Scatter plots are visualizations of two variables like X and Y. They are a fundamental tool in statistial disciplines as the regression analysis. Scatter plots help visualizing the relationship between X and Y which is the main objective in regression analysis.
Scatter plots example
Say we wish to visualize the relation between property prices and year. Let’s take the period of the financial crisis starting in 2008. X is the independent variable (year) and Y the dependent variable (price):
The regression analysis is a so-called bivariate analysis because it has two variables (X and Y). Through the regression analysis we can explore the correlation between Y is of X. In our example, we discover how dependent the property prices are based on the variation in year.
The scatter plot graph can answer to questions like these:
- how much does price (Y) vary from year 2008 to 2010 (X)?
- Is there correlation between the property prices (Y) and year (X)?
- How does property price (Y) change as year (X) changes?
Types of correlation
While scatter plots expose the association between the X and Y, they do not describe the relation in cause and effect. Example of different types of correlation between X and Y:
Direction, strength and linearity
Let’s hand draw an approximated trendline or a regression line and qualify this line as to its direction, strength and linearity which are terms often applied in statistical analysis:
In regression line and in most of the chapters related to regression analysis, we go apply the scatter plots for varies different purposes.
- Khan Academy (video 8:12): The video that I’ve based this page on: Bivariate relationship, linearity, strength and direction
- Mathsisfun (text page): Scatter plots
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