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Bivariate Statistics and Linear Models

In: The Foundations of Statistics: A Simulation-based Approach

Author

Listed:
  • Shravan Vasishth

    (University of Potsdam, Department of Linguistics)

  • Michael Broe

    (Ohio State University, 1304 Museum of Biological Diversity, Department of Evolution, Ecology & Organismal Biology)

Abstract

So far we’ve been studying univariate statistics; for example, for each individual in a population, we take a single measurement, height, age, etc. We combine these into a sample and compute a statistic: mean, variance, or some function of the variance. Now we consider the scenario where, for each individual in a population, we have two values: age and height, midterm and final exam result, etc. In such a situation we can, of course, treat each dimension independently, and compute the same univariate statistics as before. But the reason we measure two values is to assess the correlation between them, and for this, we require ‘two-dimensional’ or bivariate statistics.

Suggested Citation

  • Shravan Vasishth & Michael Broe, 2011. "Bivariate Statistics and Linear Models," Springer Books, in: The Foundations of Statistics: A Simulation-based Approach, chapter 0, pages 127-143, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-16313-5_6
    DOI: 10.1007/978-3-642-16313-5_6
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