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  2. Expected mean squares - Wikipedia

    en.wikipedia.org/wiki/Expected_mean_squares

    In statistics, expected mean squares (EMS) are the expected values of certain statistics arising in partitions of sums of squares in the analysis of variance (ANOVA). They can be used for ascertaining which statistic should appear in the denominator in an F-test for testing a null hypothesis that a particular effect is absent.

  3. Expected value - Wikipedia

    en.wikipedia.org/wiki/Expected_value

    The book extended the concept of expectation by adding rules for how to calculate expectations in more complicated situations than the original problem (e.g., for three or more players), and can be seen as the first successful attempt at laying down the foundations of the theory of probability. In the foreword to his treatise, Huygens wrote:

  4. Chi-squared distribution - Wikipedia

    en.wikipedia.org/wiki/Chi-squared_distribution

    Because the square of a standard normal distribution is the chi-squared distribution with one degree of freedom, the probability of a result such as 1 heads in 10 trials can be approximated either by using the normal distribution directly, or the chi-squared distribution for the normalised, squared difference between observed and expected value.

  5. Chi distribution - Wikipedia

    en.wikipedia.org/wiki/Chi_distribution

    It is the distribution of the positive square root of a sum of squared independent Gaussian random variables. Equivalently, it is the distribution of the Euclidean distance between a multivariate Gaussian random variable and the origin. The chi distribution describes the positive square roots of a variable obeying a chi-squared distribution.

  6. Variance - Wikipedia

    en.wikipedia.org/wiki/Variance

    The conditional expectation ... (the square root of variance). The square root is a concave function and thus introduces negative bias (by Jensen's inequality), ...

  7. Distribution of the product of two random variables - Wikipedia

    en.wikipedia.org/wiki/Distribution_of_the...

    When two random variables are statistically independent, the expectation of their product is the product of their expectations. This can be proved from the law of total expectation : E ⁡ ( X Y ) = E ⁡ ( E ⁡ ( X Y ∣ Y ) ) {\displaystyle \operatorname {E} (XY)=\operatorname {E} (\operatorname {E} (XY\mid Y))}

  8. How U.S. home sizes have evolved over time

    www.aol.com/finance/u-home-sizes-evolved-over...

    The median home size may have jumped by around 800 square feet since the 1960s, but in recent years, it has begun trending downward. Census data shows that, in 2021, new-construction single-family ...

  9. Noncentral chi-squared distribution - Wikipedia

    en.wikipedia.org/wiki/Noncentral_chi-squared...

    The probability density function (pdf) is given by (;,) = = / (/)! + (),where is distributed as chi-squared with degrees of freedom.. From this representation, the noncentral chi-squared distribution is seen to be a Poisson-weighted mixture of central chi-squared distributions.