function census(callbackarg)
%CENSUSGUI Try to predict the US population in the year 2020.
% This example is older than MATLAB.  It started as an exercise in
% "Computer Methods for Mathematical Computations", by Forsythe,
% Malcolm and Moler, published by Prentice-Hall in 1977.
% The data set has been updated every ten years since then.
% Today, MATLAB makes it easier to vary the parameters and see the
% results, but the underlying mathematical principles are unchanged:
%
%    Using polynomials of even modest degree to predict
%    the future by extrapolating data is a risky business.
%
% The data is from the decennial census of the United States for the
% years 1900 to 2010.  The task is to extrapolate beyond 2010.

p = [ 75.995  91.972 105.711 123.203 131.669 150.697 ...
     179.323 203.212 226.505 249.633 281.422 308.748]';

t = (1900:10:2010)';   % Census years
x = (1890:1:2039)';    % Evaluation years
w = (2020:10:2040)';              % Extrapolation target
guess = 320;           % Eyeball extrapolation
z = guess;             % Extrapolated value
dmax = length(t)-1;    % Maximum polynomial degree

% Polynomial degree

d = 2; %% degree

% Update plot with new model

model = 'spline';%%'polynomial';%%'exponential';%%polynomial naif';%%'pchip';%%
switch model
   case 'census data'
      y = NaN*x;
      z = 320;
   case 'polynomial naif'
      s = t;   c = polyfit(s,p,d);
      s = x;   y = polyval(c,s);
      s = w;   z = polyval(c,s);
   case 'polynomial'
      s = (t-1955)/55;   c = polyfit(s,p,d);
      s = (x-1955)/55;   y = polyval(c,s);
      s = (w-1955)/55;   z = polyval(c,s);
   case 'pchip'
      y = pchip(t,p,x);
      z = pchip(t,p,w);
   case 'spline'
      y = spline(t,p,x);
      z = spline(t,p,w);
   case 'exponential'
      c = polyfit(log(t),log(p),1);
      y = exp(polyval(c,log(x)));
      z = exp(polyval(c,log(w)));
end
h=plot(t,p,"or",x,y,"-",w,z,"og");
set(h,"linewidth",2);
z