At the end of 2021, I observed around 400 days' worth of recorded data. Various analyses could be performed with such data and wanted to test the relationships among the variables. For example, I wanted to know how meditation affected my lifestyle and other variables, such as screen time or the total percentage of the day. By finding various cause-and-effect relationships among variables, I was able to derive meaningful insights about my lifestyle.
At the end of every month, an evaluation is done to analyze my lifestyle. These are all of the past assessments beginning from February to November of 2021. As shown above, one can observe improvements in the wake-up time and total percentage as time progresses.
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Below visualizations represent meaningful relationships within variables. By utilizing Pearson's correlation coefficient, various statistical relationships can be observed. When the test between two variables results in a p-value less than 0.05 (meaning there is only a 5% chance that they are statistically unrelated), it is considered statistically significant. Thus, when the p-value gets closer to 0, it demonstrates a stronger relationship between tested variables. As shown below, a red line represents a p-value of 0.05, so if a colored line is above the red line, it is most likely statistically significant.
Note that since there are numerous variables, it may seem a little messy; however, the purpose of these visualizations is to grasp a general depiction of the meaningful relationships of the data. Thus, one can merely consider them as a map for further analysis.
Observations found in November
Observations found in All Data (400+)
Proving the hypothesis: Relationship between Meditation & Screen time
Previously in 2.1, observations found in the November correlation plot led to a hypothesis that the meditation variable negatively correlates with the Screen time variable. Thus, the above code can help to evaluate such an assumption further.
As can be seen above, the first graph demonstrate the relationship between corresponding variables. It can be observed that the p-value is 0.03077 and the stat value is -0.451.
The stat value is the correlation coefficient between two given variables, which illustrates a positive correlation as the value approaches 1, and a negative correlation as it nears -1. In this case, since the calculated stat value is -0.451, Meditation and Screen time variables are negatively related as predicted.
Every month, my lifestyle is evaluated to modify or improve my daily habits. Through the correlation tests of the numerous variables and data observation, various insights emerge that help me understand my weaknesses and strengths to always strive for a better version of myself. In the future, these evaluation processes will be automated and will also provide me answers to the question of "how" I can push myself for a healthier lifestyle.
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