Basics of MATLAB and Beyond

load population.datyear = population(:,1);P = population(:,2);plot(year,P,’:o’)box;gridThe European population prior to 1850 was very low and we are unableto see the fine detail. Detail is revealed when we use a logarithmic y-scale:semilogy(year,P,’:o’)box;gridThe following functions implement logarithmic axes:loglogsemilogxsemilogyBoth axes logarithmiclogarithmic x-axislogarithmic y-axis14 Curve Fitting—Matrix DivisionWe continue with the example of Australian population data given inthe previous section. Let us see how well a polynomial fits this data. Weassume the data can be modelled by a parabola:p = c 0 + c 1 x + c 2 x 2where x is the year, c 0 , c 1 , and c 2 are coefficients to be found, and p isthe population. We write down this equation substituting our measureddata:p 1 = c 0 + c 1 x 1 + c 2 x 2 1p 2 = c 0 + c 1 x 2 + c 2 x 2 2.p N = c 0 + c 1 x N + c 2 x 2 Nc○ 2000 by CRC Press LLC