# Chapter 1 Introduction to Correlation

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1 Chapter 1 Introduction to Correlation Suppose that you woke up one morning and discovered that you had been given the gift of being able to predict the future. Suddenly, you found yourself able to predict, with some degree of accuracy, things like: How long someone will live Whether the stock market will go up or down Whether or not someone will become a criminal Whether or not a surgery will prolong a cancer patient s life Whether or not a depressed person will commit suicide Whether or not a person will make a productive employee Whether or not a football team will make a first down on the next play Whether or not somebody s marriage will survive or end in divorce How would that make you feel? Would you feel lucky? Would you head for Las Vegas? Would you use your gift for good and not for evil? Or would you say, No thanks! Not interested. Well, in fact you are about to be given such a gift and I am going to give it to you! It is in the form of the Correlation Coefficient. The correlation coefficient is an almost magical statistic that allows you to do things like I have listed above and much more! Developed by Karl Pearson over a hundred years ago, it is officially called the Pearson Product Moment Correlation Coefficient. However, it is usually referred to simply as Pearson s r or just the Correlation Coefficient is an extremely powerful and useful statistical too that allows you to evaluate whether or not a relationship exists between two things. These things are referred to as variables. Perhaps more importantly, not only does the correlation coefficient tell you whether or not a real relationship exists, it allows you to tell just how strong that relationship is. Finally and here is where predicting the future comes in if a real relationship exists between two variables, then the correlation coefficient allows you to make predictions about one variable based solely on knowledge of the other variable. In other words, if a relationship exists between a child watching violent television programs and his or her aggressive behavior in school, then the correlation coefficient will allow you to predict how violent a child would be in school based solely on how much violent television the child watched at home! Pretty cool, huh? Think about the kind of power that gives you! Insurance companies use the correlation coefficient to predict how likely a person is to get into an accident. This enables them to set insurance rates so that high risk drivers pay more. Mutual fund managers use correlation coefficients to decide what a stock s price is likely to do and use that information to decide whether to buy or sell stocks. Healthcare providers use correlation coefficients to decide whether or not someone will likely die if they do not get a certain surgery and so they make decisions accordingly. Psychologists use correlation coefficients to predict whether or not a sexually violent predator (a person repeatedly convicted of sex crimes) is likely to 1

3 Now let s actually follow the steps listed above to see how we could discover whether or not a relationship exists between the amount of education a person gets and their starting salary. Step 1 Clearly state our question: Is a person s starting salary (the amount they are paid when they start their first job) related to how many years of education they have successfully completed? Step 2 Clearly identify the two variables we need to answer this question: 1. How many years of education did you successfully complete? 2. What hourly salary did you receive in the first job you got after completing your education? Step 3 Suppose we select 30 people at random to whom we will ask our two questions. Step 4 We ask each of the 30 people our two questions. Step 5 We graph their responses in a scatterplot (see Figure 11-1, below) FIGURE 11-1 Scatterplot of number of years of education by starting hourly salary of first job obtained after completing school (Based on fictitious, or made-up, data) Starting Hourly Salary Years of School Successfully Completed 3

5 person s data on a scatterplot. You may very well see something similar to what is shown in Figure 11-2, below. FIGURE 11-2 Scatterplot of average minutes of exercise each day by cholesterol level (Based on fictitious, or made-up, data) Cholesterol Level verage Minutes of Daily Exercise Take a close look at Figure Do you notice anything about what happens to a person s cholesterol level as their amount of daily exercise increases? Look at people who exercise less than minutes a day do they tend to have higher or lower cholesterol levels than those who exercise more than minutes a day? Based on these data, it would appear that as the number of minutes a day a person exercises goes up, their cholesterol level goes down. This is an example of a negative relationship. You can generally identify a negative relationship in a scatterplot because the tendency is for the dots to go from the upper left to the lower right portion of the graph. 5

6 The Radical Statistician Summary of Types of Relationships When you are trying to see whether or not relationships exist between two variables, you can find one of three possible things: 1) positive relationship exists between them, 2) a negative relationship exists between them or 3) no relationship at all exists between them. Positive Relationships In a positive relationship, as once variable increases (goes up) the other variable also increases (goes up). Similarly, as one variable decreases (goes down) the other variable goes down. Both variables tend to move in the same direction. If there is a positive relationship between people s amount of education and his or her starting hourly salary that means: s amount of education goes up, starting hourly salary tends to go up. s amount of education goes down, starting hourly salary tends to go down. Negative Relationships In a negative relationship, as one variable increases (goes up) the other variable decreases (goes down). Similarly, as one variable decrease (goes down) the other variable increases (goes up). In other words they go in opposite directions from each other. The variables tend to move in the opposite direction. If there is a negative relationship between average number of minutes of exercise each day and a person s cholesterol level that means: s minutes of daily exercise goes up, cholesterol level tends to go down. s minutes of daily exercise goes down, cholesterol level tends to go up. No Relationship When there is no relationship between two variables that means that you really can t say anything about one variable based on values on the other variable. In other words, as values on one variable increases (goes up) there is no predictable change in the other variable. Sometimes the other variable goes up. Sometimes the other variable goes down. Sometimes, it just stays the same. Is there a relationship between the month in which a person is born and how intelligent they are? If you were to ask 30 people their birth month and then test them to find out what their Intelligence Quotient (IQ) is, what you should find is the month in which a person is born has nothing to do with their IQ. There should be no relationship between these two variables. In cases where there is no relationship between the two variables, a scatterplot should look like a bunch of random dots kind of like a target that has been shot with a shotgun. See Figure 11-3 for an example of no relationship between two variables. 6

7 FIGURE 11-3 Scatterplot illustrating an example of no relationship between two variables (Based on fictitious data) Intelligence Birth Month Notice that you really can t say anything about a person s intelligence based on his or her birth month. There is no predictable trend or relationship that exists (at least in this fictitious data set. If you really want to find out, go out and collect some data and see what you find!). This is an example of there being no relationship between two variables. How Strong Is The Relationship? Now that you know how to see if there is a relationship between two variables, the next question to ask is, how strong is that relationship? Why is this an important question? Let s take a look at an example. Suppose Spring Break is fast approaching and you and some of your friends are thinking about heading south to Cancun, Mexico, for some rest, relaxation and fun. Being the wise consumer/traveler that you are, you ask yourself Is there a relationship between how expensive a hotel is and how much people enjoy their stay? Your thinking is that if there is a relationship that indicates that the more people pay, the more fun they tend to have, then that is what you will do. On the other hand, if people who stay at cheaper hotels have as much as or more fun, why pay the extra bucks? 7

9 FIGURE 11-5 Example of a strong positive relationship between hotel price and amount of fun reported by people visiting Cancun over spring break (Based on fictitious data) Cost of hotel How much fun reported Figure 11-5 is an example of a strong positive relationship. In a strong relationship (whether it is positive or negative) the dots tend to group to form more of a straight line. Notice how in Figure 11-5 there is a very clear trend that tells you as people pay more for their hotel they also tend to report having more fun during their stay. Why Is It Important To Know The Strength Of Relationship? Now that you have some idea about what it means to say that a relationship is strong or weak, why is it so important? Because it allows you to make predictions! Let s get back to our Spring Break example. Suppose that you have \$2,000 to spend and you want to be as certain as possible to get the most for your money. side from the cost of your airline tickets, your next largest expense is likely to be your hotel. Should you pay top dollar for your hotel or go on the cheap side? If you had conducted our little study and got the results found in Figure 11-4, above, you could say Yes, I need to avoid being too cheap because it seems like people report having more fun in more expensive hotels. However, it is hard to tell if paying more than \$500 would ensure that I would have a better time than going for a \$500 hotel. In other words, because the relationship is weak, it is difficult to predict with any 9

10 The Radical Statistician accuracy how much fun you would have although it seems pretty certain that cost seems to be related to fun. On the other hand, take a look at Figure 11-5 which illustrates a strong positive relationship. Notice that with a strong relationship it is very clear that higher price definitely seems related to more fun. Given the information in Figure 11-5, one could clearly say that People who paid \$900 reported having much more fun than people who paid \$500. Therefore, you could predict that if you paid top dollar for your lodging, you would have more fun. Note, this does not mean you will have more fun only that it appears likely that you will. You cannot say that paying more will cause you to have more fun, only that since it appears cost and fun are related it seems reasonable to predict that you may have more fun. The main point of this is that a strong relationship allows you to make much more accurate predictions than a weak relationship. This ability to make accurate predictions is critical in a great many professional settings. Psychologists, medical professionals, business executives, stock brokers, military leaders, law enforcement agents are all interested in being able to make predictions. The concept of correlation provides a tool that helps people make predictions and to do so with some amount of accuracy. The Problem them: There are a couple of problems with what we have done so far. Let me review 1. Creating scatterplots can be time consuming to construct. 2. While scatterplots can be useful to get an idea about whether or not a relationship exists between two variables, it can be very difficult to differentiate between weak relationships (which are still important) and instances when there are no relationships. What Is Needed What we need is a way to take all the information we have collected and somehow reduce it to a single summary number that tells us: 1. Is there a relationship between the two variables? 2. Is the relationship, if it exists, positive or negative? 3. How strong is the relationship? In addition, it would be great if the same number could be used to actually make predictions about one variable based on information about the other. For example, wouldn t it be great if this number would allow us to decide how much fun we want to have in Cancun and have it tell us how much we would need to spend on our hotel? s a matter of fact, there is such a number. It is called the Correlation Coefficient and it will be discussed in the next chapter. 10

11 Terms to Learn You should be able to define the following terms based on what you have learned in this chapter. Correlation Correlation Coefficient Negative Relationship Positive Relationship 11

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