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Examples of the the word, variance , in a Sentence Context

The word ( variance ), is the 7283 most frequently used in English word vocabulary

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  1. N samples (x1,..., xn),s2n their sample variance , and σ2n their population, variance ,.: \bar x_n \franc \bar x_ + \franc \!: s^2_n = \franc, \quad n>1: \sigma^2_n =
  2. Edited segments of film what computers do today. Kurosawa’s habitual method—at, variance ,with the standard Hollywood practice of editing a film only after all footage
  3. Experiment, the assumption of unit-treatment additivity implies that the, variance ,is constant for all treatments. Therefore, by contraposition, a necessary
  4. Application of the analysis of variance was published in 1921. Analysis of, variance ,became widely known after being included in Fisher's 1925 book Statistical
  5. N - 1) return variance This algorithm can easily be adapted to compute the, variance ,of a finite population: simply divide by n instead of n − 1 on the last line.
  6. To R-squared. Omega2 (omega-squared): A more unbiased estimator of the, variance ,explained in the population is omega-squared: ^2 = \franc. While this form of
  7. And cooling of metal to achieve freedom from defects. The purpose of the random, variance ,is to find close to globally optimal solutions rather than simply locally
  8. The following formulas can be used to update the mean and (estimated), variance , of the sequence, for an additional element x_. Here, n denotes the sample mean
  9. A formula for calculating an unbiased estimate of the population, variance ,from a finite sample of n observations is:: s^2 = \display style\franc. \!
  10. Two-pass algorithm An alternate approach, using a different formula for the, variance , first computes the sample mean, : \bar x = \display style \sum_in x_j/n, and
  11. Displaystyle\franc. \! Therefore, a naive algorithm to calculate the estimated, variance ,is given by the following: def naive_ variance (data):
  12. When dealing with large values. Naïve algorithm A formula for calculating the, variance ,of an entire population of size n is:: \sigma^2 = \display style\franc. \! A
  13. Le grain né must – 1926 (translated as If It Dies) Algorithms for calculating, variance ,play a major role in statistical computing. A key problem in the design of good
  14. Each treatment (e.g., in a longitudinal study). *Multivariate analysis of, variance ,(ANOVA) is used when there is more than one response variable. History The
  15. Or observational study) that the responses be transformed to stabilize the, variance , Also, a statistician may specify that logarithmic transforms be applied to the
  16. Estimates only the effect size in the sample). On average, it overestimates the, variance ,explained in the population. As the sample size gets larger the amount of bias
  17. Sum_SQR + x*x mean = Sum/n variance = (Sum_SQR - Sum*mean)/ (n - 1) return, variance ,This algorithm can easily be adapted to compute the variance of a finite
  18. A model often presented in textbooks Many textbooks present the analysis of, variance ,in terms of a linear model, which makes the following assumptions about the
  19. In groups should be the same. Model-based approaches usually assume that the, variance ,is constant. The constant- variance property also appears in the randomization (
  20. 0 for x in data: n = n + 1 Sum = Sum + x Sum_SQR = Sum_SQR + x*x mean = Sum/n, variance ,= (Sum_SQR - Sum*mean)/ (n - 1) return variance This algorithm can easily be
  21. Uses the new value of mean variance _n = M2/n variance = M2/ (n - 1) return, variance ,This algorithm is much less prone to loss of precision due to massive
  22. Sum3 = 0 for x in data: sum2 = sum2 + (x - mean)**2 sum3 = sum3 + (x - mean), variance , = (sum2 - sum3**2/n)/ (n - 1) return variance On-line algorithm It is often
  23. Is used when there is more than one response variable. History The analysis of, variance ,was used informally by researchers in the 1800s using the least squares. In physics
  24. Inside the loop. For a particularly robust two-pass algorithm for computing the, variance , first compute and subtract an estimate of the mean, and then use this
  25. Sum1 + x mean = sum1/n for x in data: sum2 = sum2 + (x - mean)* (x - mean), variance , = sum2/ (n - 1) return variance This algorithm is often more numerically
  26. His government over a people who did not want it. Theoretical and material, variance ,among anarcho-capitalists A notable dispute within the anarcho-capitalist
  27. The sample mean of the first n samples (x1,..., xn),s2n their sample, variance , and σ2n their population variance .: \bar x_n \franc \bar x_ + \franc \!: s^2_n =
  28. If the responses of a randomized balanced experiment fail to have constant, variance , then the assumption of unit treatment additivity is necessarily violated. To
  29. Utilize more environmentally friendly alternatives. In statistics, analysis of, variance ,(ANOVA) is a collection of statistical models, and their associated
  30. X - mean) # This expression uses the new value of mean variance _n = M2/n, variance ,= M2/ (n - 1) return variance This algorithm is much less prone to loss of
  31. Contraposition, a necessary condition for unit-treatment additivity is that the, variance ,is constant. The property of unit-treatment additivity is not invariant under a
  32. And analysis for the two types. Assumptions of ANOVA The analysis of, variance ,has been studied from several approaches, the most common of which use a linear
  33. Stephen Stigler's histories. Sir Ronald Fisher proposed a formal analysis of, variance ,in a 1918 article The Correlation Between Relatives on the Supposition of
  34. M2 + delta * (mean) " sum weight = temp variance _n = M2/sum weight, variance ,= variance _n * Len (dataWeightPairs)/ (Len (dataWeightPairs) − 1) Parallel
  35. Here. Fixed-effects models (Model 1) The fixed-effects model of analysis of, variance ,applies to situations in which the experimenter applies one or more treatments
  36. Or more means. Models There are three classes of models used in the analysis of, variance , and these are outlined here. Fixed-effects models (Model 1) The
  37. Supposition of Mendelian Inheritance. His first application of the analysis of, variance ,was published in 1921. Analysis of variance became widely known after being
  38. 108 + 4,108 + 7,108 + 13,108 + 16),which gives rise to the same estimated, variance ,as the first sample. Algorithm II computes this variance estimate correctly
  39. Controlling for other predictors. Eta-squared is a biased estimator of the, variance ,explained by the model in the population (it estimates only the effect size in
  40. Of statistical models, and their associated procedures, in which the observed, variance ,in a particular variable is partitioned into components attributable to
  41. Data: sum2 = sum2 + (x - mean)* (x - mean) variance = sum2/ (n - 1) return, variance ,This algorithm is often more numerically reliable than the naïve algorithm for
  42. Is nonlinear, it can be approximated by a linear model for which an analysis of, variance ,may be appropriate. A model often presented in textbooks Many textbooks present
  43. To r2 when DF of the numerator equals 1 (both measures' proportion of, variance ,accounted for),these guidelines may overestimate the size of the effect. If
  44. Equality (or" homogeneity" ) of variance s, called homoscedasticity — the, variance ,of data in groups should be the same. Model-based approaches usually assume
  45. To the same estimated variance as the first sample. Algorithm II computes this, variance ,estimate correctly, but Algorithm I returns 29.333333333333332 instead of 30.
  46. Sum3 = sum3 + (x - mean) variance = (sum2 - sum3**2/n)/ (n - 1) return, variance ,On-line algorithm It is often useful to be able to compute the variance in a
  47. The estimated population mean is 10,and the unbiased estimate of population, variance ,is 30. Both Algorithm I and Algorithm II compute these values correctly. Next
  48. And intercorrelation. Η2 (eta-squared): Eta-squared describes the ratio of, variance ,explained in the dependent variable by a predictor while controlling for other
  49. Return variance On-line algorithm It is often useful to be able to compute the, variance ,in a single pass, inspecting each value x_i only once; for example, when the
  50. In the design of good algorithms for this problem is that formulas for the, variance ,may involve sums of squares, which can lead to numerical instability as well as

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