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OECD Statistics. Definition: The set of procedures employed to improve coverage / classification / timing / valuation of the data or to conform to an accounting/recording basis or address data quality differences in compiling specific data sets.
Data adjustment. A location adjustment visits each entity (row or column or both) in turn, computes a value for that entity, and subtracts it from every data element in the entity. A scale adjustment divides each data element by a value.
The adjusted mean arises when statistical averages must be corrected to compensate for data imbalances and large variances. Outliers present in data sets will often be removed, as they have a large impact on the calculated means of small populations. An adjusted mean can be determined by removing these outlier figures.
The adjusted means will be a weighted average of the cell means with the males being given weight 100/150 and the females given weight 50/150. For omnivores the adjusted mean is 223.3 [= 230 (100/150) + 210 (50/150)], while for vegans it is 213.3 [= 220 (100/150) + 200 (50/150)].
Propensity score adjustment of effect estimates in observational studies of treatment is a common technique used to control for bias in treatment assignment. Regression adjustment is used most often and can be highly efficient, but it can lead to biased results when model assumptions are violated.
An accounting adjustment is a business transaction that has not yet been included in the accounting records of a business as of a specific date. Most transactions are eventually recorded through the decoration of (for example) a supplier invoice, a customer billing, or the receipt of cash.
1. When you “statistically adjust” something, what you are really doing is removing the effect of whatever you are adjusting for. Formally, the things you are adjusting for are called “covariance”, which is just the set of variables that (a) you did not experimentally control and (b) have in impact on the outcome.
”Controlling” for a variable means adding it to the model so its effect on your outcome variable(s) can be estimated and statistically isolated from the effect of the independent variable you're really interested in. We could also add other variables such as age, education level, and the like.
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