18738/what-is-linear-regression

Linear regression is a statistical technique to predict the value of y based on some value input value x, after recognizing the relationship among the variables y and x.

General formula for a linear regression is y = m_{1}x_{1 }+ m_{2}x_{2}+m_{3}x_{3}+....+c. Where in x_{1}, x_{2}, x_{3},... are the input variables.

Linear regression is a basic and commonly used type of predictive analysis. The overall idea of regression is to examine two things: (1) does a set of predictor variables do a good job in predicting an outcome (dependent) variable? (2) Which variables in particular are significant predictors of the outcome variable, and in what way do they–indicated by the magnitude and sign of the beta estimates–impact the outcome variable? These regression estimates are used to explain the relationship between one dependent variable and one or more independent variables. The simplest form of the regression equation with one dependent and one independent variable is defined by the formula y = c + b*x, where y = estimated dependent variable score, c = constant, b = regression coefficient, and x = score on the independent variable.

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