Statistical Concepts

Internal

• Statistics

Concepts

• Mean/Median/Mode. Difference between mean and average.
• Unique Median
• standard deviation
• Regression and Linear Regression.
  • Dependent variable (criterion)
  • Independent variable (predictor)

• Bayes Rule
• Scatter Plot
• Linearity (linear exact or not exact)
• Positive and negative linear relationship.
• Outlier
• Deviation
• Noise - deviation from a linear graph.
• Monotonicity.
• Bar Charts. Applies to 2D data.
• Global trends.
• Historgram. A bar chart where the vertical axis is a frequency count, as a function of the range. Applies to 1D data.
• Frequency Count
• Pie charts - represent relative outcomes.
• Unrelated data
• Simpson's paradox
• Be skeptical and really understand how to turn raw data into conclusions.
• Probability - the opposite of statistics.
• P() notation
• Truth table
• Probability of a composite event (independence)
• Dependence
• Conditional probability
• Conditional probability notation - important for Bayes Rule
• Total probability
• Bayes Rule
  • Prior probability
  • Unreliable measurement (Sensitivity/Specificity)
  • Joint probabilty
  • Posterior probabilty
• Probability Distribution
• Continous Probability Distribution.
• Density of probability

• Estimators
• Laplacian estimator
• Empirical (observational) frequency
• Maximum likelihood estimator
• Dirichelet data
• Laplacian Estimator
• Mode, bimodal, multimodal
• Variance
• Standard Deviation
• Standard Score
• 95% percentile.

Correlation and Causation

• Correlation vs. Causation
• Variables
• Definition of correlation (Is correlation injectivity?)
• Confounding variable.
• https://en.wikipedia.org/wiki/Correlation_does_not_imply_causation
• Even if there is no causation, correlation can be used in prediction.

TODO

• Relocate Continous Functions
• Granger causality test https://en.wikipedia.org/wiki/Granger_causality