6. Measures of Location 23
1.6.1 Which Measure of Location? 25
1.6.2 The Bootstrap 27
1.7. Samples and Populations 30
1.7.1 Drawing a Random Sample 32
1.7.2 Ensuring the Sample is Representative 34
1.8. Variation—Within and Between 34
1.9. Summary and Review 36
2. Probability 39
2.1. Probability 39
2.1.1 Events and Outcomes 41
2.1.2 Venn Diagrams 41
2.2. Binomial 43
2.2.1 Permutations and Rearrangements 45
2.2.2 Back to the Binomial 472.2.3 The Problem Jury 47
2.2.4 Properties of the Binomial 48
2.2.5 Multinomial 52
2.3. Conditional Probability 53
2.3.1 Market Basket Analysis 55
2.3.2 Negative Results 56
2.4. Independence 57
2.5. Applications to Genetics 59
2.6. Summary and Review 60
3. Distributions 63
3.1. Distribution of Values 63
3.1.1 Cumulative Distribution Function 64
3.1.2 Empirical Distribution Function 66
3.2. Discrete Distributions 66
3.3. Poisson: Events Rare in Time and Space 68
3.3.1 Applying the Poisson 69
3.3.2 Comparing Empirical and Theoretical Poisson
Distributions 70
3.4. Continuous Distributions 71
3.4.1 The Exponential Distribution 71
3.4.2 The Normal Distribution 72
3.4.3 Mixtures of Normal Distributions 74
3.5. Properties of Independent Observations 74
3.6. Testing a Hypothesis 76
3.6.1 Analyzing the Experiment 77
3.6.2 Two Types of Errors 80
3.7. Estimating Effect Size 81
3.7.1 Confidence Interval for Difference in Means 82
3.7.2 Are Two Variables Correlated? 84
3.7.3 Using Confidence Intervals to Test Hypotheses 86
3.8. Summary and Review 87
4. Testing Hypotheses 89
4.1. One-Sample Problems 89
4.1.1 Percentile Bootstrap 89
4.1.2 Parametric Bootstrap 90
4.1.3 Student’s t 91
4.2. Comparing Two Samples 93
4.2.1 Comparing Two Poisson Distributions 93
4.2.2 What Should We Measure? 944.2.3 Permutation Monte Carlo 95
4.2.4 Two-Sample t-Test 97
4.3. Which Test Should We Use? 97
4.3.1 p Values and Significance Levels 98
4.3.2 Test Assumptions 98
4.3.3 Robustness 99
4.3.4 Power of a Test Procedure 100
4.3.5 Testing for Correlation 101
4.4. Summary and Review 104
5. Designing an Experiment or Survey 105
5.1. The Hawthorne Effect 106
5.1.1 Crafting an Experiment 106
5.2. Designing an Experiment or Survey 108
5.2.1 Objectives 109
5.2.2 Sample from the Right Population 110
5.2.3 Coping with Variation 112
5.2.4 Matched Pairs 113
5.2.5 The Experimental Unit 114
5.2.6 Formulate Your Hypotheses 114
5.2.7 What Are You Going to Measure? 115
5.2.8 Random Representative Samples 116
5.2.9 Treatment Allocation 117
5.2.10 Choosing a Random Sample 118
5.2.11 Ensuring that Your Observations are
Independent 119
5.3. How Large a Sample? 120
5.3.1 Samples of Fixed Size 121
• Known Distribution 122
• Almost Normal Data 125
• Bootstrap 127
5.3.2 Sequential Sampling 129
• Stein’s Two-Stage Sampling Procedure 129
• Wald Sequential Sampling 129
• Adaptive Sampling 133
5.4. Meta-Analysis 134
5.5. Summary and Review 135
6. Analyzing Complex Experiments 137
6.1. Changes Measured in Percentages 137
6.2. Comparing More Than Two Samples 1386.2.1 Programming the Multisample Comparison
with Excel 139
6.2.2 What Is the Alternative? 141
6.2.3 Testing for a Dose Response or Other Ordered
Alternative 141
6.3. Equalizing Variances 145
6.4. Stratified Samples 147
6.5. Categorical Data 148
6.5.1 One-Sided Fisher’s Exact Test 150
6.5.2 The Two-Sided Test 151
6.5.3 Multinomial Tables 152
6.5.4 Ordered Categories 153
6.6. Summary and Review 154
7. Developing Models 155
7.1. Models 155
7.1.1 Why Build Models? 156
7.1.2 Caveats 158
7.2. Regression 159
7.2.1 Linear Regression 160
7.3. Fitting a Regression Equation 161
7.3.1 Ordinary Least Squares 162
• Types of Data 166
7.3.2 Least Absolute Deviation Regression 168
7.3.3 Errors-in-Variables Regression 168
7.3.4 Assumptions 171
7.4. Problems with Regression 172
7.4.1 Goodness of fit versus prediction 172
7.4.2 Which Model? 173
7.4.3 Measures of Predictive Success 174
7.4.4 Multivariable Regression 175
7.5. Quantile Regression 182
7.6. Validation 183
7.6.1 Independent Verification 183
7.6.2 Splitting the Sample 184
7.6.3 Cross-Validation with the Bootstrap 185
7.7. Classification and Regression Trees 186
7.8. Data Mining 190
7.9. Summary and Review 1938. Reporting Your Findings 195
8.1. What to Report 195
8.2. Text, Table, or Graph? 199
8.3. Summarizing Your Results 200
8.3.1 Center of the Distribution 201
8.3.2 Dispersion 203
8.4. Reporting Analysis Results 204
8.4.1 p Values? Or Confidence Intervals? 205
8.5. Exceptions Are the Real Story 206
8.5.1 Nonresponders 206
8.5.2 The Missing Holes 207
8.5.3 Missing Data 207
8.5.4 Recognize and Report Biases 208
8.6. Summary and Review 209
9. Problem Solving 211
9.1. The Problems 211
9.2. Solving Practical Problems 215
9.2.1 The Data’s Provenance 215
9.2.2 Inspect the Data 216
9.2.3 Validate the Data Collection Methods 217
9.2.4 Formulate Hypotheses 217
9.2.5 Choosing a Statistical Methodology 218
9.2.6 Be Aware of What You Don’t Know 218
9.2.7 Qualify Your Conclusions 218
Appendix: An Microsoft Office Excel Primer 221
Index to Excel and Excel Add-In Functions 227
Subject Index 229
CONTENTS ix