The correct answer is: B. “Explained variance” is routinely used in principal component analysis.
Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables.
The principal components are constructed to maximize the variance of the projected data. This means that the first principal component accounts for as much of the variability in the data as possible, and the second principal component accounts for as much of the remaining variability as possible, and so on.
The explained variance is the proportion of the total variance in the data that is explained by each principal component. The first principal component always has the highest explained variance, and the remaining principal components have decreasing explained variances.
In PCA, the explained variance is not routinely used. Instead, the principal components are used to reduce the dimensionality of the data. This can be done by projecting the data onto the first few principal components, which will capture most of the variability in the data.
The fraction of variance unexplained is an established concept in the context of linear regression. It is the proportion of the total variance in the response variable that is not explained by the model. The fraction of variance unexplained is calculated as 1 – R^2, where R^2 is the coefficient of determination.
The general linear model extends simple linear regression (SLR) by adding terms linearly into the model. In SLR, the response variable is modeled as a linear function of a single predictor variable. In the general linear model, the response variable is modeled as a linear function of multiple predictor variables.
The general linear model is a powerful tool for modeling relationships between variables. It is used in a wide variety of fields, including statistics, economics, and biology.