R Notebook

library(gtsummary)

library(dplyr)

##UKDriverDeaths

data("UKDriverDeaths")
View(UKDriverDeaths)

summary(UKDriverDeaths)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1057    1462    1631    1670    1851    2654 

str(UKDriverDeaths)

 Time-Series [1:192] from 1969 to 1985: 1687 1508 1507 1385 1632 ...

summary(UKDriverDeaths)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1057    1462    1631    1670    1851    2654 

    str(UKDriverDeaths)

 Time-Series [1:192] from 1969 to 1985: 1687 1508 1507 1385 1632 ...

    plot(UKDriverDeaths)

Interpretation

Interpretation of the Distribution

Skewed Distribution: The fact that the mean (1670) is higher than the median (1631) suggests the distribution is right-skewed. This means that there are a small number of months with significantly high death counts, pulling the mean above the median.

Range of Deaths: The deaths range from a minimum of 1057 to a maximum of 2654, indicating a substantial variation in the number of deaths over the periods being analyzed.

Interquartile Range (IQR): The interquartile range (IQR) is the difference between the third quartile and the first quartile, i.e., 1851 - 1462 = 389. This tells us that the middle 50% of the data falls within a range of 389 deaths. This is a relatively wide range, suggesting moderate variability in the number of deaths.

Key Insights

The central 50% of the data (between the 1st and 3rd quartiles) spans from 1462 to 1851 deaths.

The mean being higher than the median suggests that extreme values (outliers with higher deaths) are influencing the dataset.

The maximum value of 2654 deaths is significantly higher than the 3rd quartile, suggesting there could be months with unusually high death counts.

Further Investigation

To better understand the cause of the skewness, it would be useful to:

Visualize the data: A histogram or boxplot could reveal the distribution shape more clearly and highlight any outliers.

Examine potential outliers: Investigate the months or periods with extremely high death counts to understand why these extreme values exist.

Check for seasonal patterns: If the data is monthly, certain months might consistently report higher or lower deaths due to seasonal factors (e.g., weather conditions, holidays, etc.).