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 • Running distance yesterday
When Run Distance goes up, next day's Focus Time tends to increases. This is a strong relationship. High statistical confidence (p < 0.01).
Number of coffee servings • Coffee cups yesterday
When Coffee goes up, next day's Coffee Cups tends to increases. High statistical confidence (p < 0.01).
Total caffeine consumed • Coffee cups yesterday
When Coffee goes up, next day's Caffeine Intake tends to increases. High statistical confidence (p < 0.01).
Time spent outdoors • Number of coffee servings
When Outdoor Time goes up, Coffee Cups tends to increases. High statistical confidence (p < 0.01).
Time spent reading • Running distance yesterday
When Run Distance goes up, next day's Reading Time tends to increases. Moderate statistical confidence (p < 0.05).
Distance ran • Sleep score yesterday
When Sleep goes up, next day's Running Distance tends to increases. Exploratory finding (p < 0.1).
Time spent running • Sleep score yesterday
When Sleep goes up, next day's Running Duration tends to increases. Exploratory finding (p < 0.1).