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.
Total caffeine consumed • Sleep score yesterday
When Sleep goes up, next day's Caffeine Intake tends to decreases. High statistical confidence (p < 0.01).
Average daily cloud cover • Coffee cups yesterday
When Coffee goes up, next day's Cloudiness tends to decreases. High statistical confidence (p < 0.01).
Deep focus work time • Time spent outdoors
When Focus Time goes up, Outdoor Time tends to increases. High statistical confidence (p < 0.01).
Time spent outdoors • Sleep score yesterday
When Sleep goes up, next day's Outdoor Time tends to increases. Moderate statistical confidence (p < 0.05).
Number of coffee servings • Sleep score yesterday
When Sleep goes up, next day's Coffee Cups tends to decreases. Moderate statistical confidence (p < 0.05).
Time spent outdoors • Total caffeine consumed
When Outdoor Time goes up, Caffeine Intake tends to decreases. Moderate statistical confidence (p < 0.05).
Average daily temperature • Coffee cups yesterday
When Coffee goes up, next day's Temperature tends to decreases. Moderate statistical confidence (p < 0.05).