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  2. Principal component regression - Wikipedia

    en.wikipedia.org/wiki/Principal_component_regression

    In statistics, principal component regression (PCR) is a regression analysis technique that is based on principal component analysis (PCA). PCR is a form of reduced rank regression . [ 1 ] More specifically, PCR is used for estimating the unknown regression coefficients in a standard linear regression model .

  3. Cochran's C test - Wikipedia

    en.wikipedia.org/wiki/Cochran's_C_test

    Cochran's test, [1] named after William G. Cochran, is a one-sided upper limit variance outlier statistical test .The C test is used to decide if a single estimate of a variance (or a standard deviation) is significantly larger than a group of variances (or standard deviations) with which the single estimate is supposed to be comparable.

  4. L1-norm principal component analysis - Wikipedia

    en.wikipedia.org/wiki/L1-norm_principal...

    L1-norm principal component analysis (L1-PCA) is a general method for multivariate data analysis. [1] L1-PCA is often preferred over standard L2-norm principal component analysis (PCA) when the analyzed data may contain outliers (faulty values or corruptions), as it is believed to be robust .

  5. Parallel analysis - Wikipedia

    en.wikipedia.org/wiki/Parallel_analysis

    Parallel analysis, also known as Horn's parallel analysis, is a statistical method used to determine the number of components to keep in a principal component analysis or factors to keep in an exploratory factor analysis. It is named after psychologist John L. Horn, who created the method, publishing it in the journal Psychometrika in 1965. [1]

  6. Principal stratification - Wikipedia

    en.wikipedia.org/wiki/Principal_stratification

    An example of principal stratification is where there is attrition in a randomized controlled trial. With a binary post-treatment covariate (e.g. attrition) and a binary treatment (e.g. "treatment" and "control") there are four possible strata in which subjects could be:

  7. Blocking (statistics) - Wikipedia

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

    In the first example provided above, the sex of the patient would be a nuisance variable. For example, consider if the drug was a diet pill and the researchers wanted to test the effect of the diet pills on weight loss. The explanatory variable is the diet pill and the response variable is the amount of weight loss.

  8. Credit conversion factor - Wikipedia

    en.wikipedia.org/wiki/Credit_conversion_factor

    The key variables for (credit) risk assessment are the probability of default (PD), the loss given default (LGD) and the exposure at default (EAD).The credit conversion factor calculates the amount of a free credit line and other off-balance-sheet transactions (with the exception of derivatives) to an EAD amount [2] and is an integral part in the European banking regulation since the Basel II ...

  9. Engineering statistics - Wikipedia

    en.wikipedia.org/wiki/Engineering_statistics

    Engineering statistics involves data concerning manufacturing processes such as: component dimensions, tolerances, type of material, and fabrication process control. There are many methods used in engineering analysis and they are often displayed as histograms to give a visual of the data as opposed to being just numerical.