Today, my primary focus was on analyzing the distribution of the ‘age’ variable. As depicted in the density plot of the ‘age’ variable, it’s evident that this column, representing the ages of 7,499 individuals (non-null values), displays a positive skew. This skewness indicates that the majority of individuals in the dataset tend to be on the younger side, resulting in a right-tailed distribution.
The average age stands at approximately 37.21 years, with a moderate level of variability around this mean, as evidenced by a standard deviation of 12.98. The age range spans from 2 to 92 years, encompassing a diverse age demographic. The youngest individual in the dataset is 2 years old, while the oldest is 92 years old. With a kurtosis value of 0.234, the distribution appears to be somewhat less peaked than a normal distribution, signifying a dispersion of ages rather than a tight clustering around the mean. Additionally, the median age, which falls at 35 years, serves as the midpoint of the dataset.
Moving on to the box plot representation of the ‘age’ variable, it’s apparent that outliers beyond the upper whisker are present. In a box plot, the ‘whiskers’ typically indicate the range within which most of the data points fall. Any data point lying beyond these whiskers is considered an outlier, signifying that it deviates significantly from the typical range of values.
In this specific case, the upper whisker of the box plot extends to a threshold typically defined as 1.5 times the interquartile range (IQR) above the third quartile (Q3). Data points beyond this threshold are identified as outliers. The presence of outliers beyond the upper whisker in the ‘age’ variable suggests that there are individuals in the dataset whose ages significantly exceed the upper age range found within the ‘typical’ or ‘normal’ population.