Statistics · Data representation
Chapter 1 · 4
The idea
Time series
Measurements plotted against time and joined in order — why joining is legitimate here but not on a scatter graph, how to describe a trend so it earns the mark, how to spot seasonal variation and use it to predict the next quarter, and why predicting far beyond the data is worthless.
A full journey — read it, play with it, work it, then earn real exam marks. Everything stays on the timeline below.
In this lesson — start anywhere
Statistics · Data representation
Time series
Measurements plotted against time and joined in order — why joining is legitimate here but not on a scatter graph, how to describe a trend so it earns the mark, how to spot seasonal variation and use it to predict the next quarter, and why predicting far beyond the data is worthless.
Why it works
Measurements joined in time order
A time series is the same quantity measured over and over at regular times: a shop's sales each quarter, a town's rainfall each month, a country's population each year. You plot each measurement against the time it was taken — and then, unlike on any other scatter of dots, you join them up in order with straight lines.Why joining is allowed here
On a scatter graph joining the dots is flatly wrong — each dot is a different individual with no natural order, so any zigzag you drew would carry no information. On a time-series graph the horizontal axis is time: one order, one measurement per time, and each segment shows how the quantity changed over that gap — uphill rose, downhill fell, steeper changed faster. The line does not claim to know the values in between; it is a guide for the eye. Reading it is two moves: up from the time to the point, across to the value. A change is the difference between two readings; a total is the sum of several.Keep reading — free
The rest of the explanation, plus 3 worked examples you step through move by move.
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