For most, Productivity is generally highest after an average of 15 degrees celsius of Outdoor Temperature over the previous 7 days.
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People with higher Outdoor Temperature usually have lower Productivity
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Outdoor Temperature on each day of the week.
This chart shows the typical value recorded for Outdoor Temperature for each month of the year.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Productivity on each day of the week.
This chart shows the typical value recorded for Productivity for each month of the year.

Abstract

Aggregated data from 10 study participants suggests with a medium degree of confidence (p=0.071900852439639, 95% CI -4.292 to 4.062) that Outdoor Temperature has a weakly negative predictive relationship (R=-0.12) with Productivity. The highest quartile of Productivity measurements were observed following an average 14.49 degrees celsius Outdoor Temperature. The lowest quartile of Productivity measurements were observed following an average 17.861240969723 C Outdoor Temperature.

Objective

The objective of this study is to determine the nature of the relationship (if any) between Outdoor Temperature and Productivity. Additionally, we attempt to determine the Outdoor Temperature values most likely to produce optimal Productivity values.

Participant Instructions

Record your Outdoor Temperature daily in the reminder inbox or using the interactive web or mobile notifications.
Get RescueTime here and use it to record your Productivity. Once you have a RescueTime account, you can import your data from the Import Data page. Your data will automatically be imported and analyzed.

Design

This study is based on data donated by 10 participants. Thus, the study design is equivalent to the aggregation of 10 separate n=1 observational natural experiments.

Data Analysis

Outdoor Temperature Pre-Processing
No minimum allowed measurement value was defined for Outdoor Temperature. No maximum allowed measurement value was defined for Outdoor Temperature. No missing data filling value was defined for Outdoor Temperature so any gaps in data were just not analyzed instead of assuming zero values for those times.
Outdoor Temperature Analysis Settings

Productivity Pre-Processing
No minimum allowed measurement value was defined for Productivity. No maximum allowed measurement value was defined for Productivity. No missing data filling value was defined for Productivity so any gaps in data were just not analyzed instead of assuming zero values for those times.
Productivity Analysis Settings

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

Data Sources

Outdoor Temperature 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.

Productivity 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 correlation. 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 Outdoor Temperature and Productivity

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. 242 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Outdoor Temperature 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 0 feel that any relationship observed between Outdoor Temperature and Productivity 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 Outdoor Temperature
Effect Variable Name Productivity
Sinn Predictive Coefficient 0.18598030982975
Confidence Level medium
Confidence Interval 4.1766306574807
Forward Pearson Correlation Coefficient -0.115
Critical T Value 1.6673
Average Outdoor Temperature Over Previous 7 days Before ABOVE Average Productivity 14.49 degrees celsius
Average Outdoor Temperature Over Previous 7 days Before BELOW Average Productivity 17.861240969723 degrees celsius
Duration of Action 7 days
Effect Size weakly negative
Number of Paired Measurements 242
Optimal Pearson Product 0.23166109765331
P Value 0.071900852439639
Statistical Significance 0.69183999896049
Strength of Relationship 4.1766306574807
Study Type population
Analysis Performed At 2019-01-29
Number of Participants 10

Outdoor Temperature Statistics

Property Value
Variable Name Outdoor Temperature
Aggregation Method MEAN
Analysis Performed At 2019-01-20
Duration of Action 7 days
Kurtosis 2.4332473665576
Mean 16.083846437995 degrees celsius
Median 16.014981774659 degrees celsius
Number of Correlations 202
Number of Measurements 1201641
Onset Delay 0 seconds
Standard Deviation 6.7535527508374
Unit Degrees Celsius
Variable ID 5954773
Variance 61.12688730403

Productivity Statistics

Property Value
Variable Name Productivity
Aggregation Method MEAN
Analysis Performed At 2018-12-22
Duration of Action 7 days
Kurtosis 5.0137749648511
Mean 53.862130434783 percent
Median 55.003636878917 percent
Number of Correlations 356
Number of Measurements 73351
Onset Delay 0 seconds
Standard Deviation 12.036213501182
Unit Percent
Variable ID 1876
Variance 216.35129348244

https://lh6.googleusercontent.com/-BHr4hyUWqZU/AAAAAAAAAAI/AAAAAAAIG28/2Lv0en738II/photo.jpg Principal Investigator - Mike Sinn