Discover statistically significant correlations in the quantified self data
This correlation engine analyzes the daily metrics to find meaningful patterns.
Statistical Analysis: Uses Pearson correlation coefficient (r) to measure the strength and direction of relationships between metrics.
Confidence Levels:
Correlation does not imply causation. These insights show associationsbetween metrics, not cause-and-effect relationships.
Deep focus work time • Time spent outdoors
When Focus Time goes up, Outdoor Time tends to increases. This is a strong relationship. High statistical confidence (p < 0.01).
Deep focus work time • Time spent writing
When Focus Time goes up, Writing Time tends to increases. High statistical confidence (p < 0.01).
Time spent outdoors • Average daily temperature
When Outdoor Time goes up, Temperature tends to decreases. High statistical confidence (p < 0.01).
Deep focus work time • Average daily temperature
When Focus Time goes up, Temperature tends to decreases. High statistical confidence (p < 0.01).
Time spent writing • Average daily temperature
When Writing Time goes up, Temperature tends to decreases. Moderate statistical confidence (p < 0.05).
Sleep quality score (0-100) • Average daily cloud cover
When Sleep Score goes up, Cloudiness tends to decreases. Exploratory finding (p < 0.1).