• bmbstats book
  • Welcome
    • R and R packages
    • License
  • I Part One
  • 1 Introduction
  • 2 Description
    • 2.1 Comparing two independent groups
      • 2.1.1 Sample mean as the simplest statistical model
      • 2.1.2 Effect Sizes
      • 2.1.3 The Smallest Effect Size Of Interest
    • 2.2 Comparing dependent groups
      • 2.2.1 Describing groups as independent
      • 2.2.2 Effect Sizes
    • 2.3 Describing relationship between two variables
      • 2.3.1 Magnitude-based estimators
    • 2.4 Advanced uses
  • 3 Prediction
    • 3.1 Overfitting
    • 3.2 Cross-Validation
      • 3.2.1 Sample mean as the simplest predictive model
    • 3.3 Bias-Variance decomposition and trade-off
    • 3.4 Interpretability
    • 3.5 Magnitude-based prediction estimators
    • 3.6 Practical example: MAS and YoYoIR1 prediction
      • 3.6.1 Predicting MAS from YoYoIR1
      • 3.6.2 Predicting YoYoIR1 from MAS
  • 4 Causal inference
    • 4.1 Necessary versus sufficient causality
    • 4.2 Observational data
    • 4.3 Potential outcomes or counterfactuals
    • 4.4 Ceteris paribus and the biases
      • 4.4.1 Randomization
    • 4.5 Subject matter knowledge
    • 4.6 Example of randomized control trial
    • 4.7 Prediction as a complement to causal inference
      • 4.7.1 Analysis of the individual residuals: responders vs non-responders
      • 4.7.2 Counterfactual analysis and Individual Treatment Effects
      • 4.7.3 Direct and indirect effect, covariates and then some
      • 4.7.4 Model selection
    • 4.8 Ergodicity
  • 5 Statistical inference
    • 5.1 Two kinds of uncertainty, probability, and statistical inference
  • 6 Frequentist perspective
    • 6.1 Null-Hypothesis Significance Testing
    • 6.2 Statistical Power
    • 6.3 New Statistics: Confidence Intervals and Estimation
    • 6.4 Minimum Effect Tests
      • 6.4.1 Individual vs. Parameter SESOI
      • 6.4.2 Two one-sided tests of equivalence
      • 6.4.3 Superiority and Non-Inferiority
      • 6.4.4 Inferiority and Non-Superiority
      • 6.4.5 Inference from METs
    • 6.5 Magnitude Based Inference
  • 7 Bayesian perspective
    • 7.1 Grid approximation
    • 7.2 Priors
    • 7.3 Likelihood function
    • 7.4 Posterior probability
    • 7.5 Adding more possibilities
    • 7.6 Different prior
    • 7.7 More data
    • 7.8 Summarizing prior and posterior distributions with MAP and HDI
    • 7.9 Comparison to NHST Type I errors
  • 8 Bootstrap
    • 8.1 Summarizing bootstrap distribution
    • 8.2 Bootstrap Type I errors
  • 9 Statistical inference conclusion
  • 10 Measurement Error
    • 10.1 Estimating TE using ordinary least products regression
    • 10.2 Smallest Detectable Change
    • 10.3 Interpreting individual changes using SESOI and SDC
    • 10.4 What to do when we know the error?
    • 10.5 Extending the Classical Test Theory
  • 11 Conclusion
  • II Part Two
  • 12 bmbstats: Bootstrap Magnitude-based Statistics package
    • 12.1 bmbstats Installation
  • 13 Descriptive tasks using bmbstats
    • 13.1 Generating height data
    • 13.2 Visualization and analysis of a single group (or variable)
      • 13.2.1 Using your own estimators
    • 13.3 Visualization and analysis of the two independent groups
    • 13.4 NHST, METs and MBI functions
    • 13.5 Comparing two dependent groups
      • 13.5.1 Measurement error issues
      • 13.5.2 Analysis of the dependent groups using compare_dependent_groups
      • 13.5.3 Statistical tests
    • 13.6 Describing relationship between two groups
  • 14 Predictive tasks using bmbstats
    • 14.1 How to implement different performance metrics?
    • 14.2 How to use different prediction model?
    • 14.3 Example of using tuning parameter
    • 14.4 Plotting
    • 14.5 Comparing models
    • 14.6 Bootstrapping model
  • 15 Validity and Reliability
    • 15.1 Data generation
    • 15.2 Validity
      • 15.2.1 True vs Criterion
      • 15.2.2 Practical vs Criterion
      • 15.2.3 Prediction approach
      • 15.2.4 Can we adjust for the know criterion measure random error?
      • 15.2.5 Estimating SESOI for the practical score
    • 15.3 Reliability
      • 15.3.1 Reproducibility
    • 15.4 Repeatability
    • 15.5 The difference between Reproducibility and Repeatability
  • 16 RCT analysis and prediction in bmbstats
    • 16.1 Data Generating Process behind RCT
    • 16.2 RCT analysis using bmbstats::RCT_analysis function
    • 16.3 Linear Regression Perspective
    • 16.4 Prediction perspective 1
    • 16.5 Adding some effects
    • 16.6 What goes inside the measurement error (or Control group change or residuals SD)?
    • 16.7 Prediction perspective 2
    • 16.8 Making it more complex by adding covariate
    • 16.9 Prediction perspective 3
  • 17 Appendix A: dorem package
    • 17.1 dorem Installation
    • 17.2 dorem Example
  • 18 Appendix B: shorts package
    • 18.1 shorts Installation
    • 18.2 short Examples
      • 18.2.1 Profiling using split times
      • 18.2.2 Profiling using radar gun data
      • 18.2.3 Using corrections
      • 18.2.4 Leave-One-Out Cross-Validation (LOOCV)
    • 18.3 shorts Citation
  • 19 Appendix C: vjsim package
    • 19.1 vjsim Installation
    • 19.2 vjsim Usage
      • 19.2.1 Introduction to vjsim
      • 19.2.2 Simulation
      • 19.2.3 Profiling
      • 19.2.4 Optimization
      • 19.2.5 Exploring
      • 19.2.6 Modeling
      • 19.2.7 Shiny App
    • 19.3 vjsim Example
  • 20 Appendix D: Recommended material
  • 21 About
  • References

bmbstats: bootstrap magnitude-based statistics for sports scientists

Chapter 20 Appendix D: Recommended material

General

  1. Dienes Z. 2008. Understanding Psychology as a Science: An Introduction to Scientific and Statistical Inference. New York: Red Globe Press.

  2. Ellenberg J, Ellenberg J. 2014. How not to be wrong: the hidden maths of everyday life. New York, New York: Penguin Books.

  3. Foreman JW. 2014. Data smart: using data science to transform information into insight. Hoboken, New Jersey: John Wiley & Sons.

  4. Gelman A, Hill J, Vehtari A. 2020. Regression and Other Stories. S.l.: Cambridge University Press.

  5. Spiegelhalter D. 2019. The art of statistics: how to learn from data. New York: Basic Books, an imprint of Perseus Books, a subsidiary of Hachette Book Group.

Bayesian analysis

  1. Kruschke JK. 2015. Doing Bayesian data analysis: a tutorial with R, JAGS, and Stan. Boston: Academic Press.

  2. Lambert B. 2018. A student’s guide to Bayesian statistics. Los Angeles: SAGE.

  3. McElreath R. 2020. Statistical rethinking: a Bayesian course with examples in R and Stan. Boca Raton: Taylor and Francis, CRC Press.

  4. Stanton JM. 2017. Reasoning with data: an introduction to traditional and Bayesian statistics using R. New York: The Guilford Press.

Predictive analysis and Machine Learning

  1. James G, Witten D, Hastie T, Tibshirani R. 2017. An Introduction to Statistical Learning: with Applications in R. New York: Springer.

  2. Kuhn M, Johnson K. 2018. Applied Predictive Modeling. New York: Springer.

  3. Kuhn M, Johnson K. 2019. Feature Engineering and Selection: a Practical Approach for Predictive Models. Milton: CRC Press LLC.

  4. Lantz B. 2019. Machine learning with R: expert techniques for predictive modeling.

  5. Rhys HI. 2020. Machine Learning with R, the tidyverse, and mlr. Manning Publications.

Causal inference

  1. Hernán MA, Robins J. 2019. Causal Inference. Boca Raton: Chapman & Hall/CRC.

  2. Kleinberg S. 2015. Why: A Guide to Finding and Using Causes. Beijing; Boston: O’Reilly Media.

  3. Pearl J, Mackenzie D. 2018. The Book of Why: The New Science of Cause and Effect. New York: Basic Books.

R Language and Visualization

  1. Healy K. 2018. Data visualization: a practical introduction. Princeton, NJ: Princeton University Press.

  2. Kabacoff R. 2015. R in action: data analysis and graphics with R. Shelter Island: Manning.

  3. Matloff NS. 2011. The art of R programming: tour of statistical software design. San Francisco: No Starch Press.

  4. Wickham H, Grolemund G. 2016. R for data science: import, tidy, transform, visualize, and model data. Sebastopol, CA: O’Reilly.

  5. Wickham H. 2019. Advanced R, Second Edition. Boca Raton: Chapman and Hall/CRC.

  6. Wilke C. 2019. Fundamentals of data visualization: a primer on making informative and compelling figures. Sebastopol, CA: O’Reilly Media.

Simulation and statistical inference

  1. Carsey T, Harden J. 2013. Monte Carlo Simulation and Resampling Methods for Social Science. Los Angeles: Sage Publications, Inc.

  2. Efron B, Hastie T. 2016. Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. New York, NY: Cambridge University Press.

Online Courses

  1. Hastie T, Tibshirani R. 2016. Statistical Learning. Available at https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about (accessed October 16, 2019).

  2. Lakens D. 2017. Improving your statistical inferences. Available at https://www.coursera.org/learn/statistical-inferences (accessed October 16, 2019).

  3. Lakens D. 2019. Improving your statistical questions. Available at https://www.coursera.org/learn/improving-statistical-questions (accessed October 16, 2019).