# Statistical Concepts

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# Internal

# 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
- Percentile
- 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

## 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