Statistical Concepts: Difference between revisions
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* correlation | * correlation | ||
* [[Bayes Rule]] | * [[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 | |||
** [https://kb.novaordis.com/index.php/Bayes_Rule] | |||
** Prior probability | |||
** Unreliable measurement (Sensitivity/Specificity) | |||
** Joint probabilty | |||
** Posterior probabilty | |||
* Probability Distribution | |||
* Continous Probability Distribution. | |||
* Density of probability | |||
* Correlation vs. Causation | |||
* Variables | |||
* Definition of correlation (Is correlation injectivity?) | |||
* Confounding variable. | |||
* Estimators | |||
* Laplacian estimator | |||
* Empirical (observational) frequency | |||
* Maximum likelihood estimator | |||
=TODO= | |||
* Relocate Continous Functions | |||
Revision as of 16:34, 10 October 2016
Internal
Concepts
- mean
- standard deviation
- linear regression
- correlation
- 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
- [1]
- Prior probability
- Unreliable measurement (Sensitivity/Specificity)
- Joint probabilty
- Posterior probabilty
- Probability Distribution
- Continous Probability Distribution.
- Density of probability
- Correlation vs. Causation
- Variables
- Definition of correlation (Is correlation injectivity?)
- Confounding variable.
- Estimators
- Laplacian estimator
- Empirical (observational) frequency
- Maximum likelihood estimator
TODO
- Relocate Continous Functions