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2. Least squares - Wikipedia

   en.wikipedia.org/wiki/Least_squares

   The result of fitting a set of data points with a quadratic function Conic fitting a set of points using least-squares approximation. The method of least squares is a parameter estimation method in regression analysis based on minimizing the sum of the squares of the residuals (a residual being the difference between an observed value and the fitted value provided by a model) made in the ...

3. Geometric distribution - Wikipedia

   en.wikipedia.org/wiki/Geometric_distribution

   In probability theory and statistics, the geometric distribution is either one of two discrete probability distributions : The probability distribution of the number of Bernoulli trials needed to get one success, supported on the set ; The probability distribution of the number of failures before the first success, supported on the set .

4. Bias of an estimator - Wikipedia

   en.wikipedia.org/wiki/Bias_of_an_estimator

   In statistics, the bias of an estimator (or bias function) is the difference between this estimator 's expected value and the true value of the parameter being estimated. An estimator or decision rule with zero bias is called unbiased. In statistics, "bias" is an objective property of an estimator. Bias is a distinct concept from consistency ...

5. Estimator - Wikipedia

   en.wikipedia.org/wiki/Estimator

   An "estimator" or "point estimate" is a statistic (that is, a function of the data) that is used to infer the value of an unknown parameter in a statistical model. A common way of phrasing it is "the estimator is the method selected to obtain an estimate of an unknown parameter". The parameter being estimated is sometimes called the estimand.

6. Standard deviation - Wikipedia

   en.wikipedia.org/wiki/Standard_deviation

   The standard deviation σ of X is defined as which can be shown to equal. Using words, the standard deviation is the square root of the variance of X . The standard deviation of a probability distribution is the same as that of a random variable having that distribution. Not all random variables have a standard deviation.

7. Probability density function - Wikipedia

   en.wikipedia.org/wiki/Probability_density_function

   In probability theory, a probability density function (PDF), density function, or density of an absolutely continuous random variable, is a function whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) can be interpreted as providing a relative likelihood that the value of the random variable would be equal to that sample.