Based on data from 9 participants, Body Weight is generally highest after an average of 2.3 out of 5 of Guiltiness over the previous 24 hours.


Abstract
Aggregated data from 9 study participants suggests with a low degree of confidence (p=0.14143128384182, 95% CI 2.233 to 2.07) that Guiltiness has a very weakly negative predictive relationship (R=0.08) with Body Weight. The highest quartile of Body Weight measurements were observed following an average 2.05 out of 5 Guiltiness. The lowest quartile of Body Weight measurements were observed following an average 1.8920994109435 /5 Guiltiness.
Objective
The objective of this study is to determine the nature of the relationship (if any) between Guiltiness and Body Weight. Additionally, we attempt to determine the Guiltiness values most likely to produce optimal Body Weight values.
Participant Instructions
Record your Guiltiness daily in the reminder inbox or using the interactive web or mobile notifications.
Get Fitbit here and use it to record your Body Weight. Once you have a Fitbit account, you can import your data from the Import Data page. This individual's data will automatically be imported and analyzed.
Get Fitbit here and use it to record your Body Weight. Once you have a Fitbit account, you can import your data from the Import Data page. This individual's data will automatically be imported and analyzed.
Design
This study is based on data donated by 9 participants. Thus, the study design is equivalent to the aggregation of 9 separate n=1 observational natural experiments.
Data Analysis
Guiltiness PreProcessing
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.
Body Weight PreProcessing
Body Weight measurement values below 0 pounds were assumed erroneous and removed. Body Weight measurement values above 1000 pounds were assumed erroneous and removed. No missing data filling value was defined for Body Weight so any gaps in data were just not analyzed instead of assuming zero values for those times.
Predictive Analytics
It was assumed that 0 hours would pass before a change in Guiltiness would produce an observable change in Body Weight. It was assumed that Guiltiness could produce an observable change in Body Weight for as much as 1 days after the stimulus event.
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.

Body Weight PreProcessing
Body Weight measurement values below 0 pounds were assumed erroneous and removed. Body Weight measurement values above 1000 pounds were assumed erroneous and removed. No missing data filling value was defined for Body Weight so any gaps in data were just not analyzed instead of assuming zero values for those times.

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

Data Sources
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.
Body Weight data was primarily collected using Fitbit. Fitbit makes activity tracking easy and automatic.
Body Weight data was primarily collected using Fitbit. Fitbit makes activity tracking easy and automatic.
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 nonexistent 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 Guiltiness and Body Weight
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. 81 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Guiltiness 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 timeprecedence 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 biochemical 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 Guiltiness and Body Weight 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.
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 Guiltiness and Body Weight
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. 81 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Guiltiness 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 timeprecedence 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 biochemical 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 Guiltiness and Body Weight 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  Guiltiness 
Effect Variable Name  Body Weight 
Sinn Predictive Coefficient  0.0045360726390021 
Confidence Level  low 
Confidence Interval  2.1515411408584 
Forward Pearson Correlation Coefficient  0.0815 
Critical T Value  1.6997777777778 
Average Guiltiness Over Previous 24 hours Before ABOVE Average Body Weight  2.05 out of 5 
Average Guiltiness Over Previous 24 hours Before BELOW Average Body Weight  1.8920994109435 out of 5 
Duration of Action  24 hours 
Effect Size  very weakly negative 
Number of Paired Measurements  81 
Optimal Pearson Product  0.1824793341941 
P Value  0.14143128384182 
Statistical Significance  0.32136666754054 
Strength of Relationship  2.1515411408584 
Study Type  population 
Analysis Performed At  20190514 
Number of Participants  9 
Guiltiness Statistics
Property  Value 

Variable Name  Guiltiness 
Aggregation Method  MEAN 
Analysis Performed At  20190326 
Duration of Action  24 hours 
Kurtosis  3.7212578791298 
Maximum Allowed Value  5 out of 5 
Mean  2.315524127182 out of 5 
Median  2.249596093161 out of 5 
Minimum Allowed Value  1 out of 5 
Number of Correlations  631 
Number of Measurements  31791 
Onset Delay  0 seconds 
Standard Deviation  0.74731995879789 
Unit  1 to 5 Rating 
Variable ID  1335 
Variance  0.8465948884929 
Body Weight Statistics
Property  Value 

Variable Name  Body Weight 
Aggregation Method  MEAN 
Analysis Performed At  20190202 
Duration of Action  7 days 
Kurtosis  25.470204651548 
Maximum Allowed Value  1000 pounds 
Mean  168.97283090379 pounds 
Median  169.42999976732 pounds 
Minimum Allowed Value  0 pounds 
Number of Correlations  966 
Number of Measurements  76334 
Onset Delay  0 seconds 
Standard Deviation  184.51819331854 
Unit  Pounds 
UPC  875011003902 
Variable ID  1486 
Variance  8495258.0892244 
Tracking Guiltiness
Record your Guiltiness daily in the reminder inbox or using the interactive web or mobile notifications.Tracking Body Weight
Get Fitbit here and use it to record your Body Weight. Once you have a Fitbit account, you can import your data from the Import Data page. This individual's data will automatically be imported and analyzed.

Principal Investigator  Mike Sinn