You could have determined this from looking at the least squares line plotted over the scatterplot, but the Correlation Coefficient gives you scientific proof! Conclusion Our value is close to positive 1, which means that the data is highly correlated, and positive. How well the data fits the Least Squares Line is the Correlation Coefficient. We are not 100 percent accurate, but with more data, we would likely improve our accuracy. Using just a rudimentary Least Squares Line drawn by hand through the data, we could predict that a hunting party of 4 chimpanzees is going to be around 52% successful. Notice how the line I drew through the data does not fit it perfectly, but the points approximate a linear pattern? The line I drew through the data is the Least Squares Line, and is used to predict y values for given x values. (Busse, 1978) I am going to grab a few data points from Busse to use for this article, and plot the data using a seaborn scatterplot. The relationship between Chimpanzee hunting party size and percentage of successful hunts is well documented. Let’s make up some data to use as an example. Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data ( Linear Regression, n.d.). From that scatterplot, we would like to determine, what is the line of best fit that describes the linear qualities of the data, and how well does the line fit the cluster of points? It will likely be in the form of a cluster of data points on a scatterplot. In the real world, our data will not be perfectly linear.

To predict y, we would just plug in the given values of x and b.

Now, if the data were perfectly linear, we could simply calculate the slope intercept form of the line in terms y = mx+ b. We want to predict the value of y for a given value of x. In our case, y is the dependent variable, and x is the independent variable. Linear regression is a method for predicting y from x.

Regress x on y stata