Nayland College - Mathematics Home . Year 9 . Year 10 . Level 1 . Level 2 . L3 Statistics . L3 Maths . L3 Calculus . About . Links

# Recomposing data

 5 Recompose the data Seasonal Decomposition and Forecasting, Part I (Auckland Uni) - Tourist visitors to NZ from different countries notes, - Alcohol exemplar - class work, Worksheet & Google Doc - Homework Travel Destination- Google doc template (Merit) Recomposing the data into trend + seasonal to establish data values above and below 'average'. Comparing the Raw data with the 'Recomposed Data' to establish data values above and below 'average' and comment. Discussion MUST be in CONTEXT (Excellence) Are there patterns in the recomposed data. Investigate & compare the additive and multiplicative models Exemplar: Recomposing From the data sets available on statslc.com EXEMPLARS: Use iNZight to produce a time series graph and add the trend line. Copy this into your report and discuss - Alcohol Consumptn: Data csv,| Word Worksheet | Google Doc class write up - Travel Destination Data csv | Word Write up | Google Doc class work - Travel Purposes: Data csv - Electricity Production: Data csv | (Website) Videos - Recomposing

Recompose the time series data.

Trend line + Estimated Seasonal Effect

Because the seasonal component increases in range over the time series a multiplicative model would be better. iNZight is an additive model, so it suggests that the early seasonal cycles are below average, and later seasonal cycles are larger than average - but we knew that.

For Excellence don't discuss this incorrectly

This can be seen by the Residuals being larger at either ends of the time range, and less in the centre.

Another example of this:

Total numbers of visitors to NZ: the seasonal component increases in range from 1999 to 2002, then similar seasonal cycles from 2003 to 2009. There is a slight increase in seasonal cycles 2010-12 with a much higher visitor numbers around September 2011