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Bivariate Analysis - Numerical & Numerical

Scatter Plot

A scatter plot is a useful visual representation of the relationship between two numerical variables (attributes) and is usually drawn before working out a linear correlation or fitting a regression line. The resulting pattern indicates the type (linear or non-linear) and strength of the relationship between two variables. More information can be added to a two-dimensional scatter plot, for example, we might label points with a code to indicate the level of a third variable. If we are dealing with many variables in a data set, a way of presenting all possible scatter plots of two variables at a time is in a scatter plot matrix.


Linear Correlation

Linear correlation quantifies the strength of a linear relationship between two numerical variables. When there is no correlation between two variables, there is no tendency for the values of one quantity to increase or decrease with the values of the second quantity.

r only measures the strength of a linear relationship and is always between -1 and 1 where -1 means perfect negative linear correlation and +1 means perfect positive linear correlation and zero means no linear correlation.
Temperature 83 64 72 81 70 68 65 75 71 85 80 72 69 75
Humidity 86 65 90 75 96 80 70 80 91 85 90 95 70 70
  Variance Covariance Correlation
Temperature 40.10 19.78 0.32
Humidity 98.23    

There is a weak linear correlation between Temperature and Humidity.