LEADER 05250nam 22006014a 450 001 9910831196003321 005 20230617004213.0 010 $a1-280-27720-3 010 $a9786610277209 010 $a0-470-32514-3 010 $a0-471-74177-9 010 $a0-471-74176-0 035 $a(CKB)1000000000355096 035 $a(EBL)232613 035 $a(OCoLC)171257359 035 $a(SSID)ssj0000182935 035 $a(PQKBManifestationID)11168332 035 $a(PQKBTitleCode)TC0000182935 035 $a(PQKBWorkID)10193752 035 $a(PQKB)10575728 035 $a(MiAaPQ)EBC232613 035 $a(EXLCZ)991000000000355096 100 $a20050204d2005 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aIntroduction to statistics through resampling methods and Microsoft Office Excel$b[electronic resource] /$fPhillip I. Good 210 $aHoboken, N.J. $cWiley-Interscience$dc2005 215 $a1 online resource (245 p.) 300 $aDescription based upon print version of record. 311 $a0-471-73191-9 320 $aIncludes bibliographical references and index. 327 $aINTRODUCTION TO STATISTICS THROUGH RESAMPLING METHODS AND MICROSOFT OFFICE EXCELŽ; Contents; Preface; 1. Variation (or What Statistics Is All About); 1.1. Variation; 1.2. Collecting Data; 1.3. Summarizing Your Data; 1.3.1 Learning to Use Excel; 1.4. Reporting Your Results: the Classroom Data; 1.4.1 Picturing Data; 1.4.2 Displaying Multiple Variables; 1.4.3 Percentiles of the Distribution; 1.5. Types of Data; 1.5.1 Depicting Categorical Data; 1.5.2 From Observations to Questions; 1.6. Measures of Location; 1.6.1 Which Measure of Location?; 1.6.2 The Bootstrap; 1.7. Samples and Populations 327 $a1.7.1 Drawing a Random Sample1.7.2 Ensuring the Sample is Representative; 1.8. Variation-Within and Between; 1.9. Summary and Review; 2. Probability; 2.1. Probability; 2.1.1 Events and Outcomes; 2.1.2 Venn Diagrams; 2.2. Binomial; 2.2.1 Permutations and Rearrangements; 2.2.2 Back to the Binomial; 2.2.3 The Problem Jury; 2.2.4 Properties of the Binomial; 2.2.5 Multinomial; 2.3. Conditional Probability; 2.3.1 Market Basket Analysis; 2.3.2 Negative Results; 2.4. Independence; 2.5. Applications to Genetics; 2.6. Summary and Review; 3. Distributions; 3.1. Distribution of Values 327 $a3.1.1 Cumulative Distribution Function3.1.2 Empirical Distribution Function; 3.2. Discrete Distributions; 3.3. Poisson: Events Rare in Time and Space; 3.3.1 Applying the Poisson; 3.3.2 Comparing Empirical and Theoretical Poisson Distributions; 3.4. Continuous Distributions; 3.4.1 The Exponential Distribution; 3.4.2 The Normal Distribution; 3.4.3 Mixtures of Normal Distributions; 3.5. Properties of Independent Observations; 3.6. Testing a Hypothesis; 3.6.1 Analyzing the Experiment; 3.6.2 Two Types of Errors; 3.7. Estimating Effect Size; 3.7.1 Confidence Interval for Difference in Means 327 $a3.7.2 Are Two Variables Correlated?3.7.3 Using Confidence Intervals to Test Hypotheses; 3.8. Summary and Review; 4. Testing Hypotheses; 4.1. One-Sample Problems; 4.1.1 Percentile Bootstrap; 4.1.2 Parametric Bootstrap; 4.1.3 Student's t; 4.2. Comparing Two Samples; 4.2.1 Comparing Two Poisson Distributions; 4.2.2 What Should We Measure?; 4.2.3 Permutation Monte Carlo; 4.2.4 Two-Sample t-Test; 4.3. Which Test Should We Use?; 4.3.1 p Values and Significance Levels; 4.3.2 Test Assumptions; 4.3.3 Robustness; 4.3.4 Power of a Test Procedure; 4.3.5 Testing for Correlation; 4.4. Summary and Review 327 $a5. Designing an Experiment or Survey5.1. The Hawthorne Effect; 5.1.1 Crafting an Experiment; 5.2. Designing an Experiment or Survey; 5.2.1 Objectives; 5.2.2 Sample from the Right Population; 5.2.3 Coping with Variation; 5.2.4 Matched Pairs; 5.2.5 The Experimental Unit; 5.2.6 Formulate Your Hypotheses; 5.2.7 What Are You Going to Measure?; 5.2.8 Random Representative Samples; 5.2.9 Treatment Allocation; 5.2.10 Choosing a Random Sample; 5.2.11 Ensuring that Your Observations are Independent; 5.3. How Large a Sample?; 5.3.1 Samples of Fixed Size; Known Distribution; Almost Normal Data 327 $a Bootstrap 330 $aLearn statistical methods quickly and easily with the discovery methodWith its emphasis on the discovery method, this publication encourages readers to discover solutions on their own rather than simply copy answers or apply a formula by rote. Readers quickly master and learn to apply statistical methods, such as bootstrap, decision trees, t-test, and permutations to better characterize, report, test, and classify their research findings. In addition to traditional methods, specialized methods are covered, allowing readers to select and apply the most effective method for their researc 606 $aResampling (Statistics) 615 0$aResampling (Statistics) 676 $a519.52 676 $a519.54 700 $aGood$b Phillip I$0102489 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910831196003321 996 $aIntroduction to statistics through resampling methods and Microsoft Office Excel$93934591 997 $aUNINA