function P3_01
clear, clc, format short g, format compact
xyData=[-187.68	1.268E-06
-183.15	7.268E-06
-173.15	0.0001883
-163.15	0.0025884
-153.15	0.0221106
-143.15	0.1315085
-133.15	0.5900482
-123.15	2.117681
-113.15	6.352669
-103.15	16.50044
-93.15	37.87601
-83.15	78.7521
-73.15	150.754
-63.15	269.2572
-53.15	453.7621
-42.08	757.5202
-33.15	1110.03
-23.15	1635.044
-13.15	2332.562
-3.15	3225.086
6.85	4365.116
16.85	5767.654
26.85	7485.2
36.85	9525.254
46.85	1.2E+04
56.85	1.485E+04
66.85	1.823E+04
76.85	2.213E+04
86.85	2.663E+04];
X=xyData(:,1);
m=size(X,1); %Determine the number of data points
Y=xyData(:,2);
prob_title = ([' Antoine Equation Parameters, Nonlinear Regression']);
dep_var_name=['Vapor Pressure (mmHg) '];
ind_var_name=['Temperature (C)'];
parm=[6 -1000 200];
npar=size(parm,2); % Determine the number of the parameters
options=optimset('MaxFunEvals',1000); % Change the default value for MaxFunEvals
Beta=fminsearch(@AntFun,parm,options,X,Y); % Find optimal parameters using fminsearch
[f,Ycalc]=AntFun(Beta,X,Y); % Compute Y (calculated) at the optimum
disp(['   Results,' prob_title ]); 
Res=[];
for i=1:npar
	Res=[Res; i Beta(i)];
end
disp('     Parameter No.   Value      ');
disp(Res);
s2=sum((Y-Ycalc)'*(Y-Ycalc))/(m-npar); %variance 
disp(['  Variance ', num2str(s2)]);	
ymean=mean(Y);
R2=(Ycalc-ymean)'*(Ycalc-ymean)/((Y-ymean)'*(Y-ymean));%linear correlation coefficient
disp(['  Correlation Coefficient ', num2str(R2)])	
plot(Y,Y-Ycalc,'*')	% Residual plot			
title(['Residual Plot, ' prob_title ])				
xlabel([dep_var_name '(Measured)'])				
ylabel('Residual')				
pause
plot(X,Ycalc,'r-',X,Y,'b+' ) %Plot of experimental and calculated data						
title(['Calculated/Experimental Data ' prob_title])						
xlabel([ind_var_name])						
ylabel([dep_var_name])
function [f,Ycalc]=AntFun(parm,X,Y)
a=parm(1);
b=parm(2);
c=parm(3);
for i=1:size(X,1);
Ycalc(i,1)=10^(a+b/(X(i)+c));
end
resid(:,1)=Y-Ycalc;
f=resid'*resid;