This individual's Time Spent On Software Development is generally highest after an average of 21 index of Body Mass Index Or BMI over the previous 7 days.
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Blue represents the mean of Body Mass Index Or BMI over the previous 7 days
An increase in 7 days cumulative Body Mass Index Or BMI is usually followed by an decrease in Time Spent On Software Development. (R = -0.426)
Typical values for Time Spent On Software Development following a given amount of Body Mass Index Or BMI over the previous 7 days.
Typical Body Mass Index Or BMI seen over the previous 7 days preceding the given Time Spent On Software Development 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 Mass Index Or BMI changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Body Mass Index Or BMI on each day of the week.
This chart shows the typical value recorded for Body Mass Index Or BMI for each month of the year.
This chart shows how your Time Spent On Software Development changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Time Spent On Software Development on each day of the week.
This chart shows the typical value recorded for Time Spent On Software Development for each month of the year.

Abstract

This individual's Time Spent On Software Development is generally 24% higher than normal after an average of 20.87 index Body Mass Index Or BMI over the previous 7 days. This individual's data suggests with a high degree of confidence (p=1.9609755161251E-34, 95% CI -4.01 to 3.158) that Body Mass Index Or BMI has a moderately negative predictive relationship (R=-0.43) with Time Spent On Software Development. The highest quartile of Time Spent On Software Development measurements were observed following an average 20.88 index Body Mass Index Or BMI. The lowest quartile of Time Spent On Software Development measurements were observed following an average 21.26237037037 index Body Mass Index Or BMI.Time Spent On Software Development is generally 33% lower than normal after an average of 21.26237037037 index of Body Mass Index Or BMI over the previous 7 days. Time Spent On Software Development is generally 24% higher after an average of 20.88 index of Body Mass Index Or BMI over the previous 7 days.

Objective

The objective of this study is to determine the nature of the relationship (if any) between Body Mass Index Or BMI and Time Spent On Software Development. Additionally, we attempt to determine the Body Mass Index Or BMI values most likely to produce optimal Time Spent On Software Development values.

Participant Instructions

Get Fitbit here and use it to record your Body Mass Index Or BMI. 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 RescueTime here and use it to record your Time Spent On Software Development. Once you have a RescueTime 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 Mass Index Or BMI Pre-Processing
Body Mass Index Or BMI measurement values below 1 index were assumed erroneous and removed. No maximum allowed measurement value was defined for Body Mass Index Or BMI. No missing data filling value was defined for Body Mass Index Or BMI so any gaps in data were just not analyzed instead of assuming zero values for those times.
Body Mass Index Or BMI Analysis Settings

Time Spent On Software Development Pre-Processing
Time Spent On Software Development measurement values below 0 seconds were assumed erroneous and removed. Time Spent On Software Development measurement values above 7 days were assumed erroneous and removed. It was assumed that any gaps in Time Spent On Software Development data were unrecorded 0 seconds measurement values.
Time Spent On Software Development Analysis Settings

Predictive Analytics
It was assumed that 0 hours would pass before a change in Body Mass Index Or BMI would produce an observable change in Time Spent On Software Development. It was assumed that Body Mass Index Or BMI could produce an observable change in Time Spent On Software Development for as much as 7 days after the stimulus event.
Predictive Analysis Settings

Data Quantity
2355 raw Body Mass Index Or BMI measurements with 1756 changes spanning 2538 days from 2012-04-18 to 2019-03-31 were used in this analysis. 559 raw Time Spent On Software Development measurements with 564 changes spanning 682 days from 2016-06-03 to 2018-04-16 were used in this analysis.

Data Sources

Body Mass Index Or BMI data was primarily collected using Fitbit. Fitbit makes activity tracking easy and automatic.

Time Spent On Software Development data was primarily collected using RescueTime. Detailed reports show which applications and websites you spent time on. Activities are automatically grouped into pre-defined categories with built-in productivity scores covering thousands of websites and applications. You can customize categories and productivity scores to meet your needs.

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 Mass Index Or BMI and Time Spent On Software Development

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. 660 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Body Mass Index Or BMI 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 Body Mass Index Or BMI and Time Spent On Software Development 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 Mass Index Or BMI
Effect Variable Name Time Spent On Software Development
Sinn Predictive Coefficient 0.213
Confidence Level high
Confidence Interval 3.5836974099454
Forward Pearson Correlation Coefficient -0.426
Critical T Value 1.646
Average Body Mass Index Or BMI Over Previous 7 days Before ABOVE Average Time Spent On Software Development 20.88 index
Average Body Mass Index Or BMI Over Previous 7 days Before BELOW Average Time Spent On Software Development 21.262 index
Duration of Action 7 days
Effect Size moderately negative
Number of Paired Measurements 660
Optimal Pearson Product 0.26245140795631
P Value 1.9609755161251E-34
Statistical Significance 1
Strength of Relationship 3.5836974099454
Study Type individual
Analysis Performed At 2019-06-29

Body Mass Index Or BMI Statistics

Property Value
Variable Name Body Mass Index Or BMI
Aggregation Method MEAN
Analysis Performed At 2019-05-02
Duration of Action 7 days
Kurtosis 2.7834166526091
Mean 20.593 index
Median 20.532480239868 index
Minimum Allowed Value 1 index
Number of Changes 1756
Number of Correlations 2499
Number of Measurements 2355
Onset Delay 0 seconds
Standard Deviation 0.71676319189842
Unit Index
UPC 712038762439
Variable ID 1272
Variance 0.51374947326041

Time Spent On Software Development Statistics

Property Value
Variable Name Time Spent On Software Development
Aggregation Method SUM
Analysis Performed At 2019-03-30
Duration of Action 7 days
Kurtosis 3.9509291749464
Maximum Allowed Value 7 days
Mean 3 hours
Median 0 seconds
Minimum Allowed Value 0 seconds
Number of Changes 564
Number of Correlations 803
Number of Measurements 559
Onset Delay 0 seconds
Standard Deviation 4.5869899791775
Unit Hours
Variable ID 111632
Variance 21.040477069075

Tracking Body Mass Index Or BMI

Get Fitbit here and use it to record your Body Mass Index Or BMI. 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.

Tracking Time Spent On Software Development

Get RescueTime here and use it to record your Time Spent On Software Development. Once you have a RescueTime 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