find linear regression equation calculator

Most people think the name linear regression comes from a straight line relationship between the variables.

For example, if you wanted to generate a line of best fit for the association between height and shoe size, allowing you to predict shoe size on the basis of a person's height, then height would be your independent variable and shoe size your dependent variable). The most popular form of regression is linear regression, which is used to predict the value of one numeric (continuous) response variable based on one or more predictor variables (continuous or categorical). Now, here we need to find the value of the slope of the line, b, plotted in scatter plot and. The equation of linear regression is similar to the slope formula what we have learned before in earlier classes such as linear equations in two variables. To begin, you need to add paired data into the two text boxes immediately below (either one value per line or as a comma delimited list), with your independent variable in the X Values box and your dependent variable in the Y Values box. Linear regression shows the linear relationship between two variables. This calculator will determine the values of b and a for a set of data comprising two variables, and estimate the value of Y for any specified value of X. The line of best fit is described by the equation ŷ = bX + a, where b is the slope of the line and a is the intercept (i.e., the value of Y when X = 0). The only difference is that #r^2# is #r# times #r#, or in standard English, the value of the regression coefficient squared.This simple linear regression calculator uses the least squares method to find the line of best fit for a set of paired data, allowing you to estimate the value of a dependent variable ( Y) from a given independent variable ( X). Suppose the equation of the best-fitted line is given. For example, suppose a simple regression equation is given by y 7x - 3, then 7 is the coefficient, x is the predictor and -3 is the constant term. Once you know the values of m and b, you can calculate any point on the line by plugging the y- or x-value into that equation. The goal of linear regression is to find the equation of the straight line that best describes the relationship between two or more variables.

The equation of a straight line is y mx + b. This is basically the same concept as #r#, and they both show the closeness of a regression model/equation to the data which it tries to represent. The y-intercept of a line, often written as b, is the value of y at the point where the line crosses the y-axis.

If you have a graphing calculator, such as a TI- #84#, then your math teacher should be able to help you to find an #r# value for a regression (or you can just look it up on Just a note for the future: When you are trying to find #r#, a value may appear noted as #r^2#. The easy-to-use simple linear regression calculator gives you step-by-step solutions to the estimated regression equation, coefficient of determination and. So a value closer to #-1# or #1# for #r# would therefore correspond to a more reliable and accurate equation/model to represent the data. The closer the value for a regression equation/model is to #0#, the worse the model will be for showing a trend in the data.

#r#, or the regression coefficient, is a simple value that is used when finding the closeness of a regression equation to the actual data points which it is trying to show a correlation between. Estimate of Slope: Standard Error Slope: Regression Standard Error: t t -Statistic: Confidence Interval for : p p -value: p p.

Find linear regression equation calculator