Search results
Results from the WOW.Com Content Network
A normal quantile plot for a simulated set of test statistics that have been standardized to be Z-scores under the null hypothesis. The departure of the upper tail of the distribution from the expected trend along the diagonal is due to the presence of substantially more large test statistic values than would be expected if all null hypotheses were true.
In statistics, the coefficient of multiple correlation is a measure of how well a given variable can be predicted using a linear function of a set of other variables. It is the correlation between the variable's values and the best predictions that can be computed linearly from the predictive variables. [1]
The image of a function f(x 1, x 2, …, x n) is the set of all values of f when the n-tuple (x 1, x 2, …, x n) runs in the whole domain of f.For a continuous (see below for a definition) real-valued function which has a connected domain, the image is either an interval or a single value.
Formally, a multivariate random variable is a column vector = (, …,) (or its transpose, which is a row vector) whose components are random variables on the probability space (,,), where is the sample space, is the sigma-algebra (the collection of all events), and is the probability measure (a function returning each event's probability).
Principles and Procedures of Statistics with Special Reference to the Biological Sciences. Authors: Steel, R.G.D, and Torrie, J. H. Publication data: McGraw Hill (1960) 481 pages Description: Excellent introductory text for analysis of variance (one-way, multi-way, factorial, split-plot, and unbalanced designs). Also analysis of co-variance ...
The new multiple range test proposed by Duncan makes use of special protection levels based upon degrees of freedom. Let γ 2 , α = 1 − α {\displaystyle \gamma _{2,\alpha }={1-\alpha }} be the protection level for testing the significance of a difference between two means; that is, the probability that a significant difference between two ...
Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated. [1] It has been used in many fields including econometrics, chemistry, and engineering. [2]
Here the dependent variable (and variable of most interest) was the annual mean sea level at a given location for which a series of yearly values were available. The primary independent variable was "time". Use was made of a "covariate" consisting of yearly values of annual mean atmospheric pressure at sea level.