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  2. Replacement value - Wikipedia

    en.wikipedia.org/wiki/Replacement_value

    The term replacement cost or replacement value refers to the amount that an entity would have to pay to replace an asset at the present time, according to its current worth. [1] In the insurance industry, "replacement cost" or "replacement cost value" is one of several methods of determining the value of an insured item. Replacement cost is the ...

  3. Imputation (statistics) - Wikipedia

    en.wikipedia.org/wiki/Imputation_(statistics)

    That is to say, when one or more values are missing for a case, most statistical packages default to discarding any case that has a missing value, which may introduce bias or affect the representativeness of the results. Imputation preserves all cases by replacing missing data with an estimated value based on other available information.

  4. Missing data - Wikipedia

    en.wikipedia.org/wiki/Missing_data

    Values in a data set are missing completely at random (MCAR) if the events that lead to any particular data-item being missing are independent both of observable variables and of unobservable parameters of interest, and occur entirely at random. [5] When data are MCAR, the analysis performed on the data is unbiased; however, data are rarely MCAR.

  5. Listwise deletion - Wikipedia

    en.wikipedia.org/wiki/Listwise_deletion

    Listwise deletion is also problematic when the reason for missing data may not be random (i.e., questions in questionnaires aiming to extract sensitive information. [3] Due to the method, much of the subjects' data will be excluded from analysis, leaving a bias in data findings.

  6. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]

  7. Completeness (statistics) - Wikipedia

    en.wikipedia.org/wiki/Completeness_(statistics)

    Had the parameter space been finite and with a number of elements less than or equal to n, it might be possible to solve the linear equations in g(t) obtained by substituting the values of r and get solutions different from 0. For example, if n = 1 and the parameter space is {0.5}, a single observation and a single parameter value, T is not ...

  8. Vacuous truth - Wikipedia

    en.wikipedia.org/wiki/Vacuous_truth

    These examples, one from mathematics and one from natural language, illustrate the concept of vacuous truths: "For any integer x, if x > 5 then x > 3." [11] – This statement is true non-vacuously (since some integers are indeed greater than 5), but some of its implications are only vacuously true: for example, when x is the integer 2, the statement implies the vacuous truth that "if 2 > 5 ...

  9. SAML metadata - Wikipedia

    en.wikipedia.org/wiki/SAML_Metadata

    Comparing the value of the creationInstant attribute to the value of the validUntil attribute, we see that the metadata is valid for two weeks. The <mdattr:EntityAttributes> extension element [CS 2] includes a single entity attribute. The entity attribute claims that the entity is "self-certified," a presumably desirable quality.