For example, if you wanted to generate a line of best fit for the association between height, weight and shoe size, allowing you to predict shoe size on the basis of a person's height and weight, then height and weight would be your independent variables ( X 1 and X 1) and shoe size your dependent variable ( Y). To begin, you need to add data into the three text boxes immediately below (either one value per line or as a comma delimited list), with your independent variables in the two X Values boxes and your dependent variable in the Y Values box. This calculator will determine the values of b 1, b 2 and a for a set of data comprising three variables, and estimate the value of Y for any specified values of X 1 and X 2. Example: The house price (Dependent variable Y) depends on the various Independent variables (X) like locality, number of bed rooms, number of bathrooms, age of. See Example 5.4.1 for details of the unweighted linear regression and Example 5.4.4 for details of the weighted linear regression. This provides a powerful tool to model bivariate data (i.e., data involving two variables.) Regression allows us to write a linear equation that models the relationship between the independent variable ( X) and the dependent variable ( Y) which we can use to predict the value of Y. The line of best fit is described by the equation ลท = b 1X 1 + b 2X 2 + a, where b 1 and b 2 are coefficients that define the slope of the line and a is the intercept (i.e., the value of Y when X = 0). A form of regression analysis that portrays the correlation of the independent and dependent variables as a non-linear equation. Simple linear regression is a method used to fit a line to data. This simple multiple linear regression calculator uses the least squares method to find the line of best fit for data comprising two independent X values and one dependent Y value, allowing you to estimate the value of a dependent variable ( Y) from two given independent (or explanatory) variables ( X 1 and X 2).
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