Cargando…
Introduction to statistics through resampling methods and R /
"Intended for class use or self-study, the second addition of this text aspires like the first to introduce statistical methodology to a wide audience, simply and intuitively, through resampling from the data at hand. The methodology proceeds from chapter to chapter from the simple to the compl...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
John Wiley & Sons, Inc.,
[2013]
|
| Edición: | Second edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
| LEADER | 00000cam a2200000 i 4500 | ||
|---|---|---|---|
| 001 | OR_ocn814389959 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o d | ||
| 007 | cr ||||||||||| | ||
| 008 | 121023s2013 nju ob 001 0 eng | ||
| 010 | |a 2012043300 | ||
| 040 | |a DLC |b eng |e rda |e pn |c DLC |d N$T |d E7B |d TEFOD |d DG1 |d NOC |d OCLCF |d UKDOC |d CDX |d LLB |d AZU |d COO |d IDEBK |d STF |d HEBIS |d UMI |d DEBBG |d DEBSZ |d YDXCP |d VT2 |d TEFOD |d OCLCQ |d AZK |d MOR |d CCO |d LIP |d PIFAG |d OHS |d OCLCQ |d MERER |d OCLCQ |d U3W |d OCLCO |d BUF |d COCUF |d OCLCQ |d UUM |d WRM |d CEF |d NRAMU |d INT |d OCLCA |d AU@ |d OCLCO |d OCLCQ |d OTZ |d WYU |d OCLCQ |d UAB |d OCLCA |d OL$ |d OCLCO |d OCLCQ |d CNCEN |d ERF |d OCLCQ |d OCLCA |d OCLCQ |d UKCRE |d UKAHL |d OCLCO |d LUU |d OCL |d OCLCQ |d OCLCO | ||
| 019 | |a 836200889 |a 837890964 |a 868160719 |a 960738914 |a 960738920 |a 960738966 |a 961632809 |a 962650776 |a 988452524 |a 991920922 |a 1037707091 |a 1038638855 |a 1045493616 |a 1105806099 |a 1148077061 |a 1153478358 | ||
| 020 | |a 9781118497579 |q (epub) | ||
| 020 | |a 1118497570 |q (epub) | ||
| 020 | |a 9781118497562 |q (pdf) | ||
| 020 | |a 1118497562 |q (pdf) | ||
| 020 | |a 9781118497678 |q (mobi) | ||
| 020 | |a 1118497678 |q (mobi) | ||
| 020 | |a 9781118497593 |q (electronic bk.) | ||
| 020 | |a 1118497597 |q (electronic bk.) | ||
| 020 | |a 1118428218 | ||
| 020 | |a 9781118428214 | ||
| 020 | |a 9781283950015 |q (MyiLibrary) | ||
| 020 | |a 1283950014 |q (MyiLibrary) | ||
| 020 | |z 9781118428214 |q (pbk.) | ||
| 024 | 8 | |a 9781118497579 | |
| 029 | 1 | |a AU@ |b 000051629256 | |
| 029 | 1 | |a AU@ |b 000051829437 | |
| 029 | 1 | |a AU@ |b 000052918229 | |
| 029 | 1 | |a AU@ |b 000061338821 | |
| 029 | 1 | |a CHBIS |b 010026751 | |
| 029 | 1 | |a CHNEW |b 000603490 | |
| 029 | 1 | |a CHNEW |b 000940763 | |
| 029 | 1 | |a CHVBK |b 480210667 | |
| 029 | 1 | |a DEBBG |b BV041069488 | |
| 029 | 1 | |a DEBBG |b BV041778125 | |
| 029 | 1 | |a DEBSZ |b 404326064 | |
| 029 | 1 | |a DEBSZ |b 428124704 | |
| 029 | 1 | |a NZ1 |b 14970168 | |
| 029 | 1 | |a NZ1 |b 14976970 | |
| 029 | 1 | |a AU@ |b 000066261022 | |
| 035 | |a (OCoLC)814389959 |z (OCoLC)836200889 |z (OCoLC)837890964 |z (OCoLC)868160719 |z (OCoLC)960738914 |z (OCoLC)960738920 |z (OCoLC)960738966 |z (OCoLC)961632809 |z (OCoLC)962650776 |z (OCoLC)988452524 |z (OCoLC)991920922 |z (OCoLC)1037707091 |z (OCoLC)1038638855 |z (OCoLC)1045493616 |z (OCoLC)1105806099 |z (OCoLC)1148077061 |z (OCoLC)1153478358 | ||
| 037 | |a 426251 |b MIL | ||
| 037 | |a 12DF19B4-6468-46C5-AB77-D3B80B9BFF13 |b OverDrive, Inc. |n http://www.overdrive.com | ||
| 042 | |a pcc | ||
| 050 | 0 | 0 | |a QA278.8 |
| 072 | 7 | |a MAT |x 029000 |2 bisacsh | |
| 082 | 0 | 0 | |a 519.5/4 |2 23 |
| 084 | |a MAT029000 |2 bisacsh | ||
| 049 | |a UAMI | ||
| 100 | 1 | |a Good, Phillip I. | |
| 240 | 1 | 0 | |a Introduction to statistics through resampling methods and R/S-PLUS |
| 245 | 1 | 0 | |a Introduction to statistics through resampling methods and R / |c Phillip I. Good. |
| 250 | |a Second edition. | ||
| 264 | 1 | |a Hoboken, New Jersey : |b John Wiley & Sons, Inc., |c [2013] | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a data file |2 rda | ||
| 520 | |a "Intended for class use or self-study, the second addition of this text aspires like the first to introduce statistical methodology to a wide audience, simply and intuitively, through resampling from the data at hand. The methodology proceeds from chapter to chapter from the simple to the complex"-- |c Provided by publisher. | ||
| 500 | |a Includes indexes. | ||
| 588 | 0 | |a Print version record and CIP data provided by publisher. | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Cover -- Title page -- Copyright page -- Contents -- Preface -- Chapter 1: Variation -- 1.1 Variation -- 1.2 Collecting Data -- 1.2.1 A Worked-Through Example -- 1.3 Summarizing Your Data -- 1.3.1 Learning to Use R -- 1.4 Reporting Your Results -- 1.4.1 Picturing Data -- 1.4.2 Better Graphics -- 1.5 Types of Data -- 1.5.1 Depicting Categorical Data -- 1.6 Displaying Multiple Variables -- 1.6.1 Entering Multiple Variables -- 1.6.2 From Observations to Questions -- 1.7 Measures of Location -- 1.7.1 Which Measure of Location? -- *1.7.2 The Geometric Mean -- 1.7.3 Estimating Precision -- 1.7.4 Estimating with the Bootstrap -- 1.8 Samples and Populations -- 1.8.1 Drawing a Random Sample -- *1.8.2 Using Data That Are Already in Spreadsheet Form -- 1.8.3 Ensuring the Sample Is Representative -- 1.9 Summary and Review -- Chapter 2: Probability -- 2.1 Probability -- 2.1.1 Events and Outcomes -- 2.1.2 Venn Diagrams -- 2.2 Binomial Trials -- 2.2.1 Permutations and Rearrangements -- *2.2.2 Programming Your Own Functions in R -- 2.2.3 Back to the Binomial -- 2.2.4 The Problem Jury -- *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 -- Chapter 3: Two Naturally Occurring Probability Distributions -- 3.1 Distribution of Values -- 3.1.1 Cumulative Distribution Function -- 3.1.2 Empirical Distribution Function -- 3.2 Discrete Distributions -- 3.3 The Binomial Distribution -- *3.3.1 Expected Number of Successes in n Binomial Trials -- 3.3.2 Properties of the Binomial -- 3.4 Measuring Population Dispersion and Sample Precision -- 3.5 Poisson: Events Rare in Time and Space -- 3.5.1 Applying the Poisson -- 3.5.2 Comparing Empirical and Theoretical Poisson Distributions -- 3.5.3 Comparing Two Poisson Processes -- 3.6 Continuous Distributions. | |
| 505 | 8 | |a 3.6.1 The Exponential Distribution -- 3.7 Summary and Review -- Chapter 4: Estimation and the Normal Distribution -- 4.1 Point Estimates -- 4.2 Properties of the Normal Distribution -- 4.2.1 Student's t-Distribution -- 4.2.2 Mixtures of Normal Distributions -- 4.3 Using Confidence Intervals to Test Hypotheses -- 4.3.1 Should We Have Used the Bootstrap? -- 4.3.2 The Bias-Corrected and Accelerated Nonparametric Bootstrap -- 4.3.3 The Parametric Bootstrap -- 4.4 Properties of Independent Observations -- 4.5 Summary and Review -- Chapter 5: Testing Hypotheses -- 5.1 Testing a Hypothesis -- 5.1.1 Analyzing the Experiment -- 5.1.2 Two Types of Errors -- 5.2 Estimating Effect Size -- 5.2.1 Effect Size and Correlation -- 5.2.2 Using Confidence Intervals to Test Hypotheses -- 5.3 Applying the t-Test to Measurements -- 5.3.1 Two-Sample Comparison -- 5.3.2 Paired t-Test -- 5.4 Comparing Two Samples -- 5.4.1 What Should We Measure? -- 5.4.2 Permutation Monte Carlo -- 5.4.3 One- vs. Two-Sided Tests -- 5.4.4 Bias-Corrected Nonparametric Bootstrap -- 5.5 Which Test Should We Use? -- 5.5.1 p-Values and Significance Levels -- 5.5.2 Test Assumptions -- 5.5.3 Robustness -- 5.5.4 Power of a Test Procedure -- 5.6 Summary and Review -- Chapter 6: Designing an Experiment or Survey -- 6.1 The Hawthorne Effect -- 6.1.1 Crafting an Experiment -- 6.2 Designing an Experiment or Survey -- 6.2.1 Objectives -- 6.2.2 Sample from the Right Population -- 6.2.3 Coping with Variation -- 6.2.4 Matched Pairs -- 6.2.5 The Experimental Unit -- 6.2.6 Formulate Your Hypotheses -- 6.2.7 What Are You Going to Measure? -- 6.2.8 Random Representative Samples -- 6.2.9 Treatment Allocation -- 6.2.10 Choosing a Random Sample -- 6.2.11 Ensuring Your Observations Are Independent -- 6.3 How Large a Sample? -- 6.3.1 Samples of Fixed Size -- 6.3.2 Sequential Sampling -- 6.4 Meta-Analysis. | |
| 505 | 8 | |a 6.5 Summary and Review -- Chapter 7: Guide to Entering, Editing, Saving, and Retrieving Large Quantities of Data Using R -- 7.1 Creating and Editing a Data File -- 7.2 Storing and Retrieving Files from within R -- 7.3 Retrieving Data Created by Other Programs -- 7.3.1 The Tabular Format -- 7.3.2 Comma-Separated Values -- 7.3.3 Data from Microsoft Excel -- 7.3.4 Data from Minitab, SAS, SPSS, or Stata Data Files -- 7.4 Using R to Draw a Random Sample -- Chapter 8: Analyzing Complex Experiments -- 8.1 Changes Measured in Percentages -- 8.2 Comparing More Than Two Samples -- 8.2.1 Programming the Multi-Sample Comparison in R -- *8.2.2 Reusing Your R Functions -- 8.2.3 What Is the Alternative? -- 8.2.4 Testing for a Dose Response or Other Ordered Alternative -- 8.3 Equalizing Variability -- 8.4 Categorical Data -- 8.4.1 Making Decisions with R -- 8.4.2 One-Sided Fisher's Exact Test -- 8.4.3 The Two-Sided Test -- 8.4.4 Testing for Goodness of Fit -- 8.4.5 Multinomial Tables -- 8.5 Multivariate Analysis -- 8.5.1 Manipulating Multivariate Data in R -- 8.5.2 Hotelling's T2 -- *8.5.3 Pesarin-Fisher Omnibus Statistic -- 8.6 R Programming Guidelines -- 8.7 Summary and Review -- Chapter 9: Developing Models -- 9.1 Models -- 9.1.1 Why Build Models? -- 9.1.2 Caveats -- 9.2 Classification and Regression Trees -- 9.2.1 Example: Consumer Survey -- 9.2.2 How Trees Are Grown -- 9.2.3 Incorporating Existing Knowledge -- 9.2.4 Prior Probabilities -- 9.2.5 Misclassification Costs -- 9.3 Regression -- 9.3.1 Linear Regression -- 9.4 Fitting a Regression Equation -- 9.4.1 Ordinary Least Squares -- 9.4.2 Types of Data -- 9.4.3 Least Absolute Deviation Regression -- 9.4.4 Errors-in-Variables Regression -- 9.4.5 Assumptions -- 9.5 Problems with Regression -- 9.5.1 Goodness of Fit versus Prediction -- 9.5.2 Which Model? -- 9.5.3 Measures of Predictive Success. | |
| 505 | 8 | |a 9.5.4 Multivariable Regression -- 9.6 Quantile Regression -- 9.7 Validation -- 9.7.1 Independent Verification -- 9.7.2 Splitting the Sample -- 9.7.3 Cross-Validation with the Bootstrap -- 9.8 Summary and Review -- Chapter 10: Reporting Your Findings -- 10.1 What to Report -- 10.1.1 Study Objectives -- 10.1.2 Hypotheses -- 10.1.3 Power and Sample Size Calculations -- 10.1.4 Data Collection Methods -- 10.1.5 Clusters -- 10.1.6 Validation Methods -- 10.2 Text, Table, or Graph? -- 10.3 Summarizing Your Results -- 10.3.1 Center of the Distribution -- 10.3.2 Dispersion -- 10.3.3 Categorical Data -- 10.4 Reporting Analysis Results -- 10.4.1 p-Values? Or Confidence Intervals? -- 10.5 Exceptions Are the Real Story -- 10.5.1 Nonresponders -- 10.5.2 The Missing Holes -- 10.5.3 Missing Data -- 10.5.4 Recognize and Report Biases -- 10.6 Summary and Review -- Chapter 11: Problem Solving -- 11.1 The Problems -- 11.2 Solving Practical Problems -- 11.2.1 Provenance of the Data -- 11.2.2 Inspect the Data -- 11.2.3 Validate the Data Collection Methods -- 11.2.4 Formulate Hypotheses -- 11.2.5 Choosing a Statistical Methodology -- 11.2.6 Be Aware of What You Don't Know -- 11.2.7 Qualify Your Conclusions -- Answers to Selected Exercises -- Index. | |
| 542 | |f Copyright © John Wiley & Sons |g 2013 | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Resampling (Statistics) | |
| 650 | 0 | |a Statistics. | |
| 650 | 1 | 2 | |a Statistics as Topic |
| 650 | 6 | |a Rééchantillonnage (Statistique) | |
| 650 | 6 | |a Statistiques. | |
| 650 | 6 | |a Statistique. | |
| 650 | 7 | |a statistics. |2 aat | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a Statistics |2 fast | |
| 650 | 7 | |a Resampling (Statistics) |2 fast | |
| 650 | 7 | |a Statistik |2 gnd | |
| 650 | 7 | |a Resampling |2 gnd | |
| 650 | 7 | |a R |g Programm |2 gnd | |
| 775 | 0 | 8 | |i Revion of: |a Good, Phillip I. |t Introduction to statistics through resampling methods and R/S-PLUS. |d Hoboken, N.J. : Wiley-Interscience, ©2005 |w (DLC) 2004061922 |
| 776 | 0 | 8 | |i Print version: |a Good, Phillip I. |t Introduction to statistics through resampling methods and R. |b Second edition. |d Hoboken, New Jersey : John Wiley & Sons, Inc., [2013] |z 9781118428214 |w (DLC) 2012031774 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781118497579/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a Askews and Holts Library Services |b ASKH |n AH25045996 | ||
| 938 | |a 123Library |b 123L |n 62647 | ||
| 938 | |a Coutts Information Services |b COUT |n 24561238 | ||
| 938 | |a ebrary |b EBRY |n ebr10645199 | ||
| 938 | |a EBSCOhost |b EBSC |n 522954 | ||
| 938 | |a ProQuest MyiLibrary Digital eBook Collection |b IDEB |n cis24561238 | ||
| 938 | |a YBP Library Services |b YANK |n 9571186 | ||
| 938 | |a YBP Library Services |b YANK |n 9961451 | ||
| 938 | |a YBP Library Services |b YANK |n 9976864 | ||
| 994 | |a 92 |b IZTAP | ||