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Cohen's kappa measures the agreement between two raters who each classify N items into C mutually exclusive categories. The definition of is =, where p o is the relative observed agreement among raters, and p e is the hypothetical probability of chance agreement, using the observed data to calculate the probabilities of each observer randomly selecting each category.
In statistics, an effect size is a value measuring the strength of the relationship between two variables in a population, or a sample-based estimate of that quantity. It can refer to the value of a statistic calculated from a sample of data, the value of one parameter for a hypothetical population, or to the equation that operationalizes how statistics or parameters lead to the effect size ...
Kappa can only achieve very high values when both agreement is good and the rate of the target condition is near 50% (because it includes the base rate in the calculation of joint probabilities). Several authorities have offered "rules of thumb" for interpreting the level of agreement, many of which agree in the gist even though the words are ...
the standardized regression coefficient for predictor or independent variables in linear regression (unstandardized regression coefficients are represented with the lower-case Latin b, but are often called "betas" as well) the ratio of collector current to base current in a bipolar junction transistor (BJT) in electronics (current gain)
The Asymmetric Laplace distribution is commonly used with an alternative parameterization for performing quantile regression in a Bayesian inference context. [4] Under this approach, the κ {\displaystyle \kappa } parameter describing asymmetry is replaced with a p {\displaystyle p} parameter indicating the percentile or quantile desired.
Regression models predict a value of the Y variable given known values of the X variables. Prediction within the range of values in the dataset used for model-fitting is known informally as interpolation. Prediction outside this range of the data is known as extrapolation. Performing extrapolation relies strongly on the regression assumptions.
Ordinary least squares regression of Okun's law.Since the regression line does not miss any of the points by very much, the R 2 of the regression is relatively high.. In statistics, the coefficient of determination, denoted R 2 or r 2 and pronounced "R squared", is the proportion of the variation in the dependent variable that is predictable from the independent variable(s).
where Y ij is the i th observation in the j th group, μ is an unobserved overall mean, α j is an unobserved random effect shared by all values in group j, and ε ij is an unobserved noise term. [5] For the model to be identified, the α j and ε ij are assumed to have expected value