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Scatterplots/Bivariate Data

Author: Alexander Greene

What's in this packet

This packet has video on how to make a scatterplot. We also show you how to interpret the scatterplot, decide which variable is responsive and which is explanatory, and how to explain the relationship between the two variables. Some terms that may be new to you are:

 

  • Scatterplot
  • Responsive Variable
  • Explanatory Variable
  • Association
  • Correlation
  • Correlation Coefficient
  • Lurking Variables

Source: Greene

Definitions

Seeing as the video has most of the information you need, I will just have a few definitions here for your reference.

Bivariate data - data that has two variables per observation, usually an x and y variable.

Scatterplot - graph displaying categorical data, with an x-axis and y-axis.

Response Variable - the variable that is explained by the other.

Explanatory Variable - the variable that explains the other.

Association - The relationship between two variables. Can be positive or negative, can be strong of weak, can be linear or curvilinear.

Correlation Coefficient(r) - The quantitative representation of association. This is a number between -1 and 1. Anything close to -1 is a strong negative linear association, anything close to 1 is a strong positive linear association, and anything close to 0 is a weak linear association, or no linear association.

Lurking variables - variables that are not considered in the bivariate data, yet affect the relationship between the two variables. Also called a counfounding variable.

Source: Greene

Scatterplots

This video shows you two variables, x and y, and how to make a scatterplot from them. It also talks about correlation, relationships, and lurking variables.

Source: Greene