Identifying Seasonal Effects in Sales Data

Seasonality is a key concept in time-series analysis that refers to periodic fluctuations in data. These changes can occur at regular intervals, such as monthly or quarterly, due to seasonal factors. Understanding seasonality can greatly enhance the accuracy of forecasts. In a given analysis of sales data for a retail store, it has been observed that sales tend to peak during the holiday season every December.

Consider the following data points for the monthly sales (in thousands of dollars) over one year:

January: 30
February: 25
March: 35
April: 40
May: 50
June: 70
July: 60
August: 45
September: 40
October: 55
November: 70
December: 90

Which month shows the most significant seasonal effect based on the sales data?