Скачать Linear and Non-Linear Regression Analyse

program least2;      { --> 203 }
{ Pascal program to perform a linear least-squares fit }
{ with Gauss-Jordan routine }
{ Sperate modules needed:
         GAUSSJ,
         PLOT }
 
 
const   maxr   = 20;      { data prints }
   maxc   = 4;      { polynomial terms }
 
type   ary   = array[1..maxr] of real;
   arys   = array[1..maxc] of real;
   ary2   = array[1..maxr,1..maxc] of real;
   ary2s   = array[1..maxc,1..maxc] of real;
 
var   x,y,y_calc   : ary;
   resid      : ary;
   coef,sig   : arys;
   nrow,ncol   : integer;
   correl_coef   : real;
 
 
external procedure cls;
 
procedure get_data(var x: ary;      { independant variable }
         var y: ary;      { dependant variable }
         var nrow: integer);   { length of vectors }
{ get values for n and arrays x,y }
 
var   i   : integer;
 
begin
  nrow:=9;
  for i:=1 to nrow do x[i]:=i;
  y[1]:=2.07;   y[2]:=8.6;
  y[3]:=14.42;   y[4]:=15.8;
  y[5]:=18.92;   y[6]:=17.96;
  y[7]:=12.98;   y[8]:=6.45;
  y[9]:=0.27;
end;      { proceddure get data }
 
procedure write_data;
{ print out the answers }
var   i   : integer;
begin
  writeln;
  writeln;
  writeln('  I      X      Y        YCALC   RESID');
  for i:=1 to nrow do
    writeln(i:3,x[i]:8:1,y[i]:9:2,y_calc[i]:9:2,resid[i]:9:2);
  writeln; writeln(' Coefficients errors ');
  writeln(coef[1],' ',sig[1],' constant term');
  for i:=2 to ncol do
    writeln(coef[i],' ',sig[i]);      { other terms }
  writeln;
  writeln('Correlation coefficient is ',correl_coef:8:5)
end;      { write_data }
 
procedure square(x: ary2;
       y: ary;
        var a: ary2s;
        var g: arys;
    nrow,ncol: integer);
{ matrix multiplication routine }
{ a= transpose x times x }
{ g= y times x }
 
var   i,k,l   : integer;
 
begin      { square }
  for k:=1 to ncol do
    begin
      for l:=1 to k do
   begin
     a[k,l]:=0.0;
     for i:=1 to nrow do
       begin
         a[k,l]:=a[k,l]+x[i,l]*x[i,k];
         if k<>l then a[l,k]:=a[k,l]
       end
   end;      { l-loop }
      g[k]:=0.0;
      for i:=1 to nrow do
   g[k]:=g[k]+y[i]*x[i,k]
      end   { k-loop }
end;      { SQUARE }
 
{external procedure gaussj(var b:   ary2s;
               y:   arys;
           var coef:   arys;
           ncol:      integer;}