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added lecture 18 #7

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Mar 30, 2017
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Binary file added lecture_17/in-class_regression.pdf
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10 changes: 5 additions & 5 deletions lecture_17/lecture_17.ipynb
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Expand Up @@ -2,7 +2,7 @@
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Expand All @@ -13,7 +13,7 @@
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Expand Down Expand Up @@ -2385,7 +2385,7 @@
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Expand All @@ -2409,7 +2409,7 @@
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Binary file added lecture_18/.Newtint.m.swp
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6 changes: 6 additions & 0 deletions lecture_18/.ipynb_checkpoints/lecture_18-checkpoint.ipynb
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{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 2
}
33 changes: 33 additions & 0 deletions lecture_18/Newtint.m
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function yint = Newtint_bak(x,y,xx)
% Newtint: Newton interpolating polynomial
% yint = Newtint(x,y,xx): Uses an (n - 1)-order Newton
% interpolating polynomial based on n data points (x, y)
% to determine a value of the dependent variable (yint)
% at a given value of the independent variable, xx.
% input:
% x = independent variable
% y = dependent variable
% xx = value of independent variable at which
% interpolation is calculated
% output:
% yint = interpolated value of dependent variable

% compute the finite divided differences in the form of a
% difference table
n = length(x);
if length(y)~=n, error('x and y must be same length'); end
b = zeros(n,n);
% assign dependent variables to the first column of b.
b(:,1) = y(:); % the (:) ensures that y is a column vector.
for j = 2:n
for i = 1:n-j+1
b(i,j) = (b(i+1,j-1)-b(i,j-1))/(x(i+j-1)-x(i));
end
end
% use the finite divided differences to interpolate
xt = 1;
yint = b(1,1);
for j = 1:n-1
xt = xt*(xx-x(j));
yint = yint+b(1,j+1)*xt;
end
24 changes: 24 additions & 0 deletions lecture_18/challenger_oring.csv
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Flight#,Temp,O-Ring Problem
1,53,1
2,57,1
3,58,1
4,63,1
5,66,0
6,66.8,0
7,67,0
8,67.2,0
9,68,0
10,69,0
11,69.8,1
12,69.8,0
13,70.2,1
14,70.2,0
15,72,0
16,73,0
17,75,0
18,75,1
19,75.8,0
20,76.2,0
21,78,0
22,79,0
23,81,0
1,882 changes: 1,882 additions & 0 deletions lecture_18/lecture_18.ipynb

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Binary file added lecture_18/newton_interpolation.png
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