This individual's Code Commits is generally highest after an average of 160 pounds of Body Weight over the previous 7 days.
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Blue represents the mean of Body Weight over the previous 7 days
An increase in 7 days cumulative Body Weight is usually followed by an decrease in Code Commits. (R = -0.379)
Typical values for Code Commits following a given amount of Body Weight over the previous 7 days.
Typical Body Weight seen over the previous 7 days preceding the given Code Commits value.
Correlation between outcome and aggregated predictor measurements over given number of days
Peak correlation suggests the delay between predictor and observable outcome
This chart shows how your Body Weight changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Body Weight on each day of the week.
This chart shows the typical value recorded for Body Weight for each month of the year.
This chart shows how your Code Commits changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Code Commits on each day of the week.
This chart shows the typical value recorded for Code Commits for each month of the year.

Abstract

This individual's Code Commits is generally 16.49% higher than normal after an average of 157.6 pounds Body Weight over the previous 7 days. This individual's data suggests with a high degree of confidence (p=0.0016935490483299, 95% CI -271.003 to 270.245) that Body Weight has a moderately negative predictive relationship (R=-0.38) with Code Commits. The highest quartile of Code Commits measurements were observed following an average 157.79 pounds Body Weight. The lowest quartile of Code Commits measurements were observed following an average 160.09921875 lb Body Weight.Code Commits is generally 20.47% lower than normal after an average of 160.09921875 pounds of Body Weight over the previous 7 days. Code Commits is generally 16.49% higher after an average of 157.79 pounds of Body Weight over the previous 7 days.

Objective

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

Participant Instructions

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

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

Body Weight Pre-Processing
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.
Body Weight Analysis Settings

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

Predictive Analytics
It was assumed that 0 hours would pass before a change in Body Weight would produce an observable change in Code Commits. It was assumed that Body Weight could produce an observable change in Code Commits for as much as 7 days after the stimulus event.
Predictive Analysis Settings

Data Quantity
169 raw Body Weight measurements with 58 changes spanning 291 days from 2018-08-07 to 2019-05-25 were used in this analysis. 92351 raw Code Commits measurements with 1715 changes spanning 2168 days from 2013-07-19 to 2019-06-27 were used in this analysis.

Data Sources

Body Weight data was primarily collected using Withings. Withings creates smart products and apps to take care of yourself and your loved ones in a new and easy way. Discover the Withings Pulse, Wi-Fi Body Scale, and Blood Pressure Monitor.

Code Commits 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.

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 Body Weight and Code Commits

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. 130 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Body Weight 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, 2 humans feel that there is a plausible mechanism of action and 3 feel that any relationship observed between Body Weight and Code Commits 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 Body Weight
Effect Variable Name Code Commits
Sinn Predictive Coefficient 0.4134
Confidence Level high
Confidence Interval 270.62440742747
Forward Pearson Correlation Coefficient -0.379
Critical T Value 1.646
Average Body Weight Over Previous 7 days Before ABOVE Average Code Commits 157.79 pounds
Average Body Weight Over Previous 7 days Before BELOW Average Code Commits 160.099 pounds
Duration of Action 7 days
Effect Size moderately negative
Number of Paired Measurements 130
Optimal Pearson Product 0.17771443028962
P Value 0.0016935490483299
Statistical Significance 0.91937077794664
Strength of Relationship 270.62440742747
Study Type individual
Analysis Performed At 2019-06-28

Body Weight Statistics

Property Value
Variable Name Body Weight
Aggregation Method MEAN
Analysis Performed At 2019-05-27
Duration of Action 7 days
Kurtosis 3.8946423228776
Maximum Allowed Value 1000 pounds
Mean 157.17 pounds
Median 157 pounds
Minimum Allowed Value 0 pounds
Number of Changes 58
Number of Correlations 2560
Number of Measurements 169
Onset Delay 0 seconds
Standard Deviation 2.960453211677
Unit Pounds
UPC 875011003902
Variable ID 1486
Variance 8.7642832185285

Code Commits Statistics

Property Value
Variable Name Code Commits
Aggregation Method SUM
Analysis Performed At 2019-06-28
Duration of Action 7 days
Kurtosis 96.712837218052
Mean 41.995 event
Median 15 event
Minimum Allowed Value 0 event
Number of Changes 1715
Number of Correlations 3125
Number of Measurements 92351
Onset Delay 0 seconds
Standard Deviation 95.12285039468
Unit Event
Variable ID 5955693
Variance 9048.3566672087

Tracking Body Weight

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

Tracking Code Commits

Get GitHub here and use it to record your Code Commits. 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.
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https://lh6.googleusercontent.com/-BHr4hyUWqZU/AAAAAAAAAAI/AAAAAAAIG28/2Lv0en738II/photo.jpg Principal Investigator - Mike Sinn