Agricultural and Meteorological Software

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In statistics, linear regression is a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). Linear regression is used for finding linear relationship between target and one or more predictors. Simple linear regression is useful for finding relationship between two continuous variables.

A linear regression line has an equation of the form Y = a + bX, where X is the independent variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0). Linear regression analysis is the most widely used of all statistical techniques.

In linear regression, the model specification is that the dependent variable, y is a linear combination of the parameters (but need not be linear in the independent variables). As the linear regression has a closed form solution, the regression coefficients can be efficiently computed using the Regress method of this class.

References: In linear regression, the model specification is that the dependent variable, y is a linear combination of the parameters (but need not be linear in the independent variables). As the linear regression has a closed form solution, the regression coefficients can be efficiently computed using the Regress method of this class.

We used Accord.Statistics for this calculator

Paste Y here. Each sample in one line. (independent)

Paste X here. Each sample in one line.(dependent)