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This individual's Facebook Likes is generally highest after an average of 1.9 out of 5 of Inflammatory Pain over the previous 7 days.
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Blue represents the mean of Inflammatory Pain over the previous 7 days
An increase in 7 days cumulative Inflammatory Pain is usually followed by an decrease in Facebook Likes. (R = -0.417)
Typical values for Facebook Likes following a given amount of Inflammatory Pain over the previous 7 days.
Typical Inflammatory Pain seen over the previous 7 days preceding the given Facebook Likes value.
This chart shows how your Inflammatory Pain changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Inflammatory Pain on each day of the week.
This chart shows the typical value recorded for Inflammatory Pain for each month of the year.
This chart shows how your Facebook Likes changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Facebook Likes on each day of the week.
This chart shows the typical value recorded for Facebook Likes for each month of the year.

Abstract

This individual's Facebook Likes is generally 50% higher than normal after an average of 1.9 out of 5 Inflammatory Pain over the previous 7 days. This individual's data suggests with a low degree of confidence (p=0.064448241023624, 95% CI -1.717 to 0.883) that Inflammatory Pain has a moderately negative predictive relationship (R=-0.42) with Facebook Likes. The highest quartile of Facebook Likes measurements were observed following an average 1.96 out of 5 Inflammatory Pain. The lowest quartile of Facebook Likes measurements were observed following an average 2.3363636363636 /5 Inflammatory Pain.Facebook Likes is generally 35% lower than normal after an average of 2.3363636363636 out of 5 of Inflammatory Pain over the previous 7 days. Facebook Likes is generally 50% higher after an average of 1.96 out of 5 of Inflammatory Pain over the previous 7 days.

Objective

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

Participant Instructions

Record your Inflammatory Pain daily in the reminder inbox or using the interactive web or mobile notifications.
Record your Facebook Likes 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

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

Facebook Likes Pre-Processing
Facebook Likes measurement values below 0 event were assumed erroneous and removed. No maximum allowed measurement value was defined for Facebook Likes. It was assumed that any gaps in Facebook Likes data were unrecorded 0 event measurement values.
Facebook Likes Analysis Settings

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

Data Quantity
93 raw Inflammatory Pain measurements with 37 changes spanning 87 days from 2013-01-12 to 2013-04-09 were used in this analysis. 343 raw Facebook Likes measurements with 438 changes spanning 2663 days from 2010-01-17 to 2017-05-02 were used in this analysis.

Data Sources

Inflammatory Pain 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.

Facebook Likes 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 moderately negative relationship between Inflammatory Pain and Facebook Likes

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. 51 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Inflammatory Pain 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 Inflammatory Pain and Facebook Likes 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 Inflammatory Pain
Effect Variable Name Facebook Likes
Sinn Predictive Coefficient 0.2523
Confidence Level low
Confidence Interval 1.3001961017415
Forward Pearson Correlation Coefficient -0.417
Critical T Value 1.671
Average Inflammatory Pain Over Previous 7 days Before ABOVE Average Facebook Likes 1.96 out of 5
Average Inflammatory Pain Over Previous 7 days Before BELOW Average Facebook Likes 2.336 out of 5
Duration of Action 7 days
Effect Size moderately negative
Number of Paired Measurements 51
Optimal Pearson Product 0.30117148458762
P Value 0.064448241023624
Statistical Significance 0.5231
Strength of Relationship 1.3001961017415
Study Type individual
Analysis Performed At 2019-04-04

Inflammatory Pain Statistics

Property Value
Variable Name Inflammatory Pain
Aggregation Method MEAN
Analysis Performed At 2019-01-28
Duration of Action 7 days
Kurtosis 2.7508536592149
Maximum Allowed Value 5 out of 5
Mean 2.202 out of 5
Median 2.2 out of 5
Minimum Allowed Value 1 out of 5
Number of Changes 37
Number of Correlations 242
Number of Measurements 93
Onset Delay 0 seconds
Standard Deviation 0.5272642923496
Unit 1 to 5 Rating
UPC 753970618156
Variable ID 1340
Variance 0.27800763398693

Facebook Likes Statistics

Property Value
Variable Name Facebook Likes
Aggregation Method SUM
Analysis Performed At 2019-01-27
Duration of Action 7 days
Kurtosis 51.827272422813
Mean 0.11522 event
Median 0 event
Minimum Allowed Value 0 event
Number of Changes 438
Number of Correlations 1275
Number of Measurements 343
Onset Delay 0 seconds
Standard Deviation 0.44430411233572
Unit Event
Variable ID 1883
Variance 0.19740614423843


Tracking Inflammatory Pain

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

Tracking Facebook Likes

Record your Facebook Likes 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