Everything else is ready: study the parts, the key terms and the quiz below.
Correlation, Confounding and Causation
Measure how two features move together, and know what correlation can never tell you.
In this lesson6 parts
- 01Ask what two recorded variables show
- 02Build Pearson correlation from the pairs
- 03Compare declared education groups
- 04Compare a binary income label with rates
- 05State the causal limit
- 06Reproduce the calculation and decide
Key terms
The words this lesson introduces, each in one line. The module’s glossary collects them all.
- candidate feature
- A recorded input proposed for later model evaluation.
- scatter plot
- One point per paired numerical observation.
- linear association
- A straight line pattern between two numerical variables. (narration)
- deviation
- An observed value minus the declared group's mean.
- covariance
- The average signed product of paired deviations. Its unit is the product of the two units.
- Pearson correlation
- Standardized linear association between two numerical variables: covariance divided by the two standard deviations. Written
r, between-1and1. - subgroup
- Records selected by one declared value of another variable.
- observational data
- Recorded values without assigning the proposed exposure.
- rate
- The selected outcome count divided by the group's total.
- potential confounder
- A third variable associated with predictor and outcome that could alter their interpretation.
- causal effect
- The outcome change attributable to an intervention under a valid comparison.
- predictive value
- Measured improvement on evaluation data that did not guide the exploration.
Quiz 5 questions
Your first pick on each question is the one that counts, and a right one earns a coin. Getting one wrong here is how the lesson sticks.
Practice
Problems to solve in your own notebook. Each states the problem, not the steps: working out the steps is the exercise. Level A applies the lesson, B combines it with earlier ones, C stretches it.
The self-checking notebook for this lesson is Stats 1.6 and 1.10 · Association in the census records.
Common mistakes
What you will see when it goes wrong, why it happens, and the fix.
Where it’s used
Where this lesson’s ideas turn up in real work.