When creating regressors (or contrasts) for a parametric experimental design, it is often useful to model (or test) linear, quadratic and cubic effects separately. However, if you just use the usual x, x*x, x*x*x approach to create predictors, you wil create highly correlated variables. Specifically, the quadratic term will include some of the linear effect, and vice-versa. The solution is to use orthogonal polynomials.

The very simplest way to do this is with Matlab’s orth function. E.g. for 10-level parametric regressor

n=10;
x=[1:n]';
One=ones(n,1);
X0=[One x x.^2 x.^3];
X=orth(X0);
plot(X)

But annoyingly, this standardizes the columns and produces a fairly uninterprtable basis. In particular, while a linear trend can be represented by a linear combination of those columns, it is not in any one single column.

Instead, we can carefully build up a basis that is mean zero, and uses successive othogonalization to ensure the interpretability of each column.

n=10;
x=[1:n]';
One=ones(n,1);
X0=[x x.^2 x.^3];
X=One;
for i=1:size(X0,2)
  X=[X X0(:,i)-X*pinv(X)*X0(:,i)];
end
plot(X)

Now we have three columns readily identifiable as intercept, linear, quadratic and cubic terms; after the first column, they are mean zero (so that they do not carry any of the grand mean), and all are orthogonal, as can be checked with corrcoef(X).

There are of course specialized functions to create orthogonal polynomials, but for this purpose, this code snippet is all you need.