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This individual's Anger is generally lowest after a daily total of 16 minutes of Meditation over the previous 7 days.
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Blue represents the sum of Meditation over the previous 7 days
An increase in 7 days cumulative Meditation is usually followed by an decrease in Anger. (R = -0.235)
Typical values for Anger following a given amount of Meditation over the previous 7 days.
Typical Meditation seen over the previous 7 days preceding the given Anger value.
This chart shows how your Meditation changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Meditation on each day of the week.
This chart shows the typical value recorded for Meditation for each month of the year.
This chart shows how your Anger changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Anger on each day of the week.
This chart shows the typical value recorded for Anger for each month of the year.

Abstract

This individual's Anger is generally 24.21% lower than normal after 16 minutes Meditation per 7 days. This individual's data suggests with a high degree of confidence (p=1.3788041616888E-5, 95% CI -0.574 to 0.104) that Meditation has a weakly negative predictive relationship (R=-0.24) with Anger. The highest quartile of Anger measurements were observed following an average 9 minutes Meditation per day. The lowest quartile of Anger measurements were observed following an average 15.375 min Meditation per day.Anger is generally 24.21% lower than normal after a total of 15 minutes of Meditation over the previous 7 days. Anger is generally 10.96% higher after a total of 9 minutes of Meditation over the previous 7 days.

Objective

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

Participant Instructions

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

Meditation Pre-Processing
Meditation measurement values below 0 microseconds were assumed erroneous and removed. Meditation measurement values above 7 days were assumed erroneous and removed. It was assumed that any gaps in Meditation data were unrecorded 0 microseconds measurement values.
Meditation Analysis Settings

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

Predictive Analytics
It was assumed that 0.5 hours would pass before a change in Meditation would produce an observable change in Anger. It was assumed that Meditation could produce an observable change in Anger for as much as 7 days after the stimulus event.
Predictive Analysis Settings

Data Quantity
873 raw Meditation measurements with 253 changes spanning 1840 days from 2014-04-29 to 2019-05-12 were used in this analysis. 156 raw Anger measurements with 73 changes spanning 1484 days from 2014-11-25 to 2018-12-19 were used in this analysis.

Data Sources

Meditation 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.

Anger 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 Meditation and Anger

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. 154 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Meditation 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 Meditation and Anger 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 Meditation
Effect Variable Name Anger
Sinn Predictive Coefficient 0.2261
Confidence Level high
Confidence Interval 0.33879982101146
Forward Pearson Correlation Coefficient -0.235
Critical T Value 1.646
Total Meditation Over Previous 7 days Before ABOVE Average Anger 9 minutes
Total Meditation Over Previous 7 days Before BELOW Average Anger 15 minutes
Duration of Action 7 days
Effect Size weakly negative
Number of Paired Measurements 154
Optimal Pearson Product 0.083979167586617
P Value 1.3788041616888E-5
Statistical Significance 0.71005659513709
Strength of Relationship 0.33879982101146
Study Type individual
Analysis Performed At 2019-05-14

Meditation Statistics

Property Value
Variable Name Meditation
Aggregation Method SUM
Analysis Performed At 2019-05-13
Duration of Action 7 days
Kurtosis 14.261492292546
Maximum Allowed Value 7 days
Mean 79 seconds
Median 0 microseconds
Minimum Allowed Value 0 microseconds
Number of Changes 253
Number of Correlations 280
Number of Measurements 873
Onset Delay 30 minutes
Standard Deviation 3.5489294855776
Unit Minutes
UPC 640791683572
Variable ID 85055
Variance 12.594900493602

Anger Statistics

Property Value
Variable Name Anger
Aggregation Method MEAN
Analysis Performed At 2019-04-06
Duration of Action 24 hours
Kurtosis 1.9605412643714
Maximum Allowed Value 5 out of 5
Mean 2.6526 out of 5
Median 2 out of 5
Minimum Allowed Value 1 out of 5
Number of Changes 73
Number of Correlations 706
Number of Measurements 156
Onset Delay 0 seconds
Standard Deviation 1.4064834957382
Unit 1 to 5 Rating
Variable ID 86779
Variance 1.9781958237841

Tracking Meditation

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

Tracking Anger

Record your Anger 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