Welcome to our stats refresher course. The instructors for this summer are Rafael and Josh. The structure of this course is simple: Each week we will list online resources for you to review in advance of our weekly meeting. In the meeting, we will give a light overview of the topics and answer any questions that you might have. In addition, the meetings will give you a chance to get advice on analyses for your own studies.
- Study Design https://www.khanacademy.org/math/statistics-probability/designing-studies
- Concepts, terminology, and research designs https://www.khanacademy.org/math/statistics-probability/significance-tests-one-sample/error-probabilities-and-power/v/introduction-to-type-i-and-type-ii-errors
- Data Management https://library.si.edu/sites/default/files/pdf/rdm_best_practices.pdf
- Statistical best practices https://www.nature.com/articles/s41562-021-01211-8
- Central tendency and dispersion
- https://www.khanacademy.org/math/statistics-probability/summarizing-quantitative-data/mean-median-basics/v/statistics-intro-mean-median-and-mode
- https://www.khanacademy.org/math/statistics-probability/summarizing-quantitative-data/more-mean-median/e/calculating-the-mean-from-various-data-displays
- https://www.khanacademy.org/math/statistics-probability/summarizing-quantitative-data/interquartile-range-iqr/v/calculating-interquartile-range-iqr
- https://www.khanacademy.org/math/statistics-probability/summarizing-quantitative-data/variance-standard-deviation-population/v/range-variance-and-standard-deviation-as-measures-of-dispersion
- https://www.khanacademy.org/math/statistics-probability/summarizing-quantitative-data/more-on-standard-deviation/v/another-simulation-giving-evidence-that-n-1-gives-us-an-unbiased-estimate-of-variance
- https://www.khanacademy.org/math/statistics-probability/summarizing-quantitative-data/box-whisker-plots/v/box-and-whisker-plot-exercise-example
- https://www.khanacademy.org/math/statistics-probability/summarizing-quantitative-data/other-measures-of-spread/v/range-and-mid-range
- Normal curve, Z-scores, hypothesis testing
- https://www.khanacademy.org/math/statistics-probability/modeling-distributions-of-data/z-scores/v/ck12-org-normal-distribution-problems-z-score
- https://www.khanacademy.org/math/statistics-probability/modeling-distributions-of-data/normal-distributions-library/v/ck12-org-normal-distribution-problems-qualitative-sense-of-normal-distributions
- https://www.khanacademy.org/math/statistics-probability/modeling-distributions-of-data/normal-distribution-calculation/v/z-table-for-proportion-below
- https://www.khanacademy.org/math/statistics-probability/modeling-distributions-of-data/more-on-normal-distributions/v/introduction-to-the-normal-distribution
- https://www.khanacademy.org/math/statistics-probability/significance-tests-one-sample/idea-of-significance-tests/v/simple-hypothesis-testing
- https://www.khanacademy.org/math/statistics-probability/significance-tests-one-sample/more-significance-testing-videos/v/hypothesis-testing-and-p-values
- https://www.khanacademy.org/math/statistics-probability/significance-tests-confidence-intervals-two-samples
- Regression
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Simple linear regression i. her whole series seems good: https://www.youtube.com/watch?v=KBTT052uwww&list=PLdxWrq0zBgPVjvYGoxlc2A5vIpO9NQvw3 ii. https://www.khanacademy.org/math/statistics-probability/describing-relationships-quantitative-data#regression-library
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when you do t-tests and anova and other tests you are implicitely doing regression
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Maximum likelihood interpretation of least squares: https://www.youtube.com/watch?v=avs4V7wBRw0
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Multiple linear regression: https://www.youtube.com/watch?v=5tCSR5L4nWI
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Generalized linear models i. Logistic regression: https://www.youtube.com/watch?v=yIYKR4sgzI8 ii. Poisson regression: https://www.youtube.com/watch?v=Obpz_Uvo2rQ
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Mixed effects models: https://www.youtube.com/watch?v=QeCJ9ON0WDc i. Repeated measures ii. Hierarchical models: 1. https://statmodeling.stat.columbia.edu/2014/01/21/everything-need-know-bayesian-statistics-learned-eight-schools/ 2.
- Model generalizability: https://www.youtube.com/watch?v=RsBvptfzDPs
- Bias variance tradeoff https://www.youtube.com/watch?v=EuBBz3bI-aA
- Cross validation: https://www.youtube.com/watch?v=fSytzGwwBVw
- Information criterion: https://www.youtube.com/watch?v=xS4jDHQfP2o
- Advanced topics
- Bootstrap: https://www.youtube.com/watch?v=Xz0x-8-cgaQ
- Regularization: Ridge - https://www.youtube.com/watch?v=Q81RR3yKn30 Lasso: https://www.youtube.com/watch?v=NGf0voTMlcs
- Bayes: for babies https://www.youtube.com/watch?v=CcnLnKU26dg intro lecture https://www.youtube.com/watch?v=9wCnvr7Xw4E
- Causal inference: https://www.youtube.com/watch?v=gRkUhg9Wb-I
- Tests as Linear Models: https://lindeloev.github.io/tests-as-linear/ https://www.youtube.com/watch?v=NF5_btOaCig
- Let's talk about your analyses
- Josh: josh.chang@nih.gov
- Rafael: rafael.jimenezsilva@nih.gov