Linear Regression definition

Linear Regression means a mathematical procedure for finding the best fitting line to a given set of data-points by minimizing the difference between the actual data points and the regressed data points shown on the line.

Linear Regression means the determination, in analytical chemistry, of the best linear equation for calibration data to generate a calibration curve. The concentrate of an analyte in a sample can then be determined by comparing a measurement of the unknown to the calibration curve. A linear regression uses the following equation: y = mx + b; where m = slope, b = intercept.

Linear Regression means the determination, in analytical chemistry, of the best linear equation for calibration data to generate a calibration curve. The concentrate of an analyte in a sample can then be determined by

Examples of Linear Regression in a sentence

• Ran, F.Y. Zhang and M.H. Xu 2623MapReduce-Based Parallel Linear Regression for Face RecognitionL.S. Zhang, H.Y. Liu and D.J. Lei 2628A Research of MM-SVM Technique for Fault DiagnosisH.

• Linear Regression and correlation analysis; Numerical Methods: Matrix inversion, numerical solutions of nonlinear algebraic equations, iterative methods for solving differential equations, numerical integration.

• Looking at each group separately, confidence intervals indicate that the treatment had no significant effect on part-time unemployment benefits take-up among the four least affected groups, but a significantly positive effect among the most impacted group, which corresponds to the top 5% (p-value = 0.038 with Linear Regression proxy).

• Discriminant functions and regression functions, Linear Regression with Least Square Error Criterion, Logistic Regression for Classification Tasks, Fisher's Linear Discriminant and Thresholding for Classification, Minimum Description Length Principle.

• Multinomial Logistic Regression (MLR) and Linear Regression (LR) formed most part of the analysis of the various processes of revenue sharing implementation and livelihood improvement of people around Bwindi.

• Relative Importance for Linear Regression in R: The Package relaimpo.

• The purpose of using the Multiple Linear Regression method is to describe the relationship and simultaneously help us predict the extent of dependent when we foresee the value of independent variables.

• The rational for choosing Ordinary Least Square (OLS) is that, if the Classical Linear Regression Model (CLRM) assumptions hold true, then the estimators determined by OLS will have a number of desirable properties, and are known as Best Linear Unbiased Estimators (Brooks, 2008).

• For this task, a Linear Regression Model is trained based on a series of features derived from a standard segmentation model’s output probabilities.

• Variable coefficients are used for regression-based estimators (Logistic Regression and Linear Regression) and Gini importance is used for tree-based estimators (Random Forest and XGBoost).