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Scatter plot

As explained in Effective Data Analysis

A scatterplot is a graph that displays paired numerical observations as points to reveal relationships between variables.

A scatter plot displays paired numerical observations as points, helping reveal whether two variables have a relationship, its direction, and whether a linear trend is plausible. section 3.2.2

Use scatter plots to support correlation analysis and stakeholder decisions:

  • Inspect whether a relationship is approximately linear before interpreting Pearson’s correlation. section 3.2.2
  • Add a regression line when the trend is unclear and a linear relationship needs clearer communication. section 3.2.2
  • Treat the plot as an early analytical deliverable, not a complete explanation of causes or a substitute for more sophisticated modeling. section 3.2.2

Temperature and rat sightings

A scatter plot of daily low temperature against daily rat sightings shows a clear positive relationship: warmer days correspond with more reports to the city’s 311 hotline. The pattern can inform preparations while further analysis investigates predictors of change. section 3.2.2

How Effective Data Analysis covers it

How deep it goes
Explains point-based relationship inspection, linearity checks, and regression overlays, while deferring comprehensive predictive analysis.
Examples
Uses rat sightings versus weather and production-cost variables versus product margin as analytical examples.
What it assumes
Assumes numerical variables and correlation concepts, while emphasizing practical inspection of linearity before interpretation.
What you can do after
Evaluate relationships visually, communicate correlation findings, and prepare variables for regression analysis.
How it connects
Connects scatter plots with Pearson correlation, regression, linearity, and statistical modeling.

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This explainer was generated by Manning's knowledge graph from the text of Effective Data Analysis. The section links point to the parts of the book it draws on.