y-intercept, b: =INTERCEPT(known_y's, known_x's).In the statistics section of this tutorial. The slope, y-intercept, correlation coefficient, and R-squared valuesĪre SLOPE(), INTERCEPT(), CORREL() and RSQ(), and are also covered Routine uses linear regression to calculate the slope, y-intercept and correlationĮxcel has three built-in functions that allow for a third method for determining 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.
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