Does this make sense? Just in case, let us look at a table containing a bivariate set of data which happens to use the principles of coordinates as variables:įor this example, lets say you have five friends, and all of them live in the northeast direction when taking your house as the point of reference. The process to create a scatter plot is rather simple, just think of each variable as a coordinate that will allow you to locate a point in a graph. Think of them as the graphic representation of two data sets which have been put into place by dedicating each axis in the plot to a different variable.īelow you can see a few scatter plot examples: This relationship between the two characteristic variables of the population is what we call correlation, and we will talk a little bit more about it later on this lesson.īefore we get to the correlation definition, it is important we look at the graphic representation of a bivariate data analysis: a scatter plot.Ī scatter plot is the graphic representation of the relationship between the two variables coming from a bivariate data set. while bivariate data sets describe TWO and their relationship with each other. In summary, the main difference between univariate and bivariate data is that univariate data sets describe ONE variable from a population. On the other hand, from our definition of bivariate data above we know that bivariate data focuses on the relationships between two different variables of data from a population in other words, a bivariate data set is concerned on finding the behaviour of two characteristics (be it quantitative or qualitative) from a population, and checking if these depend on each other, or affect each other in any way, providing a more extended range of information from the population in question. Univariate sets are focused on describing a particular characteristic (be it qualitative or quantitative) from a population, or a sample of it. Through our past lessons such as frequency distributions and histograms and frequency polygons, we talked about data sets that contained only one variable or univariate data sets. \quad What is the difference between univariate and bivariate data?.You can observe a few other bivariate data examples (with tables) in our videos for this lesson, for now, our last example escenario takes us to the next question (subsection of this topic): Such information could be very important for a marketing campaign on real estate, since it would allow the sellers to target the population group that has highest probability of investing in a new home that information could not be obtained from a data set with only one variable (for example, lets say we just gather the income each people in Richmond has, but do not gather any more information on them, it would be very difficult to find out which is the target population for real estate commercials just based on that, since it could be anyone).
In this case the two variables would be age and income, and such joint statistical analysis would allow the researcher to infer conclusions on the age of the population who has the highest economical means. In a bivariate data set, each data point from the set has two values corresponding to each of the two variables in the set, this pairing of values per data point allows us to see the relationship between the variables being studied (if any) and see any tendency patterns in their behaviour.įor example, a simple bivariate data set could be the gathering of the ages and yearly income from the adult population in the city of Richmond. We define bivariate data as data that has two variables. In real life, we know a population has a huge amount of different characteristics which can (or cannot) be dependent on each other, or tied to one another in a certain way therefore, this lesson will focus on that, on cases in which we start studying populations from more than one of their characteristics, thus paying attention to cases where two variables are being studied, compared, represented together and even produced conclusions based on their behaviour by themselves and with each other: it is time to learn about bivariate data (sometimes called bivariable data). But all of the topics covered so far focus on the idea of having a data set produced from the study of a single characteristic (a single variable) from a population, or a sample of a population.
#DESCRIBING SCATTER PLOTS HOW TO#
So far we have focused our lessons in statistics to learn how to gather data and present it in a meaningful and easily to communicate way.
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