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This individual's Overall Mood is generally highest after an average of 0.17 applications of Wearing Makeup over the previous 7 days.
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Blue represents the mean of Wearing Makeup over the previous 7 days
An increase in 7 days cumulative Wearing Makeup is usually followed by an decrease in Overall Mood. (R = -0.223)
Typical values for Overall Mood following a given amount of Wearing Makeup over the previous 7 days.
Typical Wearing Makeup seen over the previous 7 days preceding the given Overall Mood value.
This chart shows how your Wearing Makeup changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Wearing Makeup on each day of the week.
This chart shows the typical value recorded for Wearing Makeup for each month of the year.
This chart shows how your Overall Mood changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Overall Mood on each day of the week.
This chart shows the typical value recorded for Overall Mood for each month of the year.

Abstract

This individual's Overall Mood is generally 4% higher than normal after an average of 0.1667 applications Wearing Makeup over the previous 7 days. This individual's data suggests with a low degree of confidence (p=0.24197072451914, 95% CI -0.741 to 0.295) that Wearing Makeup has a weakly negative predictive relationship (R=-0.22) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 0.19 applications Wearing Makeup. The lowest quartile of Overall Mood measurements were observed following an average 0.24489795918367 applications Wearing Makeup.Overall Mood is generally 4% lower than normal after an average of 0.24489795918367 applications of Wearing Makeup over the previous 7 days. Overall Mood is generally 4% higher after an average of 0.19 applications of Wearing Makeup over the previous 7 days.

Objective

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

Participant Instructions

Get GitHub here and use it to record your Wearing Makeup. Once you have a GitHub account, you can import your data from the Import Data page. This individual's data will automatically be imported and analyzed.
Record your Overall Mood 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

Wearing Makeup Pre-Processing
Wearing Makeup measurement values below 0 applications were assumed erroneous and removed. Wearing Makeup measurement values above 20 applications were assumed erroneous and removed. It was assumed that any gaps in Wearing Makeup data were unrecorded 0 applications measurement values.
Wearing Makeup Analysis Settings

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

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

Data Quantity
47 raw Wearing Makeup measurements with 23 changes spanning 58 days from 2012-07-25 to 2012-09-21 were used in this analysis. 13635 raw Overall Mood measurements with 1192 changes spanning 2522 days from 2012-05-06 to 2019-04-01 were used in this analysis.

Data Sources

Wearing Makeup data was primarily collected using GitHub. GitHub is the best place to share code with friends, co-workers, classmates, and complete strangers. Over four million people use GitHub to build amazing things together.

Overall Mood 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 Wearing Makeup and Overall Mood

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. 20 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Wearing Makeup 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 Wearing Makeup and Overall Mood 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 Wearing Makeup
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.0165
Confidence Level low
Confidence Interval 0.5175
Forward Pearson Correlation Coefficient -0.223
Critical T Value 1.725
Average Wearing Makeup Over Previous 7 days Before ABOVE Average Overall Mood 0.19 applications
Average Wearing Makeup Over Previous 7 days Before BELOW Average Overall Mood 0.245 applications
Duration of Action 7 days
Effect Size weakly negative
Number of Paired Measurements 20
Optimal Pearson Product 0.066852615843736
P Value 0.24197072451914
Statistical Significance 0.0742
Strength of Relationship 0.5175
Study Type individual
Analysis Performed At 2019-04-04

Wearing Makeup Statistics

Property Value
Variable Name Wearing Makeup
Aggregation Method MEAN
Analysis Performed At 2019-04-01
Duration of Action 7 days
Kurtosis 4.4927554842162
Maximum Allowed Value 20 applications
Mean 0.1573 applications
Median 0 applications
Minimum Allowed Value 0 applications
Number of Changes 23
Number of Correlations 33
Number of Measurements 47
Onset Delay 30 minutes
Standard Deviation 0.36614938594707
Unit Applications
UPC 883836474975
Variable ID 1339
Variance 0.13406537282942

Overall Mood Statistics

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2019-04-01
Duration of Action 24 hours
Kurtosis 6.8454669666675
Maximum Allowed Value 5 out of 5
Mean 2.9135 out of 5
Median 3 out of 5
Minimum Allowed Value 1 out of 5
Number of Changes 1192
Number of Correlations 4086
Number of Measurements 13635
Onset Delay 0 seconds
Standard Deviation 0.52653757307699
Unit 1 to 5 Rating
UPC 767674073845
Variable ID 1398
Variance 0.2772418158618

Tracking Wearing Makeup

Get GitHub here and use it to record your Wearing Makeup. Once you have a GitHub account, you can import your data from the Import Data page. This individual's data will automatically be imported and analyzed.

Tracking Overall Mood

Record your Overall Mood 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