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Analyzing the large numbers of variables in biomedical and satellite imagery [[electronic resource] /] / Phillip I. Good
Analyzing the large numbers of variables in biomedical and satellite imagery [[electronic resource] /] / Phillip I. Good
Autore Good Phillip I
Pubbl/distr/stampa Hoboken, N.J., : Wiley, c2011
Descrizione fisica xii, 185 p. : ill
Disciplina 006.3/12
Soggetto topico Data mining
Mathematical statistics
Biomedical engineering - Data processing
Remote sensing - Data processing
Functions of several complex variables
R (Computer program language)
ISBN 1-283-13877-8
0-470-93725-4
9786613138774
0-470-93727-0
1-118-00214-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto ; Machine generated contents note: ; 1. Very Large Arrays -- ; 1.1. Applications -- ; 1.2. Problems -- ; 1.3. Solutions -- ; 2. Permutation Tests -- ; 2.1. Two-Sample Comparison -- ; 2.1.1. Blocks -- ; 2.2. k-Sample Comparison -- ; 2.3. Computing The p-Value -- ; 2.3.1. Monte Carlo Method -- ; 2.3.2. An R Program -- ; 2.4. Multiple-Variable Comparisons -- ; 2.4.1. Euclidean Distance Matrix Analysis -- ; 2.4.2. Hotelling's T2 -- ; 2.4.3. Mantel's U -- ; 2.4.4. Combining Univariate Tests -- ; 2.4.5. Gene Set Enrichment Analysis -- ; 2.5. Categorical Data -- ; 2.6. Software -- ; 2.7. Summary -- ; 3. Applying the Permutation Test -- ; 3.1. Which Variables Should Be Included? -- ; 3.2. Single-Value Test Statistics -- ; 3.2.1. Categorical Data -- ; 3.2.2. A Multivariate Comparison Based on a Summary Statistic -- ; 3.2.3. A Multivariate Comparison Based on Variants of Hotelling's T2
; 3.2.4. Adjusting for Covariates -- ; 3.2.5. Pre-Post Comparisons -- ; 3.2.6. Choosing a Statistic: Time-Course Microarrays -- ; 3.3. Recommended Approaches -- ; 3.4. To Learn More -- ; 4. Biological Background -- ; 4.1. Medical Imaging -- ; 4.1.1. Ultrasound -- ; 4.1.2. EEG/MEG -- ; 4.1.3. Magnetic Resonance Imaging -- ; 4.1.3.1. MRI -- ; 4.1.3.2. fMRI -- ; 4.1.4. Positron Emission Tomography -- ; 4.2. Microarrays -- ; 4.3. To Learn More -- ; 5. Multiple Tests -- ; 5.1. Reducing the Number of Hypotheses to Be Tested -- ; 5.1.1. Normalization -- ; 5.1.2. Selection Methods -- ; 5.1.2.1. Univariate Statistics -- ; 5.1.2.2. Which Statistic? -- ; 5.1.2.3. Heuristic Methods -- ; 5.1.2.4. Which Method? -- ; 5.2. Controlling the Over All Error Rate -- ; 5.2.1. An Example: Analyzing Data from Microarrays -- ; 5.3. Controlling the False Discovery Rate -- ; 5.3.1. An Example: Analyzing Time-Course Data from Microarrays -- ; 5.4. Gene Set Enrichment Analysis
; 5.5. Software for Performing Multiple Simultaneous Tests -- ; 5.5.1. AFNI -- ; 5.5.2. Cyber-T -- ; 5.5.3. dChip -- ; 5.5.4. ExactFDR -- ; 5.5.5. GESS -- ; 5.5.6. HaploView -- ; 5.5.7. MatLab -- ; 5.5.8. R -- ; 5.5.9. SAM -- ; 5.5.10. ParaSam -- ; 5.6. Summary -- ; 5.7. To Learn More -- ; 6. The Bootstrap -- ; 6.1. Samples and Populations -- ; 6.2. Precision of an Estimate -- ; 6.2.1. R Code -- ; 6.2.2. Applying the Bootstrap -- ; 6.2.3. Bootstrap Reproducibility Index -- ; 6.2.4. Estimation in Regression Models -- ; 6.3. Confidence Intervals -- ; 6.3.1. Testing for Equivalence -- ; 6.3.2. Parametric Bootstrap -- ; 6.3.3. Blocked Bootstrap -- ; 6.3.4. Balanced Bootstrap -- ; 6.3.5. Adjusted Bootstrap -- ; 6.3.6. Which Test? -- ; 6.4. Determining Sample Size -- ; 6.4.1. Establish a Threshold -- ; 6.5. Validation -- ; 6.5.1. Cluster Analysis -- ; 6.5.2. Correspondence Analysis -- ; 6.6. Building a Model -- ; 6.7. How Large Should The Samples Be?
; 6.8. Summary -- ; 6.9. To Learn More -- ; 7. Classification Methods -- ; 7.1. Nearest Neighbor Methods -- ; 7.2. Discriminant Analysis -- ; 7.3. Logistic Regression -- ; 7.4. Principal Components -- ; 7.5. Naive Bayes Classifier -- ; 7.6. Heuristic Methods -- ; 7.7. Decision Trees -- ; 7.7.1. A Worked-Through Example -- ; 7.8. Which Algorithm Is Best for Your Application? -- ; 7.8.1. Some Further Comparisons -- ; 7.8.2. Validation Versus Cross-validation -- ; 7.9. Improving Diagnostic Effectiveness -- ; 7.9.1. Boosting -- ; 7.9.2. Ensemble Methods -- ; 7.9.3. Random Forests -- ; 7.10. Software for Decision Trees -- ; 7.11. Summary -- ; 8. Applying Decision Trees -- ; 8.1. Photographs -- ; 8.2. Ultrasound -- ; 8.3. MRI Images -- ; 8.4. EEGs and EMGs -- ; 8.5. Misclassification Costs -- ; 8.6. Receiver Operating Characteristic -- ; 8.7. When the Categories Are As Yet Undefined -- ; 8.7.1. Unsupervised Principal Components Applied to fMRI
; 8.7.2. Supervised Principal Components Applied to Microarrays -- ; 8.8. Ensemble Methods -- ; 8.9. Maximally Diversified Multiple Trees -- ; 8.10. Putting It All Together -- ; 8.11. Summary -- ; 8.12. To Learn More -- Glossary of Biomedical Terminology -- Glossary of Statistical Terminology -- Appendix: An R Primer -- ; R1. Getting Started -- ; R1.1. R Functions -- ; R1.2. Vector Arithmetic -- ; R2. Store and Retrieve Data -- ; R2.1. Storing and Retrieving Files from Within R -- ; R2.2. The Tabular Format -- ; R2.3. Comma Separated Format -- ; R3. Resampling -- ; R3.1. The While Command -- ; R4. Expanding R's Capabilities -- ; R4.1. Downloading Libraries of R Functions -- ; R4.2. Programming Your Own Functions.
Record Nr. UNINA-9910208830603321
Good Phillip I  
Hoboken, N.J., : Wiley, c2011
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Analyzing the large numbers of variables in biomedical and satellite imagery [[electronic resource] /] / Phillip I. Good
Analyzing the large numbers of variables in biomedical and satellite imagery [[electronic resource] /] / Phillip I. Good
Autore Good Phillip I
Pubbl/distr/stampa Hoboken, N.J., : Wiley, c2011
Descrizione fisica xii, 185 p. : ill
Disciplina 006.3/12
Soggetto topico Data mining
Mathematical statistics
Biomedical engineering - Data processing
Remote sensing - Data processing
Functions of several complex variables
R (Computer program language)
ISBN 1-283-13877-8
0-470-93725-4
9786613138774
0-470-93727-0
1-118-00214-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto ; Machine generated contents note: ; 1. Very Large Arrays -- ; 1.1. Applications -- ; 1.2. Problems -- ; 1.3. Solutions -- ; 2. Permutation Tests -- ; 2.1. Two-Sample Comparison -- ; 2.1.1. Blocks -- ; 2.2. k-Sample Comparison -- ; 2.3. Computing The p-Value -- ; 2.3.1. Monte Carlo Method -- ; 2.3.2. An R Program -- ; 2.4. Multiple-Variable Comparisons -- ; 2.4.1. Euclidean Distance Matrix Analysis -- ; 2.4.2. Hotelling's T2 -- ; 2.4.3. Mantel's U -- ; 2.4.4. Combining Univariate Tests -- ; 2.4.5. Gene Set Enrichment Analysis -- ; 2.5. Categorical Data -- ; 2.6. Software -- ; 2.7. Summary -- ; 3. Applying the Permutation Test -- ; 3.1. Which Variables Should Be Included? -- ; 3.2. Single-Value Test Statistics -- ; 3.2.1. Categorical Data -- ; 3.2.2. A Multivariate Comparison Based on a Summary Statistic -- ; 3.2.3. A Multivariate Comparison Based on Variants of Hotelling's T2
; 3.2.4. Adjusting for Covariates -- ; 3.2.5. Pre-Post Comparisons -- ; 3.2.6. Choosing a Statistic: Time-Course Microarrays -- ; 3.3. Recommended Approaches -- ; 3.4. To Learn More -- ; 4. Biological Background -- ; 4.1. Medical Imaging -- ; 4.1.1. Ultrasound -- ; 4.1.2. EEG/MEG -- ; 4.1.3. Magnetic Resonance Imaging -- ; 4.1.3.1. MRI -- ; 4.1.3.2. fMRI -- ; 4.1.4. Positron Emission Tomography -- ; 4.2. Microarrays -- ; 4.3. To Learn More -- ; 5. Multiple Tests -- ; 5.1. Reducing the Number of Hypotheses to Be Tested -- ; 5.1.1. Normalization -- ; 5.1.2. Selection Methods -- ; 5.1.2.1. Univariate Statistics -- ; 5.1.2.2. Which Statistic? -- ; 5.1.2.3. Heuristic Methods -- ; 5.1.2.4. Which Method? -- ; 5.2. Controlling the Over All Error Rate -- ; 5.2.1. An Example: Analyzing Data from Microarrays -- ; 5.3. Controlling the False Discovery Rate -- ; 5.3.1. An Example: Analyzing Time-Course Data from Microarrays -- ; 5.4. Gene Set Enrichment Analysis
; 5.5. Software for Performing Multiple Simultaneous Tests -- ; 5.5.1. AFNI -- ; 5.5.2. Cyber-T -- ; 5.5.3. dChip -- ; 5.5.4. ExactFDR -- ; 5.5.5. GESS -- ; 5.5.6. HaploView -- ; 5.5.7. MatLab -- ; 5.5.8. R -- ; 5.5.9. SAM -- ; 5.5.10. ParaSam -- ; 5.6. Summary -- ; 5.7. To Learn More -- ; 6. The Bootstrap -- ; 6.1. Samples and Populations -- ; 6.2. Precision of an Estimate -- ; 6.2.1. R Code -- ; 6.2.2. Applying the Bootstrap -- ; 6.2.3. Bootstrap Reproducibility Index -- ; 6.2.4. Estimation in Regression Models -- ; 6.3. Confidence Intervals -- ; 6.3.1. Testing for Equivalence -- ; 6.3.2. Parametric Bootstrap -- ; 6.3.3. Blocked Bootstrap -- ; 6.3.4. Balanced Bootstrap -- ; 6.3.5. Adjusted Bootstrap -- ; 6.3.6. Which Test? -- ; 6.4. Determining Sample Size -- ; 6.4.1. Establish a Threshold -- ; 6.5. Validation -- ; 6.5.1. Cluster Analysis -- ; 6.5.2. Correspondence Analysis -- ; 6.6. Building a Model -- ; 6.7. How Large Should The Samples Be?
; 6.8. Summary -- ; 6.9. To Learn More -- ; 7. Classification Methods -- ; 7.1. Nearest Neighbor Methods -- ; 7.2. Discriminant Analysis -- ; 7.3. Logistic Regression -- ; 7.4. Principal Components -- ; 7.5. Naive Bayes Classifier -- ; 7.6. Heuristic Methods -- ; 7.7. Decision Trees -- ; 7.7.1. A Worked-Through Example -- ; 7.8. Which Algorithm Is Best for Your Application? -- ; 7.8.1. Some Further Comparisons -- ; 7.8.2. Validation Versus Cross-validation -- ; 7.9. Improving Diagnostic Effectiveness -- ; 7.9.1. Boosting -- ; 7.9.2. Ensemble Methods -- ; 7.9.3. Random Forests -- ; 7.10. Software for Decision Trees -- ; 7.11. Summary -- ; 8. Applying Decision Trees -- ; 8.1. Photographs -- ; 8.2. Ultrasound -- ; 8.3. MRI Images -- ; 8.4. EEGs and EMGs -- ; 8.5. Misclassification Costs -- ; 8.6. Receiver Operating Characteristic -- ; 8.7. When the Categories Are As Yet Undefined -- ; 8.7.1. Unsupervised Principal Components Applied to fMRI
; 8.7.2. Supervised Principal Components Applied to Microarrays -- ; 8.8. Ensemble Methods -- ; 8.9. Maximally Diversified Multiple Trees -- ; 8.10. Putting It All Together -- ; 8.11. Summary -- ; 8.12. To Learn More -- Glossary of Biomedical Terminology -- Glossary of Statistical Terminology -- Appendix: An R Primer -- ; R1. Getting Started -- ; R1.1. R Functions -- ; R1.2. Vector Arithmetic -- ; R2. Store and Retrieve Data -- ; R2.1. Storing and Retrieving Files from Within R -- ; R2.2. The Tabular Format -- ; R2.3. Comma Separated Format -- ; R3. Resampling -- ; R3.1. The While Command -- ; R4. Expanding R's Capabilities -- ; R4.1. Downloading Libraries of R Functions -- ; R4.2. Programming Your Own Functions.
Record Nr. UNINA-9910825098303321
Good Phillip I  
Hoboken, N.J., : Wiley, c2011
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Common errors in statistics (and how to avoid them) [[electronic resource] /] / Phillip I. Good, James W. Hardin
Common errors in statistics (and how to avoid them) [[electronic resource] /] / Phillip I. Good, James W. Hardin
Autore Good Phillip I
Edizione [4th ed.]
Pubbl/distr/stampa Hoboken, N.J., : Wiley, 2012
Descrizione fisica 1 online resource (336 pages)
Disciplina 519.5
Altri autori (Persone) HardinJames W (James William)
Soggetto topico Statistics
ISBN 1-280-69939-6
9786613676375
1-118-36011-7
1-118-36012-5
1-118-36013-3
Classificazione MAT029000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto pt. 1. Foundations -- pt. 2. Statistical analysis -- pt. 3. Building a model.
Record Nr. UNINA-9910141418403321
Good Phillip I  
Hoboken, N.J., : Wiley, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Introduction to statistics through resampling methods and Microsoft Office Excel [[electronic resource] /] / Phillip I. Good
Introduction to statistics through resampling methods and Microsoft Office Excel [[electronic resource] /] / Phillip I. Good
Autore Good Phillip I
Pubbl/distr/stampa Hoboken, N.J., : Wiley-Interscience, c2005
Descrizione fisica 1 online resource (245 p.)
Disciplina 519.52
519.54
Soggetto topico Resampling (Statistics)
Soggetto genere / forma Electronic books.
ISBN 1-280-27720-3
9786610277209
0-470-32514-3
0-471-74177-9
0-471-74176-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto INTRODUCTION TO STATISTICS THROUGH RESAMPLING METHODS AND MICROSOFT OFFICE EXCEL®; 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
1.7.1 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
3.1.1 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
3.7.2 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
5. 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
Bootstrap
Record Nr. UNINA-9910143402803321
Good Phillip I  
Hoboken, N.J., : Wiley-Interscience, c2005
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Introduction to statistics through resampling methods and Microsoft Office Excel [[electronic resource] /] / Phillip I. Good
Introduction to statistics through resampling methods and Microsoft Office Excel [[electronic resource] /] / Phillip I. Good
Autore Good Phillip I
Pubbl/distr/stampa Hoboken, N.J., : Wiley-Interscience, c2005
Descrizione fisica 1 online resource (245 p.)
Disciplina 519.52
519.54
Soggetto topico Resampling (Statistics)
ISBN 1-280-27720-3
9786610277209
0-470-32514-3
0-471-74177-9
0-471-74176-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto INTRODUCTION TO STATISTICS THROUGH RESAMPLING METHODS AND MICROSOFT OFFICE EXCEL®; 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
1.7.1 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
3.1.1 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
3.7.2 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
5. 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
Bootstrap
Record Nr. UNINA-9910831196003321
Good Phillip I  
Hoboken, N.J., : Wiley-Interscience, c2005
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Introduction to statistics through resampling methods and Microsoft Office Excel [[electronic resource] /] / Phillip I. Good
Introduction to statistics through resampling methods and Microsoft Office Excel [[electronic resource] /] / Phillip I. Good
Autore Good Phillip I
Pubbl/distr/stampa Hoboken, N.J., : Wiley-Interscience, c2005
Descrizione fisica 1 online resource (245 p.)
Disciplina 519.52
519.54
Soggetto topico Resampling (Statistics)
ISBN 1-280-27720-3
9786610277209
0-470-32514-3
0-471-74177-9
0-471-74176-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto INTRODUCTION TO STATISTICS THROUGH RESAMPLING METHODS AND MICROSOFT OFFICE EXCEL®; 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
1.7.1 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
3.1.1 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
3.7.2 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
5. 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
Bootstrap
Record Nr. UNINA-9910841452603321
Good Phillip I  
Hoboken, N.J., : Wiley-Interscience, c2005
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Introduction to statistics through resampling methods and R [[electronic resource] /] / Phillip I. Good
Introduction to statistics through resampling methods and R [[electronic resource] /] / Phillip I. Good
Autore Good Phillip I
Edizione [2nd ed.]
Pubbl/distr/stampa Hoboken, N.J., : John Wiley & Sons, Inc., 2013
Descrizione fisica 1 online resource (224 p.)
Disciplina 519.5/4
Soggetto topico Resampling (Statistics)
R (Computer program language)
ISBN 1-118-49759-7
1-118-49756-2
1-283-95001-4
1-118-49757-0
Classificazione MAT029000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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 Populations1.8.1 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 Distributions3.1 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
3.6.1 The Exponential Distribution3.7 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 Errors5.2 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
Record Nr. UNINA-9910141528803321
Good Phillip I  
Hoboken, N.J., : John Wiley & Sons, Inc., 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Introduction to statistics through resampling methods and R [[electronic resource] /] / Phillip I. Good
Introduction to statistics through resampling methods and R [[electronic resource] /] / Phillip I. Good
Autore Good Phillip I
Edizione [2nd ed.]
Pubbl/distr/stampa Hoboken, N.J., : John Wiley & Sons, Inc., 2013
Descrizione fisica 1 online resource (224 p.)
Disciplina 519.5/4
Soggetto topico Resampling (Statistics)
R (Computer program language)
ISBN 1-118-49759-7
1-118-49756-2
1-283-95001-4
1-118-49757-0
Classificazione MAT029000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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 Populations1.8.1 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 Distributions3.1 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
3.6.1 The Exponential Distribution3.7 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 Errors5.2 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
Record Nr. UNINA-9910809729403321
Good Phillip I  
Hoboken, N.J., : John Wiley & Sons, Inc., 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
A manager's guide to the design and conduct of clinical trials [[electronic resource] /] / Phillip I. Good
A manager's guide to the design and conduct of clinical trials [[electronic resource] /] / Phillip I. Good
Autore Good Phillip I
Edizione [2nd ed.]
Pubbl/distr/stampa Hoboken, NJ, : Wiley-Liss, c2006
Descrizione fisica 1 online resource (274 p.)
Disciplina 610.72/4
610.724
Soggetto topico Clinical trials
Clinical medicine - Research
Soggetto genere / forma Electronic books.
ISBN 1-280-44821-0
9786610448210
0-470-32719-7
0-471-93091-1
0-471-93087-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto A MANAGER'S GUIDE TO THE DESIGN AND CONDUCT OF CLINICAL TRIALS; Contents; 1 Cut Costs and Increase Profits; No Excuse for the Wastage; Front-Loaded Solution; Downsizing; Think Transnational; A Final Word; 2 Guidelines; Start with Your Reports; The Wrong Way; Keep It in the Computer; Don't Push the River; KISS; Plug the Holes as They Arise; Pay for Results, Not Intentions; Plan, Do, Then Check; PART I PLAN; 3 Prescription for Success; Plan; A. Predesign Phase; B. Design the Trials; Do; C. Obtain Regulatory Agency Approval for the Trials; D. Form the Implementation Team
E. Line Up Your Panel of PhysiciansF. Develop the Data Entry Software; G. Test the Software; H. Train; I. Recruit Patients; J. Set Up External Review Committees; K. Conduct the Trials; L. Develop Suite of Programs for Use in Data Analysis; M. Analyze and Interpret the Data; Check; N. Complete the Submission; 4 Staffing for Success; The People You Need; Design Team; Obtain Regulatory Approval for the Trials; Track Progress; Implementation Team; Develop Data Entry Software; Test the Software; Line Up Your Panel of Physicians; External Laboratories; Site Coordinators; External Review Committees
Recruit and Enroll PatientsTransnational Trials; Conduct the Trials; Programs for Data Analysis; Analyze and Interpret the Data; The People You Don't Need; For Further Information; 5 Design Decisions; Should the Study Be Performed?; Should the Trials Be Transnational?; Study Objectives; End Points; Secondary End Points; Should We Proceed with a Full-Scale Trial?; Tertiary End Points; Baseline Data; Who Will Collect the Data?; Quality Control; Study Population; Timing; Closure; Planned Closure; Unplanned Closure; Be Defensive. Review, Rewrite, Review Again; Checklist for Design
Budgets and ExpendituresFor Further Information; 6 Trial Design; Baseline Measurements; Controlled Randomized Clinical Trials; Randomized Trials; Blocked Randomization; Stratified Randomization; Single- vs. Double-Blind Studies; Allocation Concealment; Exceptions to the Rule; Sample Size; Which Formula?; Precision of Estimates; Bounding Type I and Type II Errors; Equivalence; Software; Subsamples; Loss Adjustment; Number of Treatment Sites; Alternate Designs; Taking Cost into Consideration; For Further Information; 7 Exception Handling; Patient Related; Missed Doses; Missed Appointments
NoncomplianceAdverse Reactions; Reporting Adverse Events; When Do You Crack the Code?; Investigator Related; Lagging Recruitment; Protocol Deviations; Site-Specific Problems; Closure; Intent to Treat; Is Your Planning Complete?; PART II DO; 8 Documentation; Guidelines; Common Technical Document; Reporting Adverse Events; Initial Submission to the Regulatory Agency; Sponsor Data; Justifying the Study; Objectives; Patient Selection; Treatment Plan; Outcome Measures and Evaluation; Procedures; Clinical Follow-Up; Adverse Events; Data Management, Monitoring, Quality Control; Statistical Analysis
Investigator Responsibilities
Record Nr. UNINA-9910143422903321
Good Phillip I  
Hoboken, NJ, : Wiley-Liss, c2006
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
A manager's guide to the design and conduct of clinical trials [[electronic resource] /] / Phillip I. Good
A manager's guide to the design and conduct of clinical trials [[electronic resource] /] / Phillip I. Good
Autore Good Phillip I
Edizione [2nd ed.]
Pubbl/distr/stampa Hoboken, NJ, : Wiley-Liss, c2006
Descrizione fisica 1 online resource (274 p.)
Disciplina 610.72/4
610.724
Soggetto topico Clinical trials
Clinical medicine - Research
ISBN 1-280-44821-0
9786610448210
0-470-32719-7
0-471-93091-1
0-471-93087-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto A MANAGER'S GUIDE TO THE DESIGN AND CONDUCT OF CLINICAL TRIALS; Contents; 1 Cut Costs and Increase Profits; No Excuse for the Wastage; Front-Loaded Solution; Downsizing; Think Transnational; A Final Word; 2 Guidelines; Start with Your Reports; The Wrong Way; Keep It in the Computer; Don't Push the River; KISS; Plug the Holes as They Arise; Pay for Results, Not Intentions; Plan, Do, Then Check; PART I PLAN; 3 Prescription for Success; Plan; A. Predesign Phase; B. Design the Trials; Do; C. Obtain Regulatory Agency Approval for the Trials; D. Form the Implementation Team
E. Line Up Your Panel of PhysiciansF. Develop the Data Entry Software; G. Test the Software; H. Train; I. Recruit Patients; J. Set Up External Review Committees; K. Conduct the Trials; L. Develop Suite of Programs for Use in Data Analysis; M. Analyze and Interpret the Data; Check; N. Complete the Submission; 4 Staffing for Success; The People You Need; Design Team; Obtain Regulatory Approval for the Trials; Track Progress; Implementation Team; Develop Data Entry Software; Test the Software; Line Up Your Panel of Physicians; External Laboratories; Site Coordinators; External Review Committees
Recruit and Enroll PatientsTransnational Trials; Conduct the Trials; Programs for Data Analysis; Analyze and Interpret the Data; The People You Don't Need; For Further Information; 5 Design Decisions; Should the Study Be Performed?; Should the Trials Be Transnational?; Study Objectives; End Points; Secondary End Points; Should We Proceed with a Full-Scale Trial?; Tertiary End Points; Baseline Data; Who Will Collect the Data?; Quality Control; Study Population; Timing; Closure; Planned Closure; Unplanned Closure; Be Defensive. Review, Rewrite, Review Again; Checklist for Design
Budgets and ExpendituresFor Further Information; 6 Trial Design; Baseline Measurements; Controlled Randomized Clinical Trials; Randomized Trials; Blocked Randomization; Stratified Randomization; Single- vs. Double-Blind Studies; Allocation Concealment; Exceptions to the Rule; Sample Size; Which Formula?; Precision of Estimates; Bounding Type I and Type II Errors; Equivalence; Software; Subsamples; Loss Adjustment; Number of Treatment Sites; Alternate Designs; Taking Cost into Consideration; For Further Information; 7 Exception Handling; Patient Related; Missed Doses; Missed Appointments
NoncomplianceAdverse Reactions; Reporting Adverse Events; When Do You Crack the Code?; Investigator Related; Lagging Recruitment; Protocol Deviations; Site-Specific Problems; Closure; Intent to Treat; Is Your Planning Complete?; PART II DO; 8 Documentation; Guidelines; Common Technical Document; Reporting Adverse Events; Initial Submission to the Regulatory Agency; Sponsor Data; Justifying the Study; Objectives; Patient Selection; Treatment Plan; Outcome Measures and Evaluation; Procedures; Clinical Follow-Up; Adverse Events; Data Management, Monitoring, Quality Control; Statistical Analysis
Investigator Responsibilities
Record Nr. UNINA-9910830835203321
Good Phillip I  
Hoboken, NJ, : Wiley-Liss, c2006
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui