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In statistics, bivariate data is data that has two variables. The quantities from these two variables are often represented using a scatter plot. This is done so that the relationship (if any) between the variables is easily seen. For example, bivariate data on a scatter plot could be used to study the relationship between stride length and length of legs.
Dependent and independent variables
In some instances of bivariate data, it is determined that one variable influences or determines the second variable, and the terms dependent and independent variables are used to distinguish between the two types of variables. In the above example, the length of a person's legs is the independent variable. The stride length is determined by the length of a person's legs, so it is the dependent variable. Having long legs increases stride length, but increasing stride length will not increase the length of your legs.
Correlations occur between the two variables or data sets. These are determined as strong or weak correlations and are rated on a scale of 0-1. 1 being a perfect correlation and 0 being no correlation. In the case of long legs and long strides, there would be a strong correlation.
Analysis of bivariate data
- "Bivariate". Wolfram Research. Retrieved 2011-08-15.
- National Council of Teachers of Mathematics. "Statistics and Probability Problem." Retrieved 7 August 2013 from http://www.nctm.org/uploadedFiles/Statistics%20and%20Probability%20Problem%202.pdf#search=%22bivariate data%22
- National Center for Education Statistics. "What are Independent and Dependent Variables? NCES Kids' Zone." Retrieved 7 August 2013 from http://nces.ed.gov/nceskids/help/user_guide/graph/variables.asp
- Pierce, Rod. (4 Jan 2013). "Correlation". Math Is Fun. Retrieved 7 Aug 2013 from http://www.mathsisfun.com/data/correlation.html
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