a new title

1.

% curvefit programming

% load the data sheet

cp_data = load('data');

temperature = cp_data (:,1);

cp = cp_data(:,2);

% polynomial equation

co_effs = polyfit(temperature,cp,3);

% predict the particular no.

predicted_cp = polyval(co_effs,temperature);

% compare the curverfit with the original data.

plot(temperature, cp, 'linewidth',3)

xlabel ('Temperature[k]')

ylabel ('specific heat[KJ/KMOL-K]')

hold on

plot(temprature, predicted_cp,'linewidth',3, 'color', 'r')

legend ('original datasheet','curvefit')

Results- if we compare the curve of the [cp vs T] data, according to the number of polynomials, then we will see that it is a good approximation.

2. In order to make the curve fit perfect, we have to increase the order of the polynomial so that the error will be minimum. then the curve will be perfect.