Explanation 1
Covariance gives direction but depends on units.
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Lesson 10 of 15
Measure linear association while checking nonlinear patterns, restricted range, and influential observations.
Theory
Measure linear association while checking units, nonlinear patterns, restricted range, and influential observations. The examples in this lesson use fictional STATLAB Academy business data and independently written explanations.
Explanation 1
Covariance gives direction but depends on units.
Explanation 2
Pearson correlation standardizes linear association to -1 through 1.
Explanation 3
Outliers and restricted range can change the coefficient.
Notation and formulas
r = covariance(x, y) / (s_x * s_y)
Measure linear association while checking units, nonlinear patterns, restricted range, and influential observations.
Interactive Mission
Pearson correlation
0.99
Sensitive to influential points and restricted range.
Association does not by itself establish causation.
R connection
Run and edit the code, inspect printed output, and use the movable R Code Guide for line-by-line help.
Live R Lab
Calculate covariance, Pearson correlation, a correlation matrix, and an outlier sensitivity comparison.
Ready to run
Worked example
Scenario
A fictional training organization needs to use covariance and correlation to support an operational decision.
The best summary is the one that fits the question, data structure, units, and distribution.
Interpretation safeguard
Association does not by itself establish causation.
Dataset provenance
Original synthetic data generated for STATLAB Academy. No textbook data used.
Reflection
Name the question, data structure, units, chosen method, and one limitation.
Assistant
Coach me through Covariance and Correlation without giving direct quiz answers or writing my final report. Ask me to justify assumptions, units, and interpretation.
Exit check
Use a hint before checking if needed. Explanations appear only after an answer is submitted.
Checkpoint
Question 1 of 2. Answered 0/2. Passing score: 70%.