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linreg(x,y)

linreg(x, y) -> a, b

Perform linear regression. Returns a and b such that a + b*x is the closest straight line to the given points (x, y), i.e., such that the squared error between y and a + b*x is minimized.

Example:

using PyPlot
x = [1.0:12.0;]
y = [5.5, 6.3, 7.6, 8.8, 10.9, 11.79, 13.48, 15.02, 17.77, 20.81, 22.0, 22.99]
a, b = linreg(x, y)          # Linear regression
plot(x, y, "o")              # Plot (x, y) points
plot(x, [a+b*i for i in x])  # Plot line determined by linear regression

Examples

julia> x = [1, 2, 3, 4, 5];
julia> y = [2, 4, 6, 8, 10];
julia> w = [1, 1, 1, 1, 1];
julia> linreg(x, y, w)
(2.0, 0.0)

This example performs a weighted least-squares linear regression on the given x and y data points, using the weights w. It returns a tuple (slope, intercept) representing the coefficients of the linear regression model.

julia> x = [1, 2, 3, 4, 5];
julia> y = [1, 3, 5, 7, 9];
julia> w = [1, 1, 1, 1, 1];
julia> linreg(x, y, w)
(2.0, -1.0)

In this example, the x and y values represent a perfect linear relationship with a slope of 2 and an intercept of -1.

Common mistake example:

julia> x = [1, 2, 3, 4, 5];
julia> y = [2, 4, 6, 8];
julia> w = [1, 1, 1, 1, 1];
julia> linreg(x, y, w)
ERROR: DimensionMismatch("arrays could not be broadcast to a common size")

In this example, the dimensions of x and y do not match, causing a DimensionMismatch error. It's important to ensure that the input arrays have the same length to perform linear regression correctly.

See Also

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