Plot a line through a series of points (x, y).

 linear [ (1, 1), (2, 2), (3, 3), (4, 4) ]
     --> Ok { slope = 1, intercept = 0 }

Use this in situations where the relationship between x and y is linear and has as few outliers as possible. A relationship is linear if it can be described accurately as y = x * slope + intercept. The easiest way to determine this is to look at a plot of your values. If they look roughly like a line, we're in business. But if your plot shows a curve or a random point cloud then don't trust the results of this function applied to them.

Examples of data which fit these criteria:

• relationship of some value to time. For example, are bananas getting cheaper or more expensive as time goes on?
• relationships where doing something more or less leads to doing something else more or less. For example, does smoking more decrease your life expectancy?

Statistically speaking, this is a least-squares regression.

A line plotted through points. Get one by passing your data to linear.

Make predictions using this and predictY and measure accuracy with goodnessOfFit.

How good is the fit that linear generated? We can give you a confidence between 0 and 1 representing a percent confidence. Practically, we'll usually return a value between almost 0 and almost 1.

values : List (Float, Float)
values =
    [ (1, 1), (2, 2), (3, 3), (4, 4) ]


linear values
    |> Result.andThen (\fit -> goodnessOfFit fit values)
    --> Ok 1

Statistically speaking, this is the R-squared value of the linear regression.

Predict a value of y from a LinearFit calculated with linear and a value of x.

linear [ (1, 1), (2, 2), (3, 3), (4, 4) ]
    |> Result.map (\fit -> predictY fit 5)
    --> Ok 5

Indicate that something has gone wrong in the caculation of a trend line. Specifically:

• NotEnoughData: there was not enough data to calculate a trend. The number attached is the minimum number of points needed to calculate.
• ResultWasNaN: this likely means you've tried to plot a point through a bunch of zeroes, or a bunch of values that are very very close to zero. If that's not the case, please open an issue.