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Omitted-Variable Bias
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Details
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. In statistics, omitted-variable bias (OVB) is the bias that appears in estimates of parameters in a regression analysis when the assumed specification is incorrect, in that it omits an independent variable (possibly non-delineated) that should be in the model.In statistics, linear regression includes any approach to modeling the relationship between a scalar variable y and one or more variables denoted X, such that the model depends linearly on the unknown parameters to be estimated from the data. Such a model is called a linear model . Most commonly, linear regression refers to a model in which the conditional mean of y given the value of X is an affine function of X. Less commonly, linear regression could refer to a model in which the median, or some other quantile of the conditional distribution of y given X is expressed as a linear function of X.
Weitere Informationen
- Allgemeine Informationen
- GTIN 09786131299735
- Editor Lambert M. Surhone, Mariam T. Tennoe, Susan F. Henssonow
- Größe H220mm x B220mm
- EAN 9786131299735
- Format Fachbuch
- Titel Omitted-Variable Bias
- Herausgeber Betascript Publishing
- Anzahl Seiten 76
- Genre Mathematik
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