Effect of sample size on least-square error estimates by Monte 
Carlo Simulation, Algebraic propagation-of-errors, and the 
bootstrap method, using the Matlab script TestLinearFit.m


NumPoints = 5     SD Noise = 8.8838    x-range = 29
    Simulation          Algebraic equation    Bootstrap method
    SDslope  SDint       SDslope  SDint        SDslope  SDint
      0.45026  7.8143     0.44133  12.449      0.21683  4.7916
      0.41146  7.096      0.36987  10.434      0.37149  8.6686
      0.4115   7.3879     0.5722   16.141      0.27311  7.1294
      0.44028  8.0182     0.49473  13.956      0.13605  2.6343


NumPoints = 10     SD Noise = 5.8398    x-range = 29
    Simulation           Algebraic equation    Bootstrap method
     SDslope  SDint        SDslope  SDint      SDslope  SDint
      0.29757  5.1956      0.12942  2.8923      0.07861  1.4127
      0.31692  5.5749      0.23274  5.2014      0.29586  6.9961
      0.37425  6.9359      0.42519  9.5026      0.52816  12.236
      0.30337  4.9118      0.5245  11.722       0.37215  9.2823


NumPoints = 100     SD Noise = 9.2599    x-range = 29
    Simulation          Algebraic equation    Bootstrap method
      SDslope  SDint      SDslope  SDint      SDslope  SDint
      0.11184  1.9911      0.11008  1.987      0.111     1.9946
      0.13305  2.3014      0.11903  2.1485     0.11285   1.972
      0.12152  2.1708      0.11597  2.0932     0.10383   2.0737
      0.12579  2.0406      0.10524  1.8995     0.098432  1.6627


NumPoints = 1000     SD Noise = 10.3877    x-range = 29
    Simulation           Algebraic equation    Bootstrap method
     SDslope  SDint        SDslope  SDint        SDslope  SDint
     0.036277  0.62686     0.039209  0.69241     0.036865  0.64764
     0.039653  0.66164     0.036742  0.64885     0.040106  0.65758
     0.035495  0.67532     0.038292  0.67621     0.037047  0.62136
     0.04117   0.69394     0.037444  0.66123     0.038557  0.65167