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The coefficient of multiple correlation is known as the square root of the coefficient of determination, but under the particular assumptions that an intercept is included and that the best possible linear predictors are used, whereas the coefficient of determination is defined for more general cases, including those of nonlinear prediction and those in which the predicted values have not been ...
MedCalc includes basic parametric and non-parametric statistical procedures and graphs such as descriptive statistics, ANOVA, Mann–Whitney test, Wilcoxon test, χ 2 test, correlation, linear as well as non-linear regression, logistic regression, and multivariate statistics. [5]
jamovi (stylised in all lower-case) is a free and open-source computer program for data analysis and performing statistical tests. The core developers of jamovi are Jonathon Love, Damian Dropmann, and Ravi Selker, who were developers for the JASP project.
In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the outcome or response variable, or a label in machine learning parlance) and one or more error-free independent variables (often called regressors, predictors, covariates, explanatory ...
All of the packages gave exactly the same results for correlation and regression. The free software packages also gave the same regression results as did excel. One of the main differences among the packages was how they handled missing data. With the example data sets used in the review, and for the package versions available in November 2006 ...
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).
In statistics, Bayesian multivariate linear regression is a Bayesian approach to multivariate linear regression, i.e. linear regression where the predicted outcome is a vector of correlated random variables rather than a single scalar random variable.
An example is polynomial regression, which uses a linear predictor function to fit an arbitrary degree polynomial relationship (up to a given order) between two sets of data points (i.e. a single real-valued explanatory variable and a related real-valued dependent variable), by adding multiple explanatory variables corresponding to various ...