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.
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).
Deep focus work time • Time spent reading
When Focus Time goes up, Reading Time tends to increases. 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 writing • Number of coffee servings
When Writing Time goes up, Coffee Cups tends to decreases. Moderate statistical confidence (p < 0.05).
Number of coffee servings • Total workout time
When Coffee Cups goes up, Workout Duration tends to decreases. Moderate statistical confidence (p < 0.05).
Workout duration yesterday • Coffee cups yesterday
When Previous Day Workout Duration goes up, Previous Day Coffee tends to decreases. Moderate statistical confidence (p < 0.05).
Total caffeine consumed • Average daily temperature
When Caffeine Intake goes up, Temperature tends to decreases. Exploratory finding (p < 0.1).
Total caffeine consumed • Total workout time
When Caffeine Intake goes up, Workout Duration tends to decreases. Exploratory finding (p < 0.1).
Deep focus work time • Workout duration yesterday
When Workout Duration goes up, next day's Focus Time tends to increases. Exploratory finding (p < 0.1).