Statistics data are often classified according to the number of variables in a study. One variable might be “height”, while another variable might be “weight”. Depending on how many variables are being examined, the data could be univariate or bivariate.

A study that focuses on a single variable is called univariate data. You might study college students to determine their average SAT score, or a diabetic patient group to determine their weights. Bivariate data refers to when you study two variables. If you study a group to determine their average SAT Score and the age of these students, then you only have one piece of the puzzle (SAT score and their age). Bivariate data is also useful if you need to determine the weights and heights of diabetic patients. Bivariate data can also include two sets of items dependent on each another. This could be:

  • The temperature on that day was a major factor in the sales of ice cream.
  • There are two things that can cause traffic accidents: the weather and traffic accidents.

Bivariate data can be used in many ways in real-life. It is useful for predicting when a natural phenomenon might occur, for example. Bivariate data analysis is one tool in the toolbox of statisticians. Sometimes it is as easy as plotting one variable against the other on a Cartesian plan to get a clear picture of what the data are trying to tell. The scatterplot below illustrates the relationship between Old Faithful’s eruptions and the duration of each eruption.

What is it?

Bivariate analysis is the analysis of bivariate information. This is the simplest form of statistical analysis and is used to determine if two sets of values have a relationship. It typically involves the variables X, and Y.

  • Univariate Analysis This is an analysis of one (or “uni”) variable.
  • Bivariate analysis refers to the analysis of two variables.
  • Multivariate Analysis is an analysis of more variables than one.

You can store the results of the bivariate analysis in a 2-column table. You might be interested in finding out how caloric intake affects weight. This relationship is quite strong. has more information. The caloric intake would then be your independent variable. X and weight would then be your dependent variable. Y.

Bivariate analysis is not the same as two-sample data analysis. The X and the Y are not directly related when there is two sample data analysis, such as a two-sample z test in Excel. There are many data options that can be used to analyze the samples. For example, you could have different data values for each sample. The bivariate analysis allows for a different number of data points. This would be written with the x-variable followed closely by the y-variable: (3000,000.300).
Two sample data analysis
Sample 1:
Sample 2: 44.33.101
Bivariate analysis
(X, Y)=(100,56),(23,84),(398,63),(56,42)

Types of Bivariate Analysis

There are several types of bivariate analysis that are common:

1. Scatter plots

These will give you an idea of the pattern your variables follow.

2. Regression Analysis

Regression analysis is an umbrella term that covers a variety of tools you can use in order to identify the relationships between your data points. The points in the above image look like they might follow an exponential curve rather than a straight line. Regression analysis can provide the equation for this curve or line. You can also get the correlation coefficient.

3. Correlation Coefficients

Although you can use a computer to calculate correlation coefficients, the steps for finding the correlation coefficient manually can be found here. This coefficient will tell you whether the variables are related. A zero indicates that they are not correlated (i.e. zero means they aren’t related (i.e., they are not related in any way), and a 1 (either negative or positive) means the variables have perfect correlation (i.e. They are perfectly in sync with one another.