Related Experiment Video
Updated: Jul 26, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Implementation of linear and quadratic discriminant analysis incorporating costs of misclassification
Abstract:
Discriminant analysis plays an important role in biological and medical research. In practice, standard linear and quadratic methods are often applied which assume equal costs of misclassification. However, there can be situations where misclassifications between certain groups may be more serious than between other groups. Such considerations can be taken into account by using classification methods which incorporate misclassification costs. The widely applied statistical packages BMDP, SAS, and SPSS do not offer the possibility of using unequal misclassification costs for discriminant analysis with more than two groups. In this paper a menu-driven, user-friendly PC program written in Borland Pascal is introduced which performs linear and quadratic discriminant analysis for g > or = 2 groups allowing for the incorporation of misclassification costs.
Related Concept Videos
Testing a Claim about Standard Deviation
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
Regression Analysis
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Residuals and Least-Squares Property
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Calibration Curves: Linear Least Squares
For data that follow a straight line, the standard method for fitting is the linear...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Quadratic Models

