The phrase "aim of regression" is correct and usable in written English.
It can be used in contexts related to statistics, data analysis, or research where you are discussing the purpose or objective of a regression analysis.
Example: "The aim of regression in this study is to identify the relationship between the independent and dependent variables."
Alternatives: "objective of regression" or "purpose of regression".
Similar to the formulation of linear regression, the aim of quantile regression is to estimate the parameter vector, θ, of a quantile function y = f x, θ) given a data series.
The K-fold cross-validation method is commonly used when the aim of the regression model is prediction [ 19].
The aim of multivariate regressions is to study about the correlation between several independent or predictor variables and a dependent variable to get the best-fit equation.
The aim of the MLM regression analysis is to describe the effect of the checklist intervention on primary outcome; the safety culture factors of the Hospital SOPS (dependent variables) with intervention and control groups at baseline and post-intervention surveys (independent variables).
The aim of the meta-regression was to include clinical outcomes and all dietary fatty acid changes in a univariate and multivariate model.
Test suite reduction techniques aim to decrease the cost of regression testing by removing the redundant test cases from the test suite and then obtaining a representative set of test cases that still yield a high level of code coverage.
The aim of the penalised linear regression methodology was to determine those miRNA which most strongly predict (positively or negatively), the gene expression summary score for each signature.
Intralesional IL-2 treatment was performed with the aim of achieving a complete regression of all cutaneous and soft-tissue metastases accessible from the skin.
With the aim of correcting for possible regression to the mean, the baseline value of the particular outcome variable will be added to the model.
Ridge regression penalizes the sum of squares of w with the aim of controlling the arbitrary scale of regression coefficients, which makes it possible to alleviate over-fitting on the training dataset.
