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A common source of confusion occurs when failing to distinguish clearly between: the standard deviation of the population ( σ {\displaystyle \sigma } ), the standard deviation of the sample ( σ x {\displaystyle \sigma _ {x}} ), the standard deviation of the mean itself ( σ x ¯ {\displaystyle \sigma _ {\bar {x}}} , which is the standard error), and the estimator of the standard deviation of ...
The topic of heteroskedasticity-consistent (HC) standard errors arises in statistics and econometrics in the context of linear regression and time series analysis. These are also known as heteroskedasticity-robust standard errors (or simply robust standard errors), Eicker–Huber–White standard errors (also Huber–White standard errors or ...
Clustered standard errors are often useful when treatment is assigned at the level of a cluster instead of at the individual level. For example, suppose that an educational researcher wants to discover whether a new teaching technique improves student test scores.
Generalized estimating equation. In statistics, a generalized estimating equation (GEE) is used to estimate the parameters of a generalized linear model with a possible unmeasured correlation between observations from different timepoints. [1][2] Although some believe that GEEs are robust in everything, even with the wrong choice of working ...
In statistics, the multiple comparisons, multiplicity or multiple testing problem occurs when one considers a set of statistical inferences simultaneously [1] or estimates a subset of parameters selected based on the observed values. [2] The larger the number of inferences made, the more likely erroneous inferences become.
In statistics, a standard normal table, also called the unit normal table or Z table, [1] is a mathematical table for the values of Φ, the cumulative distribution function of the normal distribution.
The statistical errors, on the other hand, are independent, and their sum within the random sample is almost surely not zero. One can standardize statistical errors (especially of a normal distribution) in a z-score (or "standard score"), and standardize residuals in a t -statistic, or more generally studentized residuals.
The mean absolute percentage error (MAPE), also known as mean absolute percentage deviation (MAPD), is a measure of prediction accuracy of a forecasting method in statistics.