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Linear Regression is an approach to modeling the straightline relationship between variables by using a linear equation to observed data. It is identical to all forms of regression analysis, focuses on the conditional probability distribution of y given X, rather than on the joint probability distribution of y and X, which is the domain of multivariate analysis. Among two variables scalar variable Y is considered to be explanatory variable and other one or more variables denoted X is considered to be dependent variable
A linear regression line has a formula of Y = A + BX, where X is the explanatory 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 functions are used to model the data in linear regression and the unknown model parameters are estimated from the data. Such method of modeling data is known as linear models. Generally, linear regression assign to a model in which the conditional mean of y given the value of X is an affine function of X. Linear regression rarely could refer to a model in which the median, or some other quantize of the conditional distribution of y given X is expressed as a linear function of X
To obtain Linear Regression calculations like Slope of the Line B, Explanatory Mean Y, Dependant Variable Mean X, Line Intercept A, Regression Equation and Number of Inputs this online calculator is an essential tool to analyze the relationship between the given two sets of data
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