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The standard error of a statistic is the standard deviation of its sampling distribution or an estimate of that standard deviation. It is used to calculate confidence ...
Learn the difference between errors and residuals in statistics and regression analysis, and how to calculate and interpret them. Find out the probability distributions, properties and applications of errors and residuals in various models.
Error bars indicate the error or uncertainty in a reported measurement on graphs. They can be used to compare, test, or fit data, but they do not show statistical ...
Learn how variables' uncertainties affect the uncertainty of a function based on them. Find formulas, examples, and caveats for linear and non-linear combinations, reciprocal and shifted reciprocal functions, and ratios.
Linear errors-in-variables models were studied first, probably because linear models were so widely used and they are easier than non-linear ones. Unlike standard least squares regression (OLS), extending errors in variables regression (EiV) from the simple to the multivariable case is not straightforward, unless one treats all variables in the same way i.e. assume equal reliability.
Learn about the linear regression model with a single explanatory variable, its formulation, computation, interpretation and applications. Find out how to estimate the slope, intercept and correlation of the regression line using least-squares method and covariance matrix.
Learn how to estimate the variance of regression coefficients when the errors have different variances across observations. Compare different methods, such as White's, HC0-HC3, and bootstrap, and see their applications and software implementations.
A Student's t-test is a statistical test to compare the means of two groups and see if they are significantly different. It is based on the t-distribution, which depends on the sample size and the variance of the populations. Learn about the history, types and uses of the t-test.