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t. -test. In statistics, Welch's t-test, or unequal variances t-test, is a two-sample location test which is used to test the (null) hypothesis that two populations have equal means. It is named for its creator, Bernard Lewis Welch, and is an adaptation of Student's t -test, [1] and is more reliable when the two samples have unequal variances ...
Prism dioptres. Prism correction is commonly specified in prism dioptres, a unit of angular measurement that is loosely related to the dioptre. Prism dioptres are represented by the Greek symbol delta (Δ) in superscript. A prism of power 1 Δ would produce 1 unit of displacement for an object held 100 units from the prism. [2]
Although the 30 samples were all simulated under the null, one of the resulting p-values is small enough to produce a false rejection at the typical level 0.05 in the absence of correction. Multiple comparisons arise when a statistical analysis involves multiple simultaneous statistical tests, each of which has a potential to produce a "discovery".
Amblyopia. Anisometropia is a condition in which a person's eyes have substantially differing refractive power. [1] Generally, a difference in power of one diopter (1D) is the threshold for diagnosis of the condition . [2] [3] Patients may have up to 3D of anisometropia before the condition becomes clinically significant due to headache, eye ...
Esophoria is an eye condition involving inward deviation of the eye, usually due to extra-ocular muscle imbalance. It is a type of heterophoria. Cause. Causes include: Refractive errors; Divergence insufficiency; Convergence excess; this can be due to nerve, muscle, congenital or mechanical anomalies.
In statistics, Bessel's correction is the use of n − 1 instead of n in the formula for the sample variance and sample standard deviation, where n is the number of observations in a sample. This method corrects the bias in the estimation of the population variance.
If we do likewise with the formula for r p , the result is easily shown to be equivalent to r p = tan ( θ i − θ t ) tan ( θ i + θ t ) . {\displaystyle r_{\text{p}}={\frac {\tan(\theta _{\text{i}}-\theta _{\text{t}})}{\tan(\theta _{\text{i}}+\theta _{\text{t}})}}.}
def two_pass_covariance(data1, data2): n = len(data1) mean1 = sum(data1) / n mean2 = sum(data2) / n covariance = 0 for i1, i2 in zip(data1, data2): a = i1 - mean1 b = i2 - mean2 covariance += a * b / n return covariance. A slightly more accurate compensated version performs the full naive algorithm on the residuals.
Although there are many possible estimators, a conventional one is to use ^ , the sample mean, and plug this into the formula. That gives: That gives: SE { p ^ } ≈ p ^ ( 1 − p ^ ) ∑ i = 1 n w i 2 {\displaystyle \ \operatorname {SE} \{\ {\hat {p}}\ \}\approx {\sqrt {~{\hat {p}}\ (1-{\hat {p}})\ \sum _{i=1}^{n}w_{i}^{2}~~}}\ }
λ = ν + λp, the Sun's true longitude on the ecliptic. The celestial sphere and the Sun's elliptical orbit as seen by a geocentric observer looking normal to the ecliptic showing the 6 angles ( M, λp, α, ν, λ, E) needed for the calculation of the equation of time. For the sake of clarity the drawings are not to scale.