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You should now see that the Excel graphing R 2 = 0.9488, which is agrees with the graph. From the graph, we see that R 2 = 0.9488.įrom our linear regression analysis, we find that r = 0.9741, therefore More familiar trendline from the graph in the first section namelyĮxcel can be used to display the R-squared value. Using linear regression techniques are identical to the values of the It is plain to see that the slope and y-intercept values that were calculated Linear regression with built-in functions. Y-intercept and correlation coefficient are highlighted in yellow. Given in the previous section to calculateĬorrelation coefficient (r) of the data. Finally, use the above components and the linear regression equations.=COUNT(B3:B8) and is shown in the formula bar in the screen shot below. The syntax for COUNT() in this example is: Now use Excel to count the number of data points, n.Additionally, the term xy is the product of x and Three columns that will be used to determine the quantities xy, We can expand our spread sheet to include these components. 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