It’s a good first step to figuring out what is going on, but it’s not possible to conclude temperature causes people to visit or not visit the library. Should we conclude that high outside temperatures cause more people to visit the library? Does that mean we should crank up the air conditioning so we can draw in more visitors? Not so fast.Īll we can conclude from these data is that there is an association between the outside temperature and people in the library. If we did this calculation, we would find that r = 0.947. In the first example above, if r = 0, one 80-degree day may have more visitors than a 40-degree day, whereas a second 80-degree day may have less visitors than a 40-degree day. The change of one variable has no effect on the other. The correlation becomes weaker as r approaches 0, with a value of 0 meaning there is no correlation whatsoever.The points on the graph would form a straight line sloping down. A 40-degree day would always have less housing insecure patrons than a 35-degree day. In the second example graph above, if r = -1, this would mean there is a uniform increase in temperature and decrease in housing insecure patrons visiting the library, with no exceptions. The closer r is to -1, the stronger the negative correlation is.The points on the graph would form a straight line sloping up. An 80-degree day would always have more visitors than a 75-degree day. In the first example graph above, if r = 1, this would mean there is a uniform increase in temperature and patrons visiting the library, with no exceptions. The closer r is to 1, the stronger the positive correlation is. Values for r always fall between 1 and -1. Without getting too deep into statistical calculations, you can determine how strong a correlation is by the correlation coefficient, which is also called r. In a weak correlation the values of one variable are related to the other, but with many exceptions.Ĭorrelation = a statistical measurement known as r If the points were more scattered, but we could still see them trending up or down, we would call that a “weak” correlation. The closer the points are to forming a compact sloped line, the stronger the correlation appears. It would look something like this, where the number of housing insecure patrons in the library are decreasing as the temperature outside increases. You can also have a strong negative correlation, which would show one value increasing as the other decreases. We would call this a strong positive correlation, which means both variables are moving in the same direction with a high level of predictability.Ĭorrelation = positive or negative weak or strong In this case, it looks like as the temperature increases, more people are visiting the library. This graph is called a scatterplot, and researchers often use it to visualize data and identify any trends that might be occurring. A graph representing these data might look like this: Our two variables are temperature and number of people. As an example, let’s say we notice our library is busier during the hotter months of the year, so we start writing down the temperature and number of people in the library each day. The confusion often occurs when we see what’s called a strong correlation -when we can predict with a high level of accuracy the values of one variable based on the values of the other. In other words, we assume one thing is the result of the other when that might not be the case. Such is the case when we confuse correlation (a statistical measurement of how two variables move in relation to each other) with causation (a cause-and-effect relationship). However, sometimes the visualization misleads us and we come to the wrong conclusions. People create charts and graphs so that we can visualize that meaning more easily. Those pieces of information are simply points on a chart or numbers in a spreadsheet until someone interprets their meaning. Data are pieces of information, like the number of books checked out at the library or reference questions asked.
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