Statistical Concepts: Difference between revisions
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=Concepts= | =Concepts= | ||
* [[Descriptive_Statistics#Overview|Descriptive Statistics]] | |||
* Mean/Median/Mode. Difference between mean and average. | * Mean/Median/Mode. Difference between mean and average. | ||
* Unique Median | * Unique Median | ||
* standard deviation | * standard deviation | ||
* | * <span id='Regression'></span>[[Regression|Regression]] and [[Regression#Linear_Regression|Linear Regression]]. | ||
** Dependent variable (criterion) | |||
* | ** Independent variable (predictor) | ||
* [[Classification]] | |||
* [[Bayes Rule]] | * [[Bayes Rule]] | ||
* [[Percentile]] | |||
* Scatter Plot | * Scatter Plot | ||
* Linearity (linear exact or not exact) | * Linearity (linear exact or not exact) | ||
| Line 43: | Line 49: | ||
* Continous Probability Distribution. | * Continous Probability Distribution. | ||
* Density of probability | * Density of probability | ||
* Estimators | * Estimators | ||
* Laplacian estimator | * Laplacian estimator | ||
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* Standard Deviation | * Standard Deviation | ||
* Standard Score | * Standard Score | ||
* | |||
* [[Time Series]] | |||
==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= | =TODO= | ||
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* Relocate Continous Functions | * Relocate Continous Functions | ||
* Granger causality test https://en.wikipedia.org/wiki/Granger_causality | |||
Latest revision as of 22:28, 14 May 2024
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