We see that their components are nothing more than simple algebraic manipulations of the It may appear that the above equations are quite complicated, however upon inspection, Implicitly applying regression to the sample data. (Note that the limits of the summation, which are i to n,Īnd the summation indices on x and y have been omitted.) With n data points, the slope, y-intercept and correlation coefficient, r, To the data and determine these constants. It is not necessary for us to plot the data in order to If we expect a set of data to have a linear correlation, (ValuesĬlose to 1 indicate excellent linear reliability.))Įnter your data as we did in columns B and C. The linear relationship between the x and y values. Or R, the correlation coefficient gives us a measure of the reliability of Statistical texts show the correlation coefficient as " r", butĮxcel shows the coefficient as " R". Recall that the R-squared value is the square of the correlation coefficient. Trendline and display its slope, y-intercept Let's enter the above data into an Excel spread sheet, We can then find the slope, m, and y-intercept, b,įor the data, which are shown in the figure below. Of course, this relationship is governed by the familiar equation We can plot the data and draw a "best-fit" straight line through the data. There exists a linear relationship between the variables x and y, You may also wish to take a look at how we analyzed actual (See our Tutorial Page for more information about
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