the regression errors and regression residuals and where they lead to the concept of studentized residuals. In econometrics, "errors" are also called...

statistics, the residual sum of squares (RSS), also known as the sum of squared residuals (SSR) or the sum of squared estimate of errors (SSE), is the sum...

variances of the errors at these different input variable values are equal. The issue is the difference between errors and residuals in statistics, particularly...

least absolute errors (LAE), least absolute residuals (LAR), or least absolute values (LAV), is a statistical optimality criterion and a statistical optimization...

population of all possible observations, the residuals should belong to a Student's t-distribution. Studentized residuals are useful in making a statistical test...

appropriate rejection of a false null hypothesis. Type I errors can be thought of as errors of commission, in which the status quo is erroneously rejected...

attributed to such errors, they are "errors" in the sense in which that term is used in statistics; see errors and residuals in statistics. Every time a measurement...

(and are therefore always in reference to an estimate) and are called errors (or prediction errors) when computed out-of-sample (aka on the full set, referencing...

(see errors and residuals in statistics for more details). Although the MSE (as defined in this article) is not an unbiased estimator of the error variance...

error in medicine is used as a label for nearly all of the clinical incidents that harm patients. Medical errors are often described as human errors in...

autocorrelation at lag 1 in the residuals (prediction errors) from a regression analysis. It is named after James Durbin and Geoffrey Watson. The small sample...

"disturbance", "noise" or simply "error" (will be contrasted with "residual" later in the article; see errors and residuals in statistics). Note that to include...

of variability in the residuals for different levels of the explanatory variables suggests possible heteroscedasticity. Residuals against explanatory variables...

Linearity: Relationship between each predictor and outcome must be linear 2. Normality of residuals: Residuals should follow a normal distribution 3. Homoscedasticity:...

Similarly, statistical tests on the residuals can be conducted if the probability distribution of the residuals is known or assumed. We can derive the...

the case, the errors are said to be heteroskedastic, or to have heteroskedasticity, and this behaviour will be reflected in the residuals u ^ i {\textstyle...

In statistics, mean absolute error (MAE) is a measure of errors between paired observations expressing the same phenomenon. Examples of Y versus X include...

necessary to confirm suspicions of misconduct. Error analysis (linguistics) Error bar Errors and residuals in statistics Propagation of uncertainty Validated...

curve is often assumed to be that which minimizes the sum of squared residuals. This is the ordinary least squares (OLS) approach. However, in cases...

uncertainty (or propagation of error) is the effect of variables' uncertainties (or errors, more specifically random errors) on the uncertainty of a function...

The notation for standard error can be any one of SE, SEM (for standard error of measurement or mean), or SE. Standard errors provide simple measures of...

of residuals against time drift in the errors (data collected over time): run charts of the response and errors versus time independence of errors: lag...

E(e_{i}|X_{i})=0} The variance of the residuals e i {\displaystyle e_{i}} is constant across observations (homoscedasticity). The residuals e i {\displaystyle e_{i}}...

Clustered standard errors (or Liang-Zeger standard errors) are measurements that estimate the standard error of a regression parameter in settings where...

Error rate, meaning the frequency of errors, can have the following uses: Bayes error rate Bit error rate Per-comparison error rate Residual bit error...

of squared residuals (see also Errors and residuals) ε ^ i {\displaystyle {\widehat {\varepsilon }}_{i}} (differences between actual and predicted values...

amount of correlation between the residuals in the regression model. GLS is employed to improve statistical efficiency and reduce the risk of drawing erroneous...

language or grammar. Common examples include: errors and residuals in statistics, e.g. in linear regression the error term in numerical integration This set...

detected as containing errors, and will be discarded. The likelihood that a particular bit will be detected as erroneous is the bit error rate. The RBER characterizes...

models such as support vector regression with a polynomial kernel. If residuals have unequal variance, a weighted least squares estimator may be used...