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This individual's Guiltiness is generally lowest after an average of 37% recommended daily allowance of Iron intake over the previous 7 days.
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Blue represents the mean of Iron intake over the previous 7 days
An increase in 7 days cumulative Iron intake is usually followed by an increase in Guiltiness. (R = 0.091)
Typical values for Guiltiness following a given amount of Iron intake over the previous 7 days.
Typical Iron intake seen over the previous 7 days preceding the given Guiltiness value.
This chart shows how your Iron changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Iron on each day of the week.
This chart shows the typical value recorded for Iron for each month of the year.
This chart shows how your Guiltiness changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Guiltiness on each day of the week.
This chart shows the typical value recorded for Guiltiness for each month of the year.

Abstract

This individual's Guiltiness is generally -2% lower than normal after 37.285714285714% recommended daily allowance Iron per 7 days. This individual's data suggests with a medium degree of confidence (p=0.34330567778999, 95% CI -0.185 to 0.367) that Iron has a very weakly positive predictive relationship (R=0.09) with Guiltiness. The highest quartile of Guiltiness measurements were observed following an average 37.39% recommended daily allowance Iron. The lowest quartile of Guiltiness measurements were observed following an average 37.913387039989 %RDA Iron.Guiltiness is generally 2% lower than normal after an average of 37.913387039989% recommended daily allowance of Iron intake over the previous 7 days. Guiltiness is generally 1% higher after an average of 37.39% recommended daily allowance of Iron intake over the previous 7 days.

Objective

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

Participant Instructions

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

Iron Pre-Processing
Iron measurement values below 1% recommended daily allowance were assumed erroneous and removed. No maximum allowed measurement value was defined for Iron. No missing data filling value was defined for Iron so any gaps in data were just not analyzed instead of assuming zero values for those times.
Iron Analysis Settings

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

Predictive Analytics
It was assumed that 0 hours would pass before a change in Iron would produce an observable change in Guiltiness. It was assumed that Iron could produce an observable change in Guiltiness for as much as 7 days after the stimulus event.
Predictive Analysis Settings

Data Quantity
1591 raw Iron measurements with 477 changes spanning 2109 days from 2013-01-12 to 2018-10-22 were used in this analysis. 2783 raw Guiltiness measurements with 660 changes spanning 1958 days from 2013-11-17 to 2019-03-29 were used in this analysis.

Data Sources

Iron 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.

Guiltiness 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 very weakly positive relationship between Iron intake and Guiltiness

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. 196 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Iron intake 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 Iron intake and Guiltiness 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 Iron intake
Effect Variable Name Guiltiness
Sinn Predictive Coefficient 0.0905
Confidence Level medium
Confidence Interval 0.27617087155558
Forward Pearson Correlation Coefficient 0.091
Critical T Value 1.646
Average Iron intake Over Previous 7 days Before ABOVE Average Guiltiness 37.39% recommended daily allowance
Average Iron intake Over Previous 7 days Before BELOW Average Guiltiness 37.913% recommended daily allowance
Duration of Action 7 days
Effect Size very weakly positive
Number of Paired Measurements 196
Optimal Pearson Product -0.0016207374187768
P Value 0.34330567778999
Statistical Significance 0.9947
Strength of Relationship 0.27617087155558
Study Type individual
Analysis Performed At 2019-04-04

Iron Statistics

Property Value
Variable Name Iron
Aggregation Method MEAN
Analysis Performed At 2019-03-29
Duration of Action 7 days
Kurtosis 7.3136675136317
Mean 34.846% recommended daily allowance
Median 21% recommended daily allowance
Minimum Allowed Value 1% recommended daily allowance
Number of Changes 477
Number of Correlations 324
Number of Measurements 1591
Onset Delay 0 seconds
Standard Deviation 37.213795887807
Unit % Recommended Daily Allowance
UPC 050875818088
Variable ID 1357
Variance 1384.8666043794

Guiltiness Statistics

Property Value
Variable Name Guiltiness
Aggregation Method MEAN
Analysis Performed At 2019-03-29
Duration of Action 24 hours
Kurtosis 3.1184844591284
Maximum Allowed Value 5 out of 5
Mean 2.1634 out of 5
Median 2 out of 5
Minimum Allowed Value 1 out of 5
Number of Changes 660
Number of Correlations 5357
Number of Measurements 2783
Onset Delay 0 seconds
Standard Deviation 0.92179465572788
Unit 1 to 5 Rating
Variable ID 1335
Variance 0.84970538732848

Tracking Iron

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

Tracking Guiltiness

Record your Guiltiness 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