When data under analysis has a seasonal influence to it, further investigation cannot continue without deseasonalising the data. This involves calculating seasonal indices to tell us how a particular season (day/month/quarter) compares to the average season.

## Interpreting seasonal indices

Seasonal indices have an average value of 1. This can be converted into a percentage for easier interpretation. A seasonal index of 1.3 (or 130%) would indicate that that season had 30% more than the seasonal average. An example is where Christopher works all throughout the year at a ice-cream shop and earns an average of \$100,000 a season for it. If the seasonal index for summer was 1.5, then that means Christopher earns 50% more than the average \$100,000.  Likewise a seasonal index of 0.6 in winter would indicate that Christopher earns 40% less than the seasonal average.

## Calculating seasonal index

A season index is defined by:

$seasonal index = \dfrac{value for season}{seasonal average}$

Note: the sum of the seasonal indices equals the number of seasons.

## Using seasonal indices to deseasonalise the data

In order to remove the seasonal component of a time series, one must divide the amount by the seasonal index.

$deseasonalised figure = \dfrac{actual figure}{seasonal index}$