Anything that repeats on a schedule is a candidate for a sinusoidal model. A seat on a Ferris wheel rises and falls once per revolution. The water in a harbour rises and falls roughly twice a day. Hours of daylight in Ontario swell to a peak near the June solstice and shrink to a low near the December solstice, every year. In each case you can measure a highest value, a lowest value, and how long one cycle takes, and those three measurements, plus a starting point, are everything a sinusoid needs. Once you have the equation, it answers two kinds of question: what the value will be at a given time, and when the value will reach a given level. In this course every model uses radians, so a full cycle corresponds to .

From a description to an equation
| Parameter | How to get it | Ferris wheel: diameter 40 m, axle 22 m up, 3 min per turn, board at the bottom |
|---|---|---|
| Axis c | Average of the maximum and minimum | m (the axle height) |
| Amplitude |a| | Half the distance from minimum to maximum | m (the radius) |
| k | , with in minutes | |
| Function and sign | Start at a max: ; at a min: ; on the axis rising: | Starts at the bottom, so use and |
| Check | Substitute a known point | : m, the top, halfway round |
From data to an equation
Real data rarely arrives as a neat description, so you extract the same measurements from a table or graph. Suppose a harbour's water depth reaches a high of m at 3:00 a.m. and the following low of m at 9:12 a.m. The axis is m and the amplitude is m. High to low is half a cycle, h, so the period is h and . Because the data begins at a high, cosine with a horizontal shift equal to the time of that high is the simplest choice: . With data that is not perfectly sinusoidal, such as average monthly temperatures, use the largest and smallest values in the table, and expect the model to be close rather than exact. Graphing technology can also fit a sinusoidal regression to the data; compare its parameters with your hand-built ones as a check. Do not be surprised if a classmate's equation looks different from yours. The tide could equally be written with sine and a shift of (a quarter period before the high, when the water is at the axis and rising), or with and , the time of the low. All three describe exactly the same curve, because shifting by a quarter or half period converts one form into another. What matters is that your model reproduces the data.
Predicting values and finding when a value occurs
Predicting is substitution: put the time into the model with your calculator in radian mode. Finding WHEN a value occurs runs the model backwards and is a trigonometric equation. Isolate the cosine (or sine), then apply the inverse function to get one angle. That single angle is not the whole answer. Cosine takes each value twice per cycle, so the second angle is minus the first; for sine it is minus the first. Then convert each angle back to time by dividing by (and adding ), and add whole periods to get later occurrences. Graphically, the same answers are the intersections of the model with the horizontal line target value, which is a good way to see how many answers to expect before you calculate them.
Interpreting the parameters in context
| Parameter | Ferris wheel | Harbour tide | Daylight hours | Monthly temperature |
|---|---|---|---|---|
| Amplitude |a| | Wheel radius, 20 m | Half the tidal range, 3.6 m | How far day length swings from its average, about 3.25 h | Half the gap between the warmest and coldest months |
| Axis c | Axle height, 22 m | Mean water depth, 5.0 m | Average day length, about 12 h | The year's average temperature |
| Period | Time for one revolution, 3 min | About 12.4 h between high tides | One year, 365 days | 12 months |
| Phase shift d | When the rider is first at the chosen start point | Time of the first high tide | Day of the spring equinox when using sine | Month of the coldest (or warmest) average |