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Cochran's test is a non-parametric statistical test to verify whether k treatments have identical effects in the analysis of two-way randomized block designs where the response variable is binary. [ 1 ] [ 2 ] [ 3 ] It is named after William Gemmell Cochran .
Failure to compensate for multiple comparisons can have important real-world consequences, as illustrated by the following examples: Suppose the treatment is a new way of teaching writing to students, and the control is the standard way of teaching writing.
Rule 110 - most questions involving "can property X appear later" are undecidable. The problem of determining whether a quantum mechanical system has a spectral gap. [8] [9] Finding the capacity of an information-stable finite state machine channel. [10] In network coding, determining whether a network is solvable. [11] [12]
Example of direct replication and conceptual replication. There are two main types of replication in statistics. First, there is a type called “exact replication” (also called "direct replication"), which involves repeating the study as closely as possible to the original to see whether the original results can be precisely reproduced. [3]
There are two different types of questions that survey researchers use when writing a questionnaire: free-response questions and closed questions. [26] Free-response questions are open-ended, whereas closed questions are usually multiple-choice. [26] Free-response questions are beneficial because they allow the responder greater flexibility ...
In statistics, a central composite design is an experimental design, useful in response surface methodology, for building a second order (quadratic) model for the response variable without needing to use a complete three-level factorial experiment.
See Michaelis–Menten kinetics for details . In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables.
Certain types of problems involving multivariate data, for example simple linear regression and multiple regression, are not usually considered to be special cases of multivariate statistics because the analysis is dealt with by considering the (univariate) conditional distribution of a single outcome variable given the other variables.
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