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