StataQuestIndex

Statistics Fundamentals

1. Histograms, Clearly Explained
2. How to tell a story with US Census Data
3. What is a statistical distribution?
   1. StatTest!
4. The Normal Distribution
   1. StatTest!
5. Statistics Fundamentals: Population Parameters
6. Statistics Fundamentals: Estimating the Mean, Variance and Standard Deviation
   1. Why Dividing by N Underestimates the Variance
7. Covariance and Correlation Part 1: Covariance
8. Covariance and Correlation Part 2: Pearson’s Correlation
9. What is a statistical model?
   1. StatTest!
10. What does it mean to “sample from a distribution”?
    1. StatTest!
11. Expected Values Part 1, Main Ideas!!! (Expected Values for Discrete Variables)
    1. Expected Values Part 2, Continuous Variables
12. Hypothesis Testing and the Null Hypothesis
13. Alternative Hypothesis: Main Ideas
14. p-values: What they are and how to interpret them
15. How to Calculate p-values
16. p-hacking: What it is and how to avoid it
17. False Discovery Rate (FDR), Clearly Explained
18. Statistical Power, Clearly Explained
19. Power Analysis, Clearly Explained
20. Conditional Probability, Clearly Explained
21. The Binomial Distribution and Test
22. The Central Limit Theorem (or “How I Learned to Stop Worrying and Love the t-test”).
23. The Difference between Technical and Biological Replicates
    1. StatTest!
24. The sample size and the effective sample size
    1. StatTest!
25. Standard Deviation vs Standard Error
    1. StatTest!
26. The Standard Error
27. Bootstrapping Part 1: Main Ideas
28. Bootstrapping Part 2: Calculating p-values
29. Bar Charts Are Better Than Pie Charts
30. Boxplots, Clearly Explained
31. Logs (logarithms), clearly explained
32. How to make your own StatQuest!!!
33. Confidence Intervals
34. R-squared explained
    1. StatTest!
35. Linear Models Part 0: Fitting a line to data, aka Least Squares, aka Linear Regression
36. Fitting a curve to data, aka Lowess, aka Loess
    1. Sample Code
37. Linear Models Part 1: Linear Regression
    1. Study Guide
38. Linear Models: Linear Regression in R
    1. Sample Code
39. Linear Models Part 1.5: Multiple Regression
40. Linear Models: Multiple Regression in R
    1. Sample Code
41. Linear Models Part 2: t-tests and ANOVA
42. Linear Models Part 3: Design Matrices
43. Linear Models: Design Matrix Examples in R
    1. Sample Code
44. Quantiles and Percentiles
    1. StatTest!
45. Quantile-Quantile Plots (QQ Plots)
46. Quantile Normalization
    1. StatTest!
47. Probability vs Likelihood
48. Maximum Likelihood
49. Maximum Likelihood: A worked out example for the exponential distribution
50. Maximum Likelihood: A worked out example for the binomial distribution
51. Maximum Likelihood: A worked out example for the normal distribution
52. Odds and Log(Odds)
53. Odds Ratios and Log(Odds Ratios)

Statistical Tests:

• Enrichment Analysis using Fisher’s Exact Test and the Hypergeometric Distribution
• Which t-test to use
• p-values: What they are and how to interpret them
• How to Calculate p-values
• Thresholds for Significance
• FDR and the Benjamini-Hochberg Method clearly explained
• p-hacking and power calculations

High-throughput Sequencing Analysis:

• A Gentle Introduction to RNA-seq
• A Gentle Introduction to ChIP-seq
• edgeR, part1: Library Normalization
• DESeq2, part1: Library Normalization
• edgeR and DESeq2, part2: Independent Filtering (removing genes with low read counts)
• RNA-seq – The Problem with Technical Replicates
• RPKM, FPKM, and TPM

Statistics and Machine Learning in R
https://youtube.com/playlist?list=PLblh5JKOoLUJJpBNfk8_YadPwDTO2SCbx
Logistic Regression
Random Forests
Linear Regression and Linear Models