The residual plot represents the error between the actual value.
Y-axis: residuals X-axis: the predictor variable or fitted values.
Why do we want to know the error, what benefits us in getting the residuals? I've uploaded an image from the video. I don't understand what is being sampled in the illustration.
Where is the independent and dependent variable(s)? How do you identify the error?
They take one calculation and apply to the right-side of the graph and so on.
I am taking this class and don't know anything about what the instructor is talking about.
Can someone break this all the way down? (This is a beginner's course!)
Here is the transcript from the video:
Examining the predicted value and actual value we see a difference. We obtain that value by subtracting the predicted value, and the actual target value. We then plot that value on the vertical axis with the dependent variable as the horizontal axis. Similarly, for the second sample, we repeat the process. Subtracting the target value from the predicted value. Then plotting the value accordingly. Looking at the plot gives us some insight into our data. We expect to see the results to have zero mean, distributed evenly around the x axis with similar variance. There is no curvature. This type of residual plot suggests a linear plot is appropriate