From 1e8072d894b64d512908580306069afc8652eddb Mon Sep 17 00:00:00 2001
From: Andrew Cumming <andrew.cumming@mcgill.ca>
Date: Tue, 12 Sep 2023 10:52:54 -0400
Subject: [PATCH] Fixes typo in interpolate.md

---
 interpolation.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/interpolation.md b/interpolation.md
index 7a50979..64eb994 100644
--- a/interpolation.md
+++ b/interpolation.md
@@ -22,7 +22,8 @@ Choose a function (e.g. $\sin(x)$ between $0$ and $2\pi$ or a Gaussian) and inve
 
 Linear interpolation is an example of a polynomial fit -- given two data points, we can fit a straight line between them. We could go to higher order by including more data points when modelling each interval. For example, for the interval $x_i<x<x_{i+1}$, we could use the four points $x_{i-1}, x_i, x_{i+1}$, and $x_{i+2}$ to fit a cubic polynomial to use in this range. 
 
-Here is some code that uses [`numpy.polyfit`](https://numpy.org/doc/stable/reference/generated/numpy.polyfit.html) to fit a cubic polynomial to each interval. (Note that the interpolated curve is calculated only between $x_2$ and $x_{N-1}$ because we don't have enough information to fit a cubic for $x_1<x<x_2$ or $x_{N-1}<x<x_N$).
+Here is some code that uses 
+[numpy.polynomial.polynomial.Polynomial.fit](https://numpy.org/doc/stable/reference/generated/numpy.polynomial.polynomial.Polynomial.fit.html#numpy.polynomial.polynomial.Polynomial.fit) to fit a cubic polynomial to each interval. (Note that the interpolated curve is calculated only between $x_2$ and $x_{N-1}$ because we don't have enough information to fit a cubic for $x_1<x<x_2$ or $x_{N-1}<x<x_N$).
 
 ```
 # Sample the function