DATA 621 Blog 1: Descriptive Statistics in Daily Spending:

Descriptive Statistics

Descriptive statistics are used to summarize and understand the main features of a dataset. Rather than modeling or predicting outcomes, descriptive statistics help answer basic questions such as what is typical, how spread out the data is, and whether extreme values exist.

In daily life, descriptive statistics are useful for understanding personal habits, such as spending. Looking at averages alone can sometimes be misleading, especially when a few unusually large expenses occur. Measures such as the median and standard deviation provide additional context that helps describe typical behavior more accurately.

R Example

Below is a simple example representing daily spending over a two-week period.

# Daily spending amounts in dollars
spending <- c(12, 15, 10, 18, 14, 13, 11, 60, 16, 14, 12, 15, 13, 17)
spending
##  [1] 12 15 10 18 14 13 11 60 16 14 12 15 13 17

Summary Statistics

mean(spending)
## [1] 17.14286
median(spending)
## [1] 14
sd(spending)
## [1] 12.53829
summary(spending)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   10.00   12.25   14.00   17.14   15.75   60.00

Interpretation

The mean spending value is influenced by the one unusually high expense, while the median better reflects a typical day. The standard deviation shows how much spending varies from day to day. This highlights why it is important to examine multiple descriptive measures rather than relying on a single statistic.

Conclusion

Descriptive statistics provide a simple but powerful way to understand everyday data. In personal finance, they help distinguish between normal behavior and occasional extremes, allowing for more realistic budgeting and planning.