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Heteroscedasticity
| Глоссарий по эконометрике (англо-русский) |
"mixed scatter." a scatterplot or residual plot shows heteroscedasticity if the scatter in vertical slices through the plot depends on where you take the slice. linear regression is not usually a good idea if the data are heteroscedastic.
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Гетероскедастичность, русский
Неоднородность дисперсии измеряемой величины на разных уровнях изучаемых признаков.
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Scatterplot, английский
A scatterplot is a way to visualize bivariate data. a scatterplot is a plot of pairs of measurements on a collection of "individuals" (which need not be people). for example, suppose we record the heights and weights of a group of 100 people. the scatterplot of those data would be 100 points. each point represents one person`s height and weight. in a scatterplot of weight against height, the x-coordinate of each point would be height of one person, the y-coordinate of that point would be the weight of the same person. in a scatterplot of height against weight, the x-coordinates would be the weights and the y-coordinates would be the heights.
Regression, английский
- Statistical technique used to evaluate relationships among variables (22).
- Регрессия
- 1. a stage where symptoms of a disease are disappearing and the person is getting better 2. (in psychiatry) the process of returning to a mental state which existed when the person was younger
- Regression commonly refers to the process of developing an empirical (data-driven) model to predict and/or explain one or more attributes in a database or set of data. it is most frequently associated with the simple linear model (y=mx+b) taught in most introductory statistics courses; the same ideas have been extended in many directions, including classification problems. when the emphasis is on hypothesis testing and simple models, the regression output is typically a few parameters that provide a direct linkage from the input variables to the predicted variables (or classification). in other situations the emphasis is on explaining as much of the variability in the output variables as is "reasonable" from the input variables. in this case, there are a number of "advanced" techniques, such as smoothing splines, decision trees, neural nets, and so forth, for which there are many "free" parameters. the meaning of any one of these parameters can be obscure. many data mining techniques are, at their core, variations on well-known regression techniques. see also: classification, clustering, decision trees, neural nets.
- The reappearance of a previously fixed problem.
- The statistical process of predicting one or more continuous variables, such as profit or loss, based on other attributes in the dataset.
- A mathematical technique used to explain and/or predict. the general form is y = a + bx + u, where y is the variable that we are trying to predict; x is the variable that we are using to predict y, a is the intercept; b is the slope, and u is the regression residual. the a and b are chosen in a way to minimize the squared sum of the residuals. the ability to fit or explain is measured by the r-square.
- A seaward retreat of a shoreline, generally expressed as a seaward
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Гетероскедастичность, русский
Неоднородность дисперсии измеряемой величины на разных уровнях изучаемых признаков.
Гессиан, русский
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