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    1. Naturvetenskap och teknik
    2. Matematik och naturvetenskap
    3. Matematik
    4. Beräkning och matematisk analys

    Evidence-Based Statistics

    An Introduction to the Evidential Approach - from Likelihood Principle to Statistical Practice

    AvPeter M. B. Cahusac

    Inbunden, Engelska, 2020

    1 197 kr

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    E-bok

    1 372 kr

    E-bok

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    Beskrivning

    Evidence-Based Statistics: An Introduction to the Evidential Approach – from Likelihood Principle to Statistical Practice provides readers with a comprehensive and thorough guide to the evidential approach in statistics. The approach uses likelihood ratios, rather than the probabilities used by other statistical inference approaches. The evidential approach is conceptually easier to grasp, and the calculations more straightforward to perform. This book explains how to express data in terms of the strength of statistical evidence for competing hypotheses.The evidential approach is currently underused, despite its mathematical precision and statistical validity. Evidence-Based Statistics is an accessible and practical text filled with examples, illustrations and exercises. Additionally, the companion website complements and expands on the information contained in the book.While the evidential approach is unlikely to replace probability-based methods of statistical inference, it provides a useful addition to any statistician’s "bag of tricks." In this book: It explains how to calculate statistical evidence for commonly used analyses, in a step-by-step fashionAnalyses include: t tests, ANOVA (one-way, factorial, between- and within-participants, mixed), categorical analyses (binomial, Poisson, McNemar, rate ratio, odds ratio, data that's 'too good to be true', multi-way tables), correlation, regression and nonparametric analyses (one sample, related samples, independent samples, multiple independent samples, permutation and bootstraps)Equations are given for all analyses, and R statistical code provided for many of the analysesSample size calculations for evidential probabilities of misleading and weak evidence are explainedUseful techniques, like Matthews's critical prior interval, Goodman's Bayes factor, and Armitage's stopping rule are describedRecommended for undergraduate and graduate students in any field that relies heavily on statistical analysis, as well as active researchers and professionals in those fields, Evidence-Based Statistics: An Introduction to the Evidential Approach – from Likelihood Principle to Statistical Practice belongs on the bookshelf of anyone who wants to amplify and empower their approach to statistical analysis.

    Produktinformation

    • Utgivningsdatum:2020-10-06
    • Mått:10 x 10 x 10 mm
    • Vikt:454 g
    • Format:Inbunden
    • Språk:Engelska
    • Antal sidor:256
    • Förlag:John Wiley & Sons Inc
    • ISBN:9781119549802

    Utforska kategorier

    • Beräkning och matematisk analys inom Naturvetenskap och teknik

    Mer om författaren

    PETER M.B. CAHUSAC, PHD, received his doctorate in neuropharmacology from the Medical School Bristol University in 1984. He completed post-doctoral studies at Oxford University where he obtained an MSc in Applied Statistics in 1992. He is a member of the British Pharmacological Society, and Fellow of the Physiological (UK) and the Royal Statistical Societies. He is currently Associate Professor in Biostatistics and Pharmacology at Alfaisal University in Riyadh, Saudi Arabia.

    Recensioner i media

    “The likelihood approach is a distinct one that spans between the Bayesian and frequentist, but there has not been a good textbook treatment that could be used, say, by Psychology final year Bachelors’ students or established researchers. Until now, that is – Cahusac’s text is very clear, very readable, and shows exactly how to apply the likelihood approach to ANOVA and regression, and to categorical and rank data, using R.” - Zoltan Dienes, The Journal of the Royal Statistical Society, Series A (Statistics in Society) 185:1 (2022)“This book is so amazing and easy to comprehend, you will simply love it. I am sure that this book is going to be among the top-rated books in the field of biostatistics.”- Professor Dileep K. Rohra, MD, PhD (Chair of Department of Pharmacology, College of Medicine, Alfaisal University) (2021)“This superbly written book explains complex biostatistical concepts in a simpler format, making it much easier to comprehend and apply in diverse specialized areas.”- Dr. Fazal Hussain, MD, MPH (2021)

    Innehållsförteckning

    • Acknowledgements xiAbout the Author xiiiAbout the Companion Site xvIntroduction 1References 21 The Evidence is the Evidence 31.1 Evidence-Based Statistics 31.1.1 The Literature 41.2 Statistical Inference – The Basics 61.2.1 Different Statistical Approaches 71.2.2 The Likelihood/Evidential Approach 81.2.3 Types of Approach Using Likelihoods 111.2.4 Pros and Cons of Likelihood Approach 111.3 Effect Size – True If Huge! 121.4 Calculations 151.5 Summary of the Evidential Approach 16References 182 The Evidential Approach 212.1 Likelihood 212.1.1 The Principle 222.1.2 Support 242.1.3 Example – One Sample 292.1.4 Direction Matters 362.1.5 Maximum Likelihood Ratio 372.1.6 Likelihood Intervals 392.1.7 The Support Function 422.1.8 Choosing the Effect Size 422.2 Misleading and Weak Evidence 462.3 Adding More Data and Multiple Testing 482.4 Sequence of Calculations Using t 492.5 Likelihood Terminology 512.6 R Code for Chapter 2 522.6.1 Calculating the Likelihood Function for a One Sample t 522.7 Exercises 53References 533 Two Samples 553.1 Basics Using the t Distribution 553.1.1 Steps in Calculations 563.2 Related Samples 563.3 Independent Samples 593.3.1 Independent Samples with Unequal Variances 603.4 Calculation Simplification 623.5 If Variance is Known, or Large Sample Size, Use z 633.6 Methodological and Pro Forma Analyses 653.7 Adding More Data 683.8 Estimating Sample Size 703.8.1 Sample Size for One Sample and Related Samples 713.8.2 Sample Size for Independent Samples 733.9 Differences in Variances 733.10 R Code For Chapter 3 743.10.1 Calculating the Likelihood Function, the Likelihoods and Support for Independent Samples 743.10.2 Creating a Gardner–Altman Estimation Plot with Likelihood Function and Interval 763.11 Exercises 77References 774 ANOVA 794.1 Multiple Means 794.1.1 The Modelling Approach 794.1.2 Model Complexity 804.2 Example – Fitness 814.2.1 Comparing Models 824.2.2 Specific Model Comparisons 844.2.2.1 A Non-Orthogonal Contrast 884.2.3 Unequal Sample Sizes 894.3 Factorial ANOVA 904.3.1 Example – Blood Clotting Times 914.3.2 Specific Analyses in Factorial ANOVA, Including Contrasts 934.4 Alerting r2 964.4.1 Alerting r2 to Compare Contrasts for Effect Size 964.5 Repeated Measures Designs 974.5.1 Mixed Repeated Measures with Between Participant Designs 984.5.2 Contrasts in Mixed Designs 1004.6 Exercise 102References 1025 Correlation and Regression 1035.1 Relationships Between Two Variables 1035.2 Correlation 1035.2.1 Likelihood Intervals for Correlation 1075.3 Regression 1085.3.1 Obtaining Evidence from F values 1105.3.2 Examining Non-linearity 1115.4 Logistic Regression 1135.5 Exercises 120References 1206 Categorical Data 1216.1 Types of Categorical Data 1216.1.1 How is the ;;2 Test Used? 1226.2 Binomial 1236.2.1 Likelihood Intervals for Binomial 1256.2.2 Comparing Different ;; 1266.2.3 The Support Function 1276.3 Poisson 1296.4 Rate Ratios 1316.5 One-Way Categorical Data 1346.5.1 One-Way Categorical Comparing Different Expected Values 1356.5.2 One-Way with More than Two Categories 1356.6 2 × 2 Contingency Tables 1376.6.1 Paired 2 × 2 Categorical Analysis 1396.6.2 Diagnostic Tests 1416.6.2.1 Sensitivity and Specificity 1416.6.2.2 Positive and Negative Predictive Values 1426.6.2.3 Likelihood Ratio and Post-test Probability 1436.6.2.4 Comparing Sensitivities and Specificities of Two Diagnostic Procedures 1446.6.3 Odds Ratio 1466.6.3.1 Likelihood Function for the Odds Ratio 1496.6.4 Likelihood Function for Relative Risk with Fixed Entries 1516.7 Larger Contingency Tables 1516.7.1 Main Effects 1536.7.2 Evidence for Linear Trend 1546.7.3 Higher Dimensions? 1556.8 Data That Fits a Hypothesis Too Well 1586.9 Transformations of the Variable 1596.10 Clinical Trials – A Tragedy in 3 Acts 1616.11 R Code for Chapter 6 1646.11.1 One-Way Categorical Data Support Against Specified Proportions 1646.11.2 Calculating the Odds Ratio Likelihood Function and Support 1646.11.3 Calculating the Likelihood Function and Support for Relative Risk with Fixed Entries 1666.11.4 Calculating Interaction and Main Effects for Larger Contingency Tables 1686.11.5 Log-Linear Modelling for Multi-way Tables 1696.12 Exercises 171References 1727 Nonparametric Analyses 1757.1 So-Called ‘Distribution-Free’ Statistics 1757.2 Hacking SM 1767.3 One Sample and Related Samples 1767.4 Independent Samples 1797.5 More than Two Independent Samples 1817.6 Permutation Analyses 1827.7 Bootstrap Analyses for One Sample or Related Samples 1847.7.1 Bootstrap Analyses for Independent Samples 1867.8 R Code for Chapter 7 1877.8.1 Calculating Relative Support for One Sample 1877.8.2 Calculating Relative Support for Differences in Two Independent Samples 1887.8.3 Calculating Relative Support for Differences in Three Independent Samples 1897.8.4 Calculating Relative Support Using Permutations Analysis 1897.8.5 Bootstrap Analyses for One Sample 1917.8.6 Bootstrap Analyses for Two Independent Samples 1937.9 Exercises 195References 1968 Other Useful Techniques 1978.1 Other Techniques 1978.2 Critical Prior Interval 1978.3 False Positive Risk 2018.4 The Bayes Factor and the Probability of the Null Hypothesis 2058.4.1 Example 2088.5 Bayesian t Tests 2108.6 The Armitage Stopping Rule 2128.7 Counternull Effect Size 214References 217Appendix A Orthogonal Polynomials 219Appendix B Occam’s Bonus 221Reference 222Appendix C Problems with p Values 223C.1 The Misuse of p Values 223C.1.1 p Value Fallacies 225C.2 The Use of p Values 225C.2.1 Two Contradictory Traditions 226C.2.2 Whither the p Value? 227C.2.3 Remedies 228References 229Index 231