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In statistics, the number of degrees of freedom is the number of values in the final calculation of a statistic that are free to vary. [1] Estimates of statistical parameters can be based upon different amounts of information or data. The number of independent pieces of information that go into the estimate of a parameter is called the degrees ...
Because half of the sample now depends on the other half, the paired version of Student's t-test has only n / 2 − 1 degrees of freedom (with n being the total number of observations). Pairs become individual test units, and the sample has to be doubled to achieve the same number of degrees of freedom.
For the statistic t, with ν degrees of freedom, A(t | ν) is the probability that t would be less than the observed value if the two means were the same (provided that the smaller mean is subtracted from the larger, so that t ≥ 0). It can be easily calculated from the cumulative distribution function F ν (t) of the t distribution:
The definitional equation of sample variance is = (¯), where the divisor is called the degrees of freedom (DF), the summation is called the sum of squares (SS), the result is called the mean square (MS) and the squared terms are deviations from the sample mean. ANOVA estimates 3 sample variances: a total variance based on all the observation ...
Degrees of freedom (physics and chemistry), a term used in explaining dependence on parameters, or the dimensions of a phase space; Degrees of freedom (statistics), the number of values in the final calculation of a statistic that are free to vary; Degrees of freedom problem, the problem of controlling motor movement given abundant degrees of ...
It enters all analysis of variance problems via its role in the F-distribution, which is the distribution of the ratio of two independent chi-squared random variables, each divided by their respective degrees of freedom. Following are some of the most common situations in which the chi-squared distribution arises from a Gaussian-distributed sample.