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Control for a variable
| Glossary of Statistical Terms |
To control for a variable is to try to separate its effect from the treatment effect, so it will not confound with the treatment. there are many methods that try to control for variables. some are based on matching individuals between treatment and control; others use assumptions about the nature of the effects of the variables to try to model the effect mathematically, for example, using regression.
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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
Individual, английский
Физическое лицо
Mathematical, английский
- Analyzer, numerical integrator and computer эвм «маньяк»
- Математический
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Independent, independence, английский
Two events a and b are (statistically) independent if the chance that they both happen simultaneously is the product of the chances that each occurs individually; i.e., if p(ab) = p(a)p(b). this is essentially equivalent to saying that learning that one event occurs does not give any information about whether the other event occurred too: the conditional probability of a given b is the same as the unconditional probability of a, i.e., p(a|b) = p(a). two random variables x and y are independent if all events they determine are independent, for example, if the event {a < x ≤ b} is independent of the event {c < y ≤ d} for all choices of a, b, c, and d. a collection of more than two random variables is independent if for every proper subset of the variables, every event determined by that subset of the variables is independent of every event determined by the variables in the complement of the subset. for example, the three random variables x, y, and z are independent if every event determined by x is independent of every event determined by y and every event determined by x is independent of every event determined by y and z and every event determined by y is independent of every event determined by x and z and every event determined by z is independent of every event determined by x and y.
Type i and type ii errors, английский
- These refer to hypothesis testing. a type i error occurs when the null hypothesis is rejected erroneously when it is in fact true. a type ii error occurs if the null hypothesis is not rejected when it is in fact false. see also significance level and power.
- Ошибки первого рода и ошибки второго рода. см. error (ошибка).
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