Category
page 1Analysis of variance
analysis of variance
collection of statistical models used to analyze the differences between group means and their associated procedures
F-distribution
{(d_1 x+d_2)^{d_1+d_2{x\,\mathrm{B}\!\left(\frac{d_1}{2},\frac{d_2}{2}\right)}\!
| cdf = I_{\frac{d_1 x}{d_1 x + d_2 \left(\tfrac{d_1}{2}, \tfrac{d_2}{2} \right)
| mean = \frac{d_2}{d_2-2}\! for d2 > 2
| median =
| mode = \frac{d_1-2}{d_1}\;\frac{d_2}{d_2+2} for d1 > 2
| variance = \frac{2\,d_2^2\,(d_1+d_2-2)}{d_1 (d_2-2)^2 (d_2-4)}\! for d2 > 4
| skewness = \frac{(2 d_1 + d_2 - 2) \sqrt{8 (d_2-4){(d_2-6) \sqrt{d_1 (d_1 + d_2 -2)\!for d2 > 6
| kurtosis = see text
| entropy = \begin{align}
& \ln \Gamma{\left(\tfrac{d_1}{2} \right)}
+ \ln \Gamma{\left(\tfrac{d_2}{2} \right)}
- \ln \Gamma{\left
F-test
thumb|An F-test pdf with d1 and d2 = 10, at a significance level of 0.05. (Red shaded region indicates the critical region)
An F-test is a statistical test that compares variances. It is used to determine if the variances of two samples, or if the ratios of variances among multiple samples, are significantly different. The test calculates a statistic, represented by the random variable F, and checks if it follows an F-distribution. This check is valid if the null hypothesis is true and standard assumptions about the errors (ε) in the data hold.
confounding
class=skin-invert-image|thumb|upright=1.3|Whereas a mediator is a factor in the causal chain (above), a confounder is a spurious factor incorrectly implying causation (bottom)
Kruskal–Wallis one-way analysis of variance
non-parametric method for testing whether samples originate from the same distribution
Levene's test
Test in statistics
analysis of covariance
general linear model which blends ANOVA and regression
Friedman test
non-parametric statistical test
interaction
in statistics, the situation in which the simultaneous influence of two variables on a third is not additive
mixed model
statistical model containing both fixed effects and random effects
multilevel model
statistical model
Bartlett's test
statistical test
multivariate analysis of variance
procedure for comparing multivariate sample means
one-way analysis of variance
statistical test
Tukey's range test
statistical test for multiple comparisons
random effects model
type of statistical model
fixed effects model
statistical model that represents the observed quantities in terms of explanatory variables that are treated as if the quantities were non-random
least-squares spectral analysis
frequency-domain analysis method
Kaiser Meyer Olkin test
Mauchly's sphericity test
statistical test
Contrast
combination of averages whose coefficients add up to zero, or the difference between two means
generalized linear mixed model
Statistical model