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This individual's Steatorrhea is generally lowest after an average of 3.1 out of 5 of Energy over the previous 24 hours.
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Blue represents the mean of Energy over the previous 24 hours
An increase in 24 hours cumulative Energy is usually followed by an decrease in Steatorrhea. (R = -0.101)
Typical values for Steatorrhea following a given amount of Energy over the previous 24 hours.
Typical Energy seen over the previous 24 hours preceding the given Steatorrhea value.
This chart shows how your Energy changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Energy on each day of the week.
This chart shows the typical value recorded for Energy for each month of the year.
This chart shows how your Steatorrhea changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Steatorrhea on each day of the week.
This chart shows the typical value recorded for Steatorrhea for each month of the year.

Abstract

This individual's Steatorrhea is generally 2% lower than normal after 3.0833333333333 out of 5 Energy per 24 hours. This individual's data suggests with a high degree of confidence (p=0.11622937164578, 95% CI -0.283 to 0.081) that Energy has a weakly negative predictive relationship (R=-0.1) with Steatorrhea. The highest quartile of Steatorrhea measurements were observed following an average 2.87 out of 5 Energy. The lowest quartile of Steatorrhea measurements were observed following an average 3.0589622641509 /5 Energy.Steatorrhea is generally 2% lower than normal after an average of 3.0589622641509 out of 5 of Energy over the previous 24 hours. Steatorrhea is generally 8% higher after an average of 2.87 out of 5 of Energy over the previous 24 hours.

Objective

The objective of this study is to determine the nature of the relationship (if any) between Energy and Steatorrhea. Additionally, we attempt to determine the Energy values most likely to produce optimal Steatorrhea values.

Participant Instructions

Record your Energy daily in the reminder inbox or using the interactive web or mobile notifications.
Record your Steatorrhea daily in the reminder inbox or using the interactive web or mobile notifications.

Design

This study is based on data donated by one participant. Thus, the study design is consistent with an n=1 observational natural experiment.

Data Analysis

Energy Pre-Processing
Energy measurement values below 1 out of 5 were assumed erroneous and removed. Energy measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Energy so any gaps in data were just not analyzed instead of assuming zero values for those times.
Energy Analysis Settings

Steatorrhea Pre-Processing
Steatorrhea measurement values below 1 out of 5 were assumed erroneous and removed. Steatorrhea measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Steatorrhea so any gaps in data were just not analyzed instead of assuming zero values for those times.
Steatorrhea Analysis Settings

Predictive Analytics
It was assumed that 0 hours would pass before a change in Energy would produce an observable change in Steatorrhea. It was assumed that Energy could produce an observable change in Steatorrhea for as much as 1 days after the stimulus event.
Predictive Analysis Settings

Data Quantity
1079 raw Energy measurements with 318 changes spanning 2264 days from 2013-01-12 to 2019-03-27 were used in this analysis. 790 raw Steatorrhea measurements with 286 changes spanning 1116 days from 2016-03-08 to 2019-03-29 were used in this analysis.

Data Sources

Energy data was primarily collected using QuantiModo. QuantiModo allows you to easily track mood, symptoms, or any outcome you want to optimize in a fraction of a second. You can also import your data from over 30 other apps and devices. QuantiModo then analyzes your data to identify which hidden factors are most likely to be influencing your mood or symptoms.

Steatorrhea data was primarily collected using QuantiModo. QuantiModo allows you to easily track mood, symptoms, or any outcome you want to optimize in a fraction of a second. You can also import your data from over 30 other apps and devices. QuantiModo then analyzes your data to identify which hidden factors are most likely to be influencing your mood or symptoms.

Limitations

As with any human experiment, it was impossible to control for all potentially confounding variables. Correlation does not necessarily imply causation. We can never know for sure if one factor is definitely the cause of an outcome. However, lack of correlation definitely implies the lack of a causal relationship. Hence, we can with great confidence rule out non-existent relationships. For instance, if we discover no relationship between mood and an antidepressant this information is just as or even more valuable than the discovery that there is a relationship.
We can also take advantage of several characteristics of time series data from many subjects to infer the likelihood of a causal relationship if we do find a correlational relationship. The criteria for causation are a group of minimal conditions necessary to provide adequate evidence of a causal relationship between an incidence and a possible consequence.

The list of the criteria is as follows:
Strength (A.K.A. Effect Size)
A small association does not mean that there is not a causal effect, though the larger the association, the more likely that it is causal. There is a weakly negative relationship between Energy and Steatorrhea

Consistency (A.K.A. Reproducibility)
Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect. Furthermore, in accordance with the law of large numbers (LLN), the predictive power and accuracy of these results will continually grow over time. 507 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Energy values, the observed strength of the relationship will decline until it is below the threshold of significance. To it another way, in the case that we do find a spurious correlation, suggesting that banana intake improves mood for instance, one will likely increase their banana intake. Due to the fact that this correlation is spurious, it is unlikely that you will see a continued and persistent corresponding increase in mood. So over time, the spurious correlation will naturally dissipate.

Specificity
Causation is likely if a very specific population at a specific site and disease with no other likely explanation. The more specific an association between a factor and an effect is, the bigger the probability of a causal relationship.

Temporality
The effect has to occur after the cause (and if there is an expected delay between the cause and expected effect, then the effect must occur after that delay). The confidence in a causal relationship is bolstered by the fact that time-precedence was taken into account in all calculations.

Biological Gradient
Greater exposure should generally lead to greater incidence of the effect. However, in some cases, the mere presence of the factor can trigger the effect. In other cases, an inverse proportion is observed: greater exposure leads to lower incidence.

Plausibility
A plausible bio-chemical mechanism between cause and effect is critical. This is where human brains excel. Based on our responses so far, 1 humans feel that there is a plausible mechanism of action and 0 feel that any relationship observed between Energy and Steatorrhea is coincidental.

Coherence
Coherence between epidemiological and laboratory findings increases the likelihood of an effect. It will be very enlightening to aggregate this data with the data from other participants with similar genetic, diseasomic, environmentomic, and demographic profiles.

Experiment
All of human life can be considered a natural experiment. Occasionally, it is possible to appeal to experimental evidence.

Analogy
The effect of similar factors may be considered.

Relationship Statistics

Property Value
Cause Variable Name Energy
Effect Variable Name Steatorrhea
Sinn Predictive Coefficient 0.106
Confidence Level high
Confidence Interval 0.18247982892264
Forward Pearson Correlation Coefficient -0.101
Critical T Value 1.646
Average Energy Over Previous 24 hours Before ABOVE Average Steatorrhea 2.87 out of 5
Average Energy Over Previous 24 hours Before BELOW Average Steatorrhea 3.059 out of 5
Duration of Action 24 hours
Effect Size weakly negative
Number of Paired Measurements 507
Optimal Pearson Product 0.027483905315456
P Value 0.11622937164578
Statistical Significance 0.9994
Strength of Relationship 0.18247982892264
Study Type individual
Analysis Performed At 2019-04-04

Energy Statistics

Property Value
Variable Name Energy
Aggregation Method MEAN
Analysis Performed At 2019-03-30
Duration of Action 24 hours
Kurtosis 3.8118502852522
Maximum Allowed Value 5 out of 5
Mean 2.9978 out of 5
Median 3 out of 5
Minimum Allowed Value 1 out of 5
Number of Changes 318
Number of Correlations 1947
Number of Measurements 1079
Onset Delay 0 seconds
Standard Deviation 0.67983538911105
Unit 1 to 5 Rating
UPC 637769766115
Variable ID 1306
Variance 0.46217615628777

Steatorrhea Statistics

Property Value
Variable Name Steatorrhea
Aggregation Method MEAN
Analysis Performed At 2019-03-29
Duration of Action 7 days
Kurtosis 3.8540276593359
Maximum Allowed Value 5 out of 5
Mean 1.6136 out of 5
Median 1 out of 5
Minimum Allowed Value 1 out of 5
Number of Changes 286
Number of Correlations 1409
Number of Measurements 790
Onset Delay 0 seconds
Standard Deviation 0.94448994753466
Unit 1 to 5 Rating
Variable ID 5744201
Variance 0.89206126099403

Tracking Energy

Record your Energy daily in the reminder inbox or using the interactive web or mobile notifications.

Tracking Steatorrhea

Record your Steatorrhea daily in the reminder inbox or using the interactive web or mobile notifications.
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https://lh6.googleusercontent.com/-BHr4hyUWqZU/AAAAAAAAAAI/AAAAAAAIG28/2Lv0en738II/photo.jpg Principal Investigator - Mike Sinn