Workout the regression line your preferred way with the data, it's not a huge deal. I generally find it's quickest and easiest to just get it when I plot it on the graph page, but that's up to you. Hopefully you should get something like this:
But it's also pretty common to have to write it in terms of the variables, so:
[text]Sales=5.6970+01364*QuarterNumber[/tex]
Now, you're nearly 100% guaranteed to be asked something along the lines of "Calculate the deseasonalised value for..." or "Make a prediction for the..." after that, so we're going to deal with both of those situations.
1) Using the equation of your regression line, calculate the deseasonalised value for the second quarter of 2006.
So after reading the question, (hopefully) you've noticed that the second quarter of 2006 doesn't actually exist. We'll first have to work out the Time(
t) value (The "Quarter Number in our equation above"). To do that you could either:
a) Count the quarters until you're at the second quarter of 2006
b) Use some simple algebra to find it:
Where 3 is the number of full years preceding 2006, 4 is the number of quarter per year and "+2" is to calculate the
second quarter.
So now that we know that t=14, we can substitute that into our equation and solve for the deseasonalised value.
It's fastest to use your calculator like so:
Solve(y=5.6970+(0.1364*14),y
and you should get:
(round to the nearest whole number because we're talking about diiscrete data, specifically sales.)
2) "Use your deseasonalised value to find the correct seasonalised sale", or "predict the number of sales made in the second quarter in 2006".
With these, the second one especially, it's important to read the question (and highlight, if you so choose) that you're being asked for the
seasonalised value, not the deseasonalised one. Lots of people forget to convert it.
We convert it by:
In English: Multiply the deseasonalised value by the appropriate seasonal index to find the sales value.
So, in terms of our question: