One day. Twenty four hours. Every single day, people must decide how to split up the twenty four hours they are given. Our goal for this project was to determine how a person’s time usage is related to various factors such as wellbeing, intelligence, success, and happiness. Analyzing this data provides information about what people can do to make the most of their time. Although we didn’t focus on it for this project, analyzing the amount of time spent on different activities per day can also increase awareness to what can feasibly be done in one day, making it useful for time management and scheduling.
General Data
The above choropleths make it easy to visualize general statistics regarding IQ, obesity, and GDP per capita that we used in our project. Further down, we will look for correlations between these statistics and the time spent on relevant activities.
IQ
An intelligence quotient, more commonly known as an IQ, is one of the most popular methods of assessing human intelligence. Intuitively, it might seem that more time spent in school would correspond to a higher IQ. But according to the data, it seems that there is very little correlation between these two variables. The coefficient of determination of the linear regression model is 0.000526, which implies an almost nonexistent relationship. This must be taken with a grain of salt, however, because the data only takes into account the time spent in school- not necessarily the time spent studying. It also includes people of all ages, not only students who are currently attending school. What we can take from the data is that the median IQ score is about 97.49.
Intuitively, it might also seem that more time spent working would correspond to a higher national IQ, due to an assumed greater national wealth. Though the plot shows a slightly positive relationship between the two, the coefficient of determination of the linear regression model remains low, just 0.034403. Therefore, we can not conclude that average time spent working has much of a correlation to national IQ.
Obesity
The above plots explore the relationships between the time spent sleeping, exercising, eating, and partaking in leisure versus obesity rates. There seems to be a relatively strong correlation between time spent sleeping and obesity rates. A greater amount of time spent asleep could indicate laziness and an increased risk of obesity. On the other hand, there seems to be a relatively strong negative relationship between time spent exercising and obesity rates. This makes sense- the more a person exercises, the less likely they are going to be overweight. Surprisingly, there also seems to be a negative relationship between time spent eating and obesity rates. But breaking it down, this does make sense. Eating slower is associated with eating less, which results in less weight gain. When people are busy, they tend to go for unhealthy, fast food options that can be eaten on-the-go, which results in weight gain. In addition, many countries whose cultures are largely based around food (and socializing during meal time) also place great emphasis on staying active. There is also a negative correlation between leisure time and obesity rates. This also makes sense- more leisure time typically equals less stress, more time to take care of oneself, eat healthy, exercise, and sleep, all of which equal less weight gain.
Wealth
GDP per capita is calculated by dividing a country’s gross domestic product by the population. It is used to determine the country’s standard of living. We were interested in whether the average amount of leisure time in a day plays a role in the prosperity of a nation. According to the plot, it seems that there is very little correlation between these two variables. The coefficient of determination of the linear regression model is 0.030577, which implies a very weak relationship.
Similarly, we were interested in whether the average amount of time spent working each day played a role in a nation’s prosperity. According to the plot, it again appears there is very little correlation between these two variables. The coefficient of determination of the linear regression model is 0.025327, which implies a very weak relationship.
Life Expectancy
Possibly one of the most important considerations when divvying up one’s time is determining what activities will result in a longer lifespan. Here we set out to determine whether leisure, socializing, school, or work had any major implications on life expectancy. Based on the plots, there appears to be a weak relationship between leisure time and time spent socializing versus life expectancy. As mentioned earlier, leisure time has many positive effects, so this relationship is understandable. Since humans are social creatures and rely on one another to survive, the relationship between leisure and life expectancy is also understandable. There does not seem to be any relationship between the time spent in school or at work versus life expectancy.
Life Satisfaction
Another important consideration when deciding how to spend one’s time is determining what activities will make a person happy. Again, we used leisure, socializing, school, and work statistics. Life satisfaction was rated on a scale from 1 to 10. Based on the plots, there appears to be a weak positive relationship between time spent socializing and life expectancy. Again, we recognize that humans are social creatures. There appears to be a weak negative relationship between time spent in school and life satisfaction, however, the school data does come with limitations, as mentioned earlier. There does not seem to be any relationship between leisure or time spent at work versus life expectancy.
Gender: Life Expectancy vs. Life Satisfaction
We choose to dig deeper into the life expectancy and life satisfaction data and compare the differences between males and females. In the bar plot above, there is a pretty clear pattern. Across every country, the average female life expectancy is higher than the average male life expectancy. Overall, Japan has the highest life expectancy while South Africa has the lowest.
In the bar plot above, we again see that females tend to have a higher life satisfaction than males- in 19 of the 41 countries (excluding the OECD- total) women gave a higher score than men. In comparison, 14 of the 41 countries had men give a higher score. In 8 countries, men and women had the same life satisfaction. Overall, Finland has the highest life satisfaction, while South Africa and Turkey have the lowest.