1. Credit card spending from reserve bank

2. achieved

3. merit SEPT-29.7 DEC70.8 MAR-1.6 JUN-27.7

5. Excellence – further feature Spike (irregular movement) The seasonally adjusted data (green line) shows a spike in December 1992. This means the spending for this quarter is significantly below what we would expect for a December quarter. There could have been some event (in this case employment figures had fallen to there lowest value) meaning that people could not spend as much as they normally would at this time.

6. Excellence – further feature Ramps (change in trend) It appears that the data has levelled off over the period. Around 1995 the long term trend has level out significantly. The trend for 1995 and 1996 is for credit card spending to increase by about 5.2 million dollars per quarter. The initial trend may be put down to a mini recovery from a recession with the later trend showing what happens due to normal growth.

7. Excellence – further feature Interpret seasonal effect The average seasonal effect shows us that Sept(-29.7 million dollars) and June(-27.7 million dollars) spending is very low compared to December(70.8 million dollars) and March(1.6 million dollars). This shows that people spend a lot less in winter. This may be due to people deciding to stay at home more (dine out less) when the weather is cold. December is very high. This is likely to be because it is the festive season. People are having end of year functions and buying presents on their credit card. SEPT-29.7 DEC70.8 MAR-1.6 JUN-27.7

8. Excellence – further feature Noise (random variation) There is not a lot of noise. We can see that the seasonally adjusted line follows the smoothed line well. This means our model (the combination of long term and seasonal trend) is very good. Therefore our predictions should be reasonably good. (what if there was noise?)

9. Excellence – further feature Noise (random variation) There is not a lot of noise. We can see that the seasonally adjusted line follows the smoothed line well. This means our model (the combination of long term and seasonal trend) is very good. Therefore our predictions should be reasonably good. (what if there was noise?)