Higher Business Predicts lower Overall Mood

Mike Sinn

Principal Investigator Mike Sinn

↑Higher Business Predicts ↓lower Overall Mood

cause image
gauge image
effect image


For most, Overall Mood is generally highest after a total of 8 minutes of Business over the previous 7 days.

Trait Correlation Between Business and Overall Mood

The chart above indicates that people with higher average Business usually have lower average Overall Mood. (R = -0.026269494470406)

Business Hours Distribution

A frequency distribution displays the frequency of various values. Each column represents the number of the occurrences of a value.

Average Business Hours by Day of Week

Average Business Hours by Month

This chart shows the typical value recorded for Business Hours for each month of the year.

Overall Mood Distribution

A frequency distribution displays the frequency of various values. Each column represents the number of the occurrences of a value.

Average Overall Mood by Day of Week

Average Overall Mood by Month

This chart shows the typical value recorded for Overall Mood for each month of the year.



Abstract

Aggregated data from 23 study participants suggests with a medium degree of confidence (p=0.19060321090787, 95% CI -3.326 to 3.239) that Business Hours has a very weakly negative predictive relationship (R=-0.0435) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 8 minutes Business Hours per day. The lowest quartile of Overall Mood measurements were observed following an average 0.13257103353231 h Business Hours per day.

Objective

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

Participant Instructions

Get RescueTime here and use it to record your Business. Once you have a RescueTime account, you can import your data from the Import Data page. Your 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 23 participants. Thus, the study design is equivalent to the aggregation of 23 separate n=1 observational natural experiments.

Data Analysis

It was assumed that 0 hours would pass before a change in Business Hours would produce an observable change in Overall Mood. It was assumed that Business Hours could produce an observable change in Overall Mood for as much as 7 days after the stimulus event.

Data Sources

The QuantiModo platform was used to aggregate data from these data sources.

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:
1. Strength (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.
2. Consistency (reproducibility): Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.
3. 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.
4. 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).
5. 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.
6. Plausibility: A plausible mechanism between cause and effect is helpful.
7. Coherence: Coherence between epidemiological and laboratory findings increases the likelihood of an effect.
8. Experiment: Occasionally it is possible to appeal to experimental evidence.
9. Analogy: The effect of similar factors may be considered.
The confidence in a causal relationship is bolstered by the fact that time-precedence was taken into account in all calculations. Furthermore, in accordance with the law of large numbers (LLN), the predictive power and accuracy of these results will continually grow over time. 90 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Business Hours 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. Furthermore, it will be very enlightening to aggregate this data with the data from other participants with similar genetic, diseasomic, environmentomic, and demographic profiles.


Relationship Statistics

Property Value
Cause Variable Name Business
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.023226115304077
Confidence Level medium
Confidence Interval 3.2824418830212
Forward Pearson Correlation Coefficient -0.0435
Critical T Value 1.6784099965731
Total Business Over Previous 7 days Before ABOVE Average Overall Mood 8 minutes
Total Business Over Previous 7 days Before BELOW Average Overall Mood 8 minutes
Duration of Action 7 days
Effect Size very weakly negative
Number of Paired Measurements 90
Optimal Pearson Product 0.047480828039542
P Value 0.19060321090787
Statistical Significance 0.5163
Strength of Relationship 3.2824418830212
Study Type population
Analysis Performed At 2017-12-12
Number of Participants 23


Business Statistics

Property Value
Variable Name Business Hours
Aggregation Method SUM
Analysis Performed At 2018-11-16
Duration of Action 7 days
Kurtosis 59.500351521425
Maximum Allowed Value 7 days
Mean 18 minutes
Median 12 minutes
Minimum Allowed Value 0 seconds
Number of Correlations 502
Number of Measurements 20182
Onset Delay 0 seconds
Standard Deviation 0.31785669146198
Unit Hours
UPC 039956980715
Variable ID 111572
Variance 0.2018559422041


Overall Mood Statistics

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2018-11-17
Duration of Action 24 hours
Kurtosis 3.7416256154524
Maximum Allowed Value 5 out of 5
Mean 3.1155337108594 out of 5
Median 3.1365425423606 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 1151
Number of Measurements 606419
Onset Delay 0 seconds
Standard Deviation 0.57006455569065
Unit 1 to 5 Rating
UPC 767674073845
Variable ID 1398
Variance 0.4399449452354

Leave a Reply

Your email address will not be published. Required fields are marked *