There is an area of psychological research that particularly fascinates me,
and that is human beings' ability to predict the future.
It turns out we're pretty bad at it, and
despite how bad we are at making accurate predictions, we continue to do so with great confidence.
But to be fair, making predictions is difficult especially when they involve the future.
But science gives us another way to make predictions that does not involve a crystal ball
Instead we can use statistics and research to make some pretty good predictions
about how our fellow homo sapiens might act or respond,
and this technique of prediction is called linear regression,
and it works because we know the great secret about predicting the future.
The best predictor of future behavior is past behavior.
If you want to know how someone will behave in the future just look at how they have behaved in the past
Because most people, unless they are trying hard not to, behave in very consistent ways.
Even people who are erratic are consistently erratic. Predictably erratic.
So if you know someone who has a history of turning on other people that they used to call friends, watch out.
The snake in the grass does not have to bite you to still be a snake. In fact, watch how people treat others
even if they don't treat you badly because, as Dave Berry said, if you are with someone who is nice to you
but mean to the waiter, you are with a mean person.
Even if they are nice to you now
just wait if they have it in themselves to be cruel to others.
Your turn is coming if you're with someone who was cheating on somebody else to get with you.
Don't be surprised later when you get cheated on because you are with a cheater.
So let's put all of this in terms of science and statistics.
I am going to pick a guy at random from the US population, and I want you to predict his height.
How tall is he?
Now at first you might protest that you have no idea because
you've never seen him, and you know nothing about him, and he was chosen at random. How could you ever guess how tall he is?
But you already know one thing that would help you make that guess
knowing nothing else about this individual who was selected randomly from a population.
Your best prediction for his height would be what?
The mean.
The average height of the population from which he was drawn.
If you know that the average American male is
177 centimeters, 69.7 inches, 5'10" tall,
then your best guess is going to be 5'10"
because that is the most common height within the population.
But now I give you more information.
I stare into the crystal ball, and I tell you that this randomly selected male is also
a basketball player.
So now what are you going to guess for his height?
You would start with that average 5'10" and then consider that basketball players tend to be above average in height,
and you're going to guess something like
6'4"
And then I tell you that I have randomly selected another person who is a horse jockey.
Again, you're going to start with the average think about what you know about horse jockeys and make a guess that is below average.
In each case you start with the average, and then you add or subtract based on what you know about another variable,
however, this adjustment will only work if the two variables are related.
If one variable gives you no information about the other, such as the randomly selected male as a college professor,
then it would be a poor predictor.
But what did you learn in the last unit about relationships between variables?
You learned how to use
correlation to describe the strength and direction of the relationship between two variables.
So now we are going to apply that knowledge to linear regression.
The term regression is synonymous with prediction.
Linear regression is a way of predicting an unknown Y variable score based on X scores from a
correlated variable, such as using SAT scores to predict grades in college.
Again the best predictor of future behavior is past behavior.
So if someone was a bright student in elementary school,
they are probably going to make good grades in junior high school school. And if someone studied hard and did well in junior high school,
we predict they will continue to make good grades in high school.
And if they learned enough in high school to do well on the SAT, we predict that they will make good grades in college
Or if we are trying to hire the best people for a job,
we might start by developing a test that identifies our best performers currently on the job, called
incumbents. If we have a test that reliably distinguishes between top performers and poor performers already on the job,
then we might use that same test as a
pre-employment test.
Because we predict that job applicants who score well on this test,
will also be strong performers on the job because they are already like top performers that we already have.
Now in the last unit we learned about correlation. Correlation, or r,
summarizes the direction and the strength of the relationship between two variables.
Because when two variables are strongly correlated,
then one will likely be a good predictor of the other.
When one goes up you can predict what will happen to the other one because they are related.
A correlation coefficient gets calculated as part of running a regression analysis
If a correlational relationship exists,
we can use regression to predict other relationships between the variables.
The stronger the correlation, the better the prediction. If no correlation exists
there's no point in running a regression. You won't predict anything anyway.
If knowing someone is a college professor tells you nothing about their height, then using their job as a predictor won't help.
Remember there are two types of linear relationships.
Positive relationships where the variables go up or down together, and
negative relationships in which the variables move opposite of each other.
Knowing how variables are related such as heights of basketball players or jockeys will help us make better predictions.