What is a Scatter Plot?
A scatter plot displays individual data points on a two-dimensional plane, with X and Y coordinates representing two numeric variables. Each point represents one observation, making scatter plots the go-to chart for exploring relationships between paired variables.
Interpreting Patterns
- Upward trend = positive correlation.
- Downward trend = negative correlation.
- Random cloud = little to no correlation.
- Curved pattern = non-linear relationship.
Frequently Asked Questions
What does a scatter plot show?
A scatter plot displays the relationship between two numeric variables, with each point representing one observation. Patterns reveal correlation, clusters, and outliers.
How is the regression line calculated?
The tool uses ordinary least squares (OLS) linear regression. The R² value indicates how well the line fits the data (0 = no fit, 1 = perfect fit).
Key Terms
- Correlation
- Measures the strength and direction of the linear relationship between X and Y.
- Independent Var (X)
- The variable that is manipulated or controlled.
- Dependent Var (Y)
- The variable that is measured or observed.
- Regression Line
- A best-fit line through the scatter points, showing the trend.
Computation Method
- Quartiles are calculated using linear interpolation (method 7 from Hyndman & Fan, 1996), consistent with Python's NumPy and pandas defaults.
- Outlier detection uses the Tukey fences method: lower fence = Q1 − 1.5 × IQR, upper fence = Q3 + 1.5 × IQR.
- Chart scaling caps the display axis at the upper fence value so the visualization remains clear even when extreme outliers are present.
References
- Pearson, K. (1895). "Note on Regression and Inheritance in the Case of Two Parents." Proceedings of the Royal Society of London, 58, 240–242.
- Anscombe, F. J. (1973). "Graphs in Statistical Analysis." The American Statistician, 27(1), 17–21.
- Tufte, E. R. (2001). The Visual Display of Quantitative Information. 2nd ed. Graphics Press.