Mathematics 
Polynomial Fit
A first try in fitting the census data might be a simple polynomial fit. Two MATLAB functions help with this process.
Function 
Description 

Polynomial curve fit. 

Evaluation of polynomial fit. 
The MATLAB polyfit
function generates a "best fit" polynomial (in the least squares sense) of a specified order for a given set of data. For a polynomial fit of the fourthorder
p = polyfit(cdate,pop,4) Warning: Polynomial is badly conditioned. Remove repeated data points or try centering and scaling as described in HELP POLYFIT. p = 1.0e+05 * 0.0000 0.0000 0.0000 0.0126 6.0020
The warning arises because the polyfit
function uses the cdate
values as the basis for a matrix with very large values (it creates a Vandermonde matrix in its calculations  see the polyfit
Mfile for details). The spread of the cdate
values results in scaling problems. One way to deal with this is to normalize the cdate
data.
Preprocessing: Normalizing the Data
Normalization is a process of scaling the numbers in a data set to improve the accuracy of the subsequent numeric computations. A way to normalize cdate
is to center it at zero mean and scale it to unit standard deviation:
Now try the fourthdegree polynomial model using the normalized data:
Evaluate the fitted polynomial at the normalized year values, and plot the fit against the observed data points:
Another way to normalize data is to use some knowledge of the solution and units. For example, with this data set, choosing 1790 to be year zero would also have produced satisfactory results.
Case Study: Curve Fitting  Analyzing Residuals 