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  2. Mode (statistics) - Wikipedia

    en.wikipedia.org/wiki/Mode_(statistics)

    The mode of a sample is the element that occurs most often in the collection. For example, the mode of the sample [1, 3, 6, 6, 6, 6, 7, 7, 12, 12, 17] is 6. Given the list of data [1, 1, 2, 4, 4] its mode is not unique. A dataset, in such a case, is said to be bimodal, while a set with more than two modes may be described as multimodal.

  3. Mean squared error - Wikipedia

    en.wikipedia.org/wiki/Mean_squared_error

    The MSE either assesses the quality of a predictor (i.e., a function mapping arbitrary inputs to a sample of values of some random variable), or of an estimator (i.e., a mathematical function mapping a sample of data to an estimate of a parameter of the population from which the data is sampled).

  4. Minimum mean square error - Wikipedia

    en.wikipedia.org/wiki/Minimum_mean_square_error

    Also, the gain factor, +, depends on our confidence in the new data sample, as measured by the noise variance, versus that in the previous data. The initial values of x ^ {\displaystyle {\hat {x}}} and C e {\displaystyle C_{e}} are taken to be the mean and covariance of the aprior probability density function of x {\displaystyle x} .

  5. Expected value - Wikipedia

    en.wikipedia.org/wiki/Expected_value

    For example, for any random variable with finite expectation, the Chebyshev inequality implies that there is at least a 75% probability of an outcome being within two standard deviations of the expected value. However, in special cases the Markov and Chebyshev inequalities often give much weaker information than is otherwise available.

  6. Normal distribution - Wikipedia

    en.wikipedia.org/wiki/Normal_distribution

    In probability theory and statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable.The general form of its probability density function is [2] [3] = ().

  7. Logistic distribution - Wikipedia

    en.wikipedia.org/wiki/Logistic_distribution

    As the logistic distribution, which can be solved analytically, is similar to the normal distribution, it can be used instead. The blue picture illustrates an example of fitting the logistic distribution to ranked October rainfalls—that are almost normally distributed—and it shows the 90% confidence belt based on the binomial distribution.

  8. Noncentral t-distribution - Wikipedia

    en.wikipedia.org/wiki/Noncentral_t-distribution

    where ¯ is the sample mean and ^ is the unbiased sample variance. Since the right hand side of the second equality exactly matches the characterization of a noncentral t -distribution as described above, T has a noncentral t -distribution with n −1 degrees of freedom and noncentrality parameter n θ / σ {\displaystyle {\sqrt {n}}\theta ...

  9. Conditional expectation - Wikipedia

    en.wikipedia.org/wiki/Conditional_expectation

    The unconditional expectation of rainfall for an unspecified day is the average of the rainfall amounts for those 3652 days. The conditional expectation of rainfall for an otherwise unspecified day known to be (conditional on being) in the month of March, is the average of daily rainfall over all 310 days of the ten–year period that fall in ...

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