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Unit-weighted Regression
In statistics, unit-weighted regression is a simplified and robust statistics, robust version (Howard Wainer, Wainer & Thissen, 1976) of multiple regression analysis where only the intercept term is estimated. That is, it fits a model :\hat = \hat(\mathbf) = \hat + \sum_i x_i where each of the x_i are binary data, binary variables, perhaps multiplied with an arbitrary weight. Contrast this with the more common multiple regression model, where each predictor has its own estimated coefficient: :\hat = \hat(\mathbf) = \hat + \sum_i \hat_i x_i In the social sciences, unit-weighted regression is sometimes used for binary statistical classification, classification, i.e. to binary classification, predict a yes-no answer where \hat < 0 indicates "no", "yes". It is easier to interpret than multiple linear regression (known as linear discriminant analysis in the classification case). Unit weights Unit-weighted regression is a method of ...[...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistics
Statistics (from German language, German: ', "description of a State (polity), state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such as "all people living in a country" or "every atom composing a crystal". Statistics deals with every aspect of data, including the planning of data collection in terms of the design of statistical survey, surveys and experimental design, experiments. When census data (comprising every member of the target population) cannot be collected, statisticians collect data by developing specific experiment designs and survey sample (statistics), samples. Representative sampling assures that inferences and conclusions can reasonably extend from the sample ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
