Linear regression and logistic regression are two entirely different math techniques that are often confused because their names are similar, and when illustrated in graphs, the two graphs appear very similar.

The graph below illustrates logistic regression. There are two numeric predictor variables, X1 and X2. The goal is to predict the class (0 or 1) for a pair of X1, X2 values. Each pair is plotted as a red dot or a blue dot. Logistic regression finds a line that separates the two classes. In this example, logistic regression might find a horizontal line at X2 = 3.5. Notice no straight line can get all the red dots on one side and all the blue dots on the other. The best you could ever do is get 10 out of 12 classifications correct.

The next graph illustrates linear regression. There is one numeric predictor variable, X, and one numeric value to predict, Y. Linear regression finds the equation of a line that predicts Y from X. Put another way, linear regression finds the best line through the (X, Y) data points.

Notice that you can only make a simple graph for logistic egression when there are two predictor variables, and only make a simple graph for linear regression when there is one predictor variable.

To reiterate, when the simplest examples of logistic regression and linear regression are graphed, they appear somewhat similar and conceptually the goal in both is to find a line of some sort. But the similarity of appearance is deceiving because the two techniques are entirely different.