Search results
Results from the WOW.Com Content Network
In statistics, separation is a phenomenon associated with models for dichotomous or categorical outcomes, including logistic and probit regression.Separation occurs if the predictor (or a linear combination of some subset of the predictors) is associated with only one outcome value when the predictor range is split at a certain value.
A riffle box is a box containing a number (between 3 and 12) of "chutes" - slotted paths through which particles of the sample may slide. The sample is dropped into the top, and the box produces two equally divided subsamples. Riffle boxes are commonly used in mining to reduce the size of crushed rock samples prior to assaying.
Thermodynamic data is usually presented as a table or chart of function values for one mole of a substance (or in the case of the steam tables, one kg). A thermodynamic datafile is a set of equation parameters from which the numerical data values can be calculated.
In statistics, the jackknife (jackknife cross-validation) is a cross-validation technique and, therefore, a form of resampling. It is especially useful for bias and variance estimation. The jackknife pre-dates other common resampling methods such as the bootstrap .
Distributional data analysis is a branch of nonparametric statistics that is related to functional data analysis.It is concerned with random objects that are probability distributions, i.e., the statistical analysis of samples of random distributions where each atom of a sample is a distribution.
In this technique, the response of the sample is measured and recorded, for example, using an electrode selective for the analyte. Then, a small volume of standard solution is added and the response is measured again. Ideally, the standard addition should increase the analyte concentration by a factor of 1.5 to 3, and several additions should ...
Sample ratio mismatches can be detected using a chi-squared test. [3] Using methods to detect SRM can help non-experts avoid making discussions using biased data. [4] If the sample size is large enough, even a small discrepancy between the observed and expected group sizes can invalidate the results of an experiment. [5] [6]
We write this as n − 1, where n is the number of data points. Scaling (also known as normalizing) means adjusting the sum of squares so that it does not grow as the size of the data collection grows. This is important when we want to compare samples of different sizes, such as a sample of 100 people compared to a sample of 20 people.