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This individual's Guiltiness is generally lowest after a daily total of 40 count of Vaping over the previous 7 days.
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Blue represents the sum of Vaping over the previous 7 days
An increase in 7 days cumulative Vaping is usually followed by an decrease in Guiltiness. (R = -0.02)
Typical values for Guiltiness following a given amount of Vaping over the previous 7 days.
Typical Vaping seen over the previous 7 days preceding the given Guiltiness value.
This chart shows how your Vaping changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Vaping on each day of the week.
This chart shows the typical value recorded for Vaping 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 1% lower than normal after 40 count Vaping per 7 days. This individual's data suggests with a medium degree of confidence (p=0.31766711803313, 95% CI -0.156 to 0.116) that Vaping has a very weakly negative predictive relationship (R=-0.02) with Guiltiness. The highest quartile of Guiltiness measurements were observed following an average 33.46 count Vaping per day. The lowest quartile of Guiltiness measurements were observed following an average 37.294820717131 count Vaping per day.Guiltiness is generally 1% lower than normal after a total of 37.294820717131 count of Vaping over the previous 7 days. Guiltiness is generally 1% higher after a total of 33.46 count of Vaping over the previous 7 days.

Objective

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

Participant Instructions

Record your Vaping 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

Vaping Pre-Processing
Vaping measurement values below 0 count were assumed erroneous and removed. No maximum allowed measurement value was defined for Vaping. It was assumed that any gaps in Vaping data were unrecorded 0 count measurement values.
Vaping 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 0.5 hours would pass before a change in Vaping would produce an observable change in Guiltiness. It was assumed that Vaping could produce an observable change in Guiltiness for as much as 7 days after the stimulus event.
Predictive Analysis Settings

Data Quantity
409 raw Vaping measurements with 95 changes spanning 602 days from 2017-06-09 to 2019-02-02 were used in this analysis. 2800 raw Guiltiness measurements with 661 changes spanning 1964 days from 2013-11-17 to 2019-04-04 were used in this analysis.

Data Sources

Vaping 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 negative relationship between Vaping 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. 443 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Vaping 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 Vaping 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 Vaping
Effect Variable Name Guiltiness
Sinn Predictive Coefficient 0.0196
Confidence Level medium
Confidence Interval 0.13605014061283
Forward Pearson Correlation Coefficient -0.02
Critical T Value 1.646
Total Vaping Over Previous 7 days Before ABOVE Average Guiltiness 33.46 count
Total Vaping Over Previous 7 days Before BELOW Average Guiltiness 37.295 count
Duration of Action 7 days
Effect Size very weakly negative
Number of Paired Measurements 443
Optimal Pearson Product 0.0039608638230373
P Value 0.31766711803313
Statistical Significance 0.9817
Strength of Relationship 0.13605014061283
Study Type individual
Analysis Performed At 2019-04-04

Vaping Statistics

Property Value
Variable Name Vaping
Aggregation Method SUM
Analysis Performed At 2019-03-30
Duration of Action 7 days
Kurtosis 1.9943943393172
Mean 4.2567 count
Median 7 count
Minimum Allowed Value 0 count
Number of Changes 95
Number of Correlations 151
Number of Measurements 409
Onset Delay 30 minutes
Standard Deviation 3.8066766546665
Unit Count
UPC 860175000102
Variable ID 5969050
Variance 14.490787153183

Guiltiness Statistics

Property Value
Variable Name Guiltiness
Aggregation Method MEAN
Analysis Performed At 2019-04-04
Duration of Action 24 hours
Kurtosis 3.1224153723756
Maximum Allowed Value 5 out of 5
Mean 2.1627 out of 5
Median 2 out of 5
Minimum Allowed Value 1 out of 5
Number of Changes 661
Number of Correlations 5379
Number of Measurements 2800
Onset Delay 0 seconds
Standard Deviation 0.92130141512025
Unit 1 to 5 Rating
Variable ID 1335
Variance 0.84879629750257

Tracking Vaping

Record your Vaping 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