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Analyse-it provides a range of standard parametric and non-parametric procedures, including Descriptive statistics, ANOVA, ANCOVA, Mann–Whitney, Wilcoxon, chi-square, correlation, linear regression, logistic regression, polynomial regression and advanced model fitting, principal component analysis, and factor analysis.
Bayesian linear regression applies the framework of Bayesian statistics to linear regression. (See also Bayesian multivariate linear regression .) In particular, the regression coefficients β are assumed to be random variables with a specified prior distribution .
In linear regression, the model specification is that the dependent variable, is a linear combination of the parameters (but need not be linear in the independent variables). For example, in simple linear regression for modeling n {\displaystyle n} data points there is one independent variable: x i {\displaystyle x_{i}} , and two parameters, β ...
In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one [clarification needed] effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values ...
If there are other non-linear effects that have a correlation to the independent variable (such as cyclic influences), the use of least-squares estimation of the trend is not valid. Also, where the variations are significantly larger than the resulting straight line trend, the choice of start and end points can significantly change the result.
Confidence and prediction bands are often used as part of the graphical presentation of results of a regression analysis. Confidence bands are closely related to confidence intervals, which represent the uncertainty in an estimate of a single numerical value. "As confidence intervals, by construction, only refer to a single point, they are ...
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