This means that a 1 change in the X variable (the temperature) causes a -19.622. Here, the value for b is -19.622 and so is our slope. And the linear regression equation for our example turned out as follows: Y 612.77 19.622x. In the above equation, the slope is represented by b. This means that a student with a high school GPA of, say, 3 would be predicted to have a university GPA of 0.675 3 + 1.097 3.12. Remember the linear regression equation Y a + bx. 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). The respective linear regression equation is: University GPA 0.675 (High School GPA) + 1.097.
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. This linear regression calculator uses the least squares method to find the line of best fit for a set of paired data. X is the independent (explanatory) variable.
It specifically helps determine how much a dependent variable (Y) is affected by one or more independent variables (X), where: Y is the dependent variable. 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). The regression formula in statistics is a method to estimate or calculate the relation between two or more variables. 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).