This tutorial will discuss variables within the field of statistics. The following elements will be the main focus of this tutorial:
In statistics, a variable is any attribute that we can measure about a population and they are used in a study. And it's very important to carefully define the variables to be measured when creating a study.
Think of things that we could find out about people-- the state they live in, their ethnicity, their zip code, whether or not they smoke.
All sorts of these things are variables. You might only want to know one of these things or some of these things.
A political poll, for example, wouldn't really necessarily need to know if a candidate was a smoker or the number of times they eat out per week.
You might want to know only the circled ones-- their age, their gender, their state, political affiliations, zip code, ethnicity, and city.
All of these circled variables could potentially have some bearing on a political poll. They are the variables of interest for this study. They are the variables you would be interested in measuring.
However, if you were conducting a weight loss study, political affiliation will likely not be a variable to measure, but favorite food might seem important.
Some studies try to determine a cause-and-effect relationship between two variables in that one variable causes the other. An increase in one corresponds to an increase or decrease in the other. In those cases, we define the one that causes the other as the explanatory variable. And you can have more than one. And variables that are the result are called response variables.
So an example would be the number of hours you study and your grade on the exam. You might hypothesize that as you increase the number of hours that you study, your grade on the exam will increase as well. So this helps to explain your grade.
The average monthly temperature and ice cream sales. You might assume that as the temperatures get warmer, that ice cream sales would go up in kind.
Something that's a little bit less obvious is whether or not gender, which is a categorical variable, plays a role in which political party people will choose. Are males more likely to be Republican? Or are women more likely to be independent voters? We don't know. But that would be an interesting question to investigate.
So to recap, variables are what we choose to measure in a study. And then the variables of interest to you, those little ones that were circled in green on those previous slides. Those depend on the questions that we're trying to answer. Not every variable must be measured, just the ones that of interest.
By looking at variables in context, you learned that if a cause and effect relationship is thought to exist, you can break the variables even further down into explanatory and response variables.
Source: This work is adapted from Sophia author Jonathan Osters.
A variable that we believe is predictive of something else. An increase in this variable will correspond to an increase or decrease in some other variable.
A variable that is affected by the explanatory variable.
Any attribute or number that can be measured about individuals in a study.
Any variable which we need to know about in the context of a study.