[Basics] [Reliability]
[Transforming non-linear relationships]
[Fitting peaks] [Math details] [Spreadsheets] [Matlab]

The objective of curve fitting is to find the
parameters of a mathematical model that describes a set of (usually
noisy) data in a way that minimizes the difference between the model
and the data. The most common approach is the "linear least squares"
method, also called "polynomial least squares", a well-known
mathematical procedure for finding the coefficients of polynomial
equations that are a "best fit" to a set of X,Y data. A polynomial
equation expresses the dependent variable Y as a polynomial in the
independent variable X, for example as a straight line (Y = In all these cases, Y is a linear function of the parameters

Least-squares best fits can be calculated by some hand-held calculators, spreadsheets, and dedicated computer programs (see Math Details below). Although it is possible to estimate the best-fit straight line by visual estimation and a straightedge, the least-square method is more objective and easier to automate. (If you were to give a plot of X,Y data to five different people and ask them to estimate the best-fit line visually, you'd get five slightly different answers, but it you gave the data set to five different computer programs, you'd get the exact same answer every time).

Here's a very simple example: the historical prices of different sizes of SD memory cards advertised in the February 19, 2012, issue of the New York Times.

Memory Capacity in GBytes |
Price in US dollars |

2 | $9.99 |

4 | $10.99 |

8 | $19.99 |

16 | $29.99 |

What's the relationship between memory capacity and cost? Of course, we expect that the larger-capacity cards should cost more than the smaller-capacity ones, and if we plot cost vs capacity (graph on the right), we can see a rough straight-line relationship. A least-squares algorithm can compute the values of

Cost
= $6.56 + Capacity * $1.49

So, $1.49 is the slope and $6.56 is the intercept. (The equation is plotted as the solid line that passes among the data points in the figure). Basically, this is saying that the cost of a memory card consists of a fixed cost of $6.56 plus $1.49 for each Gbyte of capacity. How can we interpret this? The $6.56 represents the costs that are the same regardless of the memory capacity: a reasonable guess is that it includes things like packaging (the different cards are the same physical size and are packaged the same way), shipping, marketing, advertising, and retail shop shelf space. The $1.49 (actually 1.49 dollars/Gbyte) represents the increasing retail price of the larger chips inside the larger capacity cards, which

What can we do with this information? First, we can see how closely the actual prices conform to this equation: pretty well but not perfectly. The line of the equation passes among the data points but does not go exactly through each one. That's because actual retail prices are also influenced by several factors that are unpredictable and random: local competition, supply, demand, and even rounding to the nearest "neat" number; all those factors constitute the "noise" in these data. The least squares procedure also calculates R

The second way we can use these data is to predict the likely prices of other card capacities, if they were available, by putting in the memory capacity into the equation and evaluating the cost. For example, a 12 Gbyte card would be expected to cost $24.44 according to this model. (And a 32 Gbyte card would be predicted to cost $54.29, but

Here's another related example: the historical prices of flat-screen LCD TVs as a function of screen size, as advertised on the Web in the Spring of 2012. The prices of five selected models, similar except for screen size, are plotted against the screen size in inches in the figure on the left, and are fit to a first-order (straight-line) model. As for the previous example, the fit is not perfect. The equation of the best-fit model is shown at the top of the graph, along with the R

The goodness of fit is shown even more clearly in the little graph at the bottom of the figure, with the red dots. This shows the "residuals", the differences between each data point and the least-squares fit at that point. You can see that the deviations from zero are fairly large (±10%), but more important, they are not completely random; they form a

Least-squares calculations can fit not only straight-line data, but any set of data that can be described by a polynomial, for example a second-order (quadratic) equation (Y =

The graph on the left
shows a third example, taken from analytical chemistry:
a straight-line calibration data set where X = concentration
and Y = instrument reading (Y = **a** + **b**X). Click to download that data. The blue
dots are the data points. They don't all fall in a perfect
straight line because of random noise and measurement error in the
instrument readings and possibly also volumetric errors in
the concentrations of the standards (which are usually prepared in
the laboratory by diluting a stock solution). For this set of
data, the measured slope is 9.7926 and the intercept is 0.199. In
analytical chemistry, the slope of the calibration curve is often
called the "sensitivity". The intercept indicates the instrument
reading that would be expected if the concentration were zero.
Ordinarily instruments are adjusted ("zeroed") by the operator to
give a reading of zero for a concentration of zero, but random
noise and instrument drift can cause the intercept to be non-zero
for any particular calibration set. In this computer-generated
example, the "true" value of the slope was exactly 10 and of the
intercept was exactly zero before noise was added, and the noise
was added by a normally-distributed random-number generator, so
the presence of the noise caused this particular measurement of
slope to be off by about 2%. Had there been a larger number of
points in this data set, the calculated values of slope and
intercept would almost certainly have been better. (On average,
the accuracy of measurements of slope and intercept improve with
the square root of the number
of points in the data set).

Once the calibration curve is established, it can be used to determine the concentrations of unknown samples that are measured on the same instrument, for example by solving the equation for concentration as a function of instrument reading. The result is that the concentration of the sample Cx is given by Cx = (Sx - intercept)/slope, where Sx is the signal given by the sample solution, and "slope" and "intercept" are the results of the least-squares fit. The concentration and the instrument readings can be recorded in any convenient units, as long as the same units are used for calibration and for the measurement of unknowns.

A
plot
of the "residuals" for the calibration data shows results that
don't seem to be particularly random. Except for the the 6^{th}
data point (at a concentration of 0.6), the other points seem to
form a rough U-shaped curve, indicating that a quadratic or cubic
equation might be a better model for those points. Can we reject
the 6th point as being an "outlier", perhaps caused by a mistake
in preparing that solution standard or in reading the instrument
for that point? Discarding that point would improve the quality of fit (R^{2
}= .992 instead of .986), especially if a
quadratic fit were used (R^{2
}= .998). Many instruments do give a very linear
calibration response, but others show a slightly
non-linear response under certain circumstances. In this
particular case, the calibration data used for this example were *
computer-generated to be* perfectly
linear, with normally-distributed random numbers added to
simulate noise. So in fact that 6^{th}
point is not an outlier
and the curve is not
non-linear, but you would not
know that in a real application. Even normally-distributed
random errors can occasionally give individual deviations that are
quite far from the average. Moral: don't throw out
data points just because they seem a little off, unless you
have good reason, and don't use higher-order polynomial
fits just to get better fits if the instrument is known to
give linear response under those circumstances.

Reliability of curve fitting results

How reliable are the slope, intercept and other polynomial coefficients obtained from least-squares calculations on experimental data? The single most important factor is the appropriateness of the model chosen; it's critical that the model (e.g. linear, quadratic, etc) be a good match to the actual underlying shape of the data. You can do that either by choosing a model based on the known and expected behavior of that system (like using a linear calibration model for an instrument that is known to give linear response under those conditions) or by choosing a model that always gives randomly-scattered residuals that do not exhibit a regular shape. But even with a perfect model, the least-squares procedure applied to repetitive sets of measurements will not give the same results every time because of random error (noise) in the data. If you were to repeat the entire set of measurements many times and do least-squares calculations on each data set, the standard deviations of the coefficients would vary directly with the standard deviation of the noise and inversely with the square root of the number of data points in each fit, all else being equal. The problem, obviously, is that it is not always possible to repeat the entire set of measurements many times. You may have only one set of measurements, and each experiment may be very expensive to repeat. So, it would be great if we had a short-cut method that would let us predict the standard deviations of the coefficients from a single measurement of the signal, without actually repeating the measurements.

Here I will describe three general ways to predict the standard deviations of the polynomial coefficients: algebraic propagation of errors, Monte Carlo simulation, and the bootstrap sampling method.

Algebraic
Propagation of errors. The classical way is based on the rules for
mathematical error propagation. The propagation of errors of the
entire curve-fitting method can be described in closed-form algebra by
breaking down the method into a series of simple differences,
sums, products, and ratios, and applying the rules for
error propagation to each step. The result of
this procedure for a first-order (straight line) least-squares fit
are shown in the last three lines of the set of equations in Math Details, below. Essentially, these
equations make use of the deviations from the least-squares line
(the "residuals") to estimate the standard deviations of the slope
and intercept, based on the assumption that the noise in that
single data set is *random *and is representative of the
noise that would be obtained upon repeated measurements. Because these predictions are based
only on a single data set, they are good only insofar as
that data set is typical of others that might be obtained
in repeated measurements. If your random errors happen to
be small when you acquire your data set, you'll get a deceptively
* good-looking* fit, but then your estimates of
the standard deviation of the slope and intercept will be too
low, on average. If your random errors happen to be large in that
data set, you'll get a deceptively *bad-looking* fit, but
then your estimates of the standard deviation will be too high, on
average. This problem becomes worse when the number of data points
is small. This is not to say that it is not worth the trouble to
calculate the predicted standard deviations of slope and
intercept, but keep in mind that these predictions are accurate
only if the number of data points is large (and only if the noise
is random and normally distributed). Beware: if the deviations
from linearity in your data set are *systematic *and not *random*,
for example, if try to fit a straight
line to a smooth curved data set, then the estimates the
standard deviations of the slope and intercept by these last two
equations *will be too high*, because they assume the
deviations are caused by random noise that varies from measurement
to measurement, whereas in fact a smooth curved data set without
random noise will give the *same *slope and intercept
from measurement to measurement.

In the application to analytical calibration, the concentration of the sample Cx is given by Cx = (Sx - intercept)/slope, where Sx is the signal given by the sample solution. The uncertainty of all three terms contribute to the uncertainty of Cx. The standard deviation of Cx can be estimated from the standard deviations of slope, intercept, and Sx using the rules for mathematical error propagation. But the problem is that, in analytical chemistry, the labor and cost of preparing and running large numbers of standards solution often limits the number of standards to a rather small set, by statistical standards, so these estimates of standard deviation are often fairly rough. A spreadsheet that performs these error-propagation calculations for your own first-order (linear) analytical calibration data can be downloaded from http://terpconnect.umd.edu/~toh/models/CalibrationLinear.xls). For example, the linear calibration example just given in the previous section, where the "true" value of the slope was 10 and the intercept was zero, this spreadsheet (whose screen shot shown on the right) predicts that the slope is 9.8 with a standard deviation 0.407 (4.2%) and that the intercept is 0.197 with a standard deviation 0.25 (128%), both well within one standard deviation of the true values. This spreadsheet also performs the propagation of error calculations for the calculated concentrations of each unknown in the last two columns on the right. In the example in this figure, the instrument readings of the standards are taken as the unknowns, showing that the predicted percent concentration errors range from about 5% to 19% of the true values of those standards. (Note that the standard deviation of the concentration is greater at high concentrations than the standard deviation of the slope, and considerably greater at low concentrations because of the greater influence of the uncertainly in the intercept). For a further discussion and some examples, see http://terpconnect.umd.edu/~toh/models/Bracket.html#Cal_curve_linear.

Monte Carlo simulation. The
second way of estimating the standard deviations of the
least-squares coefficients is to perform a random-number simulation (a type of
Monte
Carlo simulation). This requires that you know (by previous
measurements) the average standard deviation of the random noise
in the data. Using a computer, you construct a model of your data
over the normal range of X and Y values (e.g. Y = **intercept**
+ **slope***X + noise,
where **noise** is the noise in the data), compute the slope
and intercept of each simulated noisy data set, then repeat that
process many times (usually a few thousand) with different sets of
random noise, and finally compute the standard deviation of all
the resulting slopes and intercepts. This is ordinarily done with
normally-distributed random noise (e.g. the RANDN function that
many programing languages have). These random number generators
produce "white" noise, but other noise colors can
be derived. If the model is good and the noise in the data
is well-characterized in terms of frequency distribution and
signal amplitude dependence, the results will be a very good
estimate of the expected standard deviations of the
least-squares coefficients. (If the noise is not constant, but
rather varies with the X or Y values, or if the noise is not white
or is not normally distributed, then that behavior must be
included in the simulation). An animated
example is shown on the right, for the case of a 100-point
straight line data set with slope=1, intercept=0, and standard
deviation of the added noise equal to 5% of the maximum value of
y. For each repeated set of simulated data, the fit coefficients
(least-squares measured slope and intercept) are slightly
different because of the noise.

Obviously this method involves programming a computer to compute
the model and is not so convenient as evaluating a
simple algebraic expression. But there are two important
advantages to this method: (1) is has great generality; it can be
applied to curve fitting methods that are too complicated for the
classical closed-form algebraic propagation-of-error calculations,
even iterative non-linear methods;
and (2) its predictions are based on the average noise in the
data, not the noise in just a single data set. For that reason, it
gives more reliable estimations, particularly when the number of
data points in each data set is small. Nevertheless, you can
not always apply this method because you don't always know
the average standard deviation of the random noise in the
data. This type of bootstrap computation is easily done in Matlab/Octave and (with greater
difficulty) in spreadsheets.

You can download a MatlabOctave script that compares the Monte Carlo simulation to the algebraic method above from http://terpconnect.umd.edu/~toh/spectrum/LinearFiMC.m. By running this script with different sizes of data sets ("NumPoints" in line 10), you can see that the standard deviation predicted by the algebraic method fluctuates a lot from run to run when NumPoints is small (e.g. 10), but the Monte Carlo predictions are much more steady. When NumPoints is large (e.g. 1000), both methods agree very well.

The Bootstrap. The third method is the "bootstrap" method, a procedure that involves choosing random sub-samples with replacement from a single data set and analyzing each sample the same way (e.g. by a least-squares fit). Every sample is returned to the data set after sampling, so that (a) a particular data point from the original data set could appear multiple times in a given sample, and (b) the number of elements in each bootstrap sub-sample equals the number of elements in the original data set. As a simple example, consider a data set with 10 x,y pairs assigned the letters a through j. The original data set is represented as [a b c d e f g h i j], and some typical bootstrap sub-samples might be [a b b d e f f h i i] or [a a c c e f g g i j], each bootstrap sample containing the same number of data points, but with about half of the data pairs skipped and the others duplicated. You would use a computer to generate hundreds or thousands of bootstrap samples like that and to apply the calculation procedure under investigation (in this case a linear least-squares) to each set. If there were no noise in the data set, and if the model were properly chosen, then all the points in the original data set and in all the bootstrap sub-samples would fall exactly on the model line, and the least-squares results would be the same for every sub-sample. But if there is noise in the data set, each set would give a slightly different result (e.g. the least-squares polynomial coefficients), because each sub-sample has a different subset of the random noise. The process is illustrated by the animation on the right, for the same 100-point straight-line data set used above. (You can see that the variation in the fit coefficients between subsamples is the same as for the Monte Carlo simulation above). The greater the amount of random noise in the data set, the greater would be the range of results from sample in the bootstrap set. This enables you to estimate the uncertainty of the quantity you are estimating, just as in the Monte-Carlo method above. The difference is that the Monte-Carlo method is based on the assumption that the noise is known, random, and can be accurately simulated by a random-number generator on a computer, whereas the bootstrap method uses the actual noise in the data set at hand, like the algebraic method, except that it does not need an algebraic solution of error propagation. The bootstrap method thus shares its generality with the Monte Carlo approach, but is limited by the assumption that the noise in that (possibly small) single data set is representative of the noise that would be obtained upon repeated measurements. This method is examined in detail in its extensive literature.

The Matlab/Octave script TestLinearFit.m
compares *all three* of these methods (Monte Carlo
simulation, algebraic method, and the bootstrap method) for a
100-point first-order linear least-squares fit. Each method is
repeated on different data sets with the same average slope,
intercept, and random noise, then the standard deviation (SD) of
the slopes (SDslope)
and intercepts (SDint)
were compiled and are tabulated below.

Simulation Algebraic equation Bootstrap method

SDslope SDint SDslope SDint SDslope SDint

Mean SD: 0.1140 4.1158 0.1133 4.4821 0.1096 4.0203

(You can download this script from http://terpconnect.umd.edu/~toh/spectrum/TestLinearFit.m).
On average, the mean standard deviation ("Mean SD") of the
three methods agree very well, but the algebraic and bootstrap
methods fluctuate more that the Monte Carlo simulation each time
this script is run, because they are based on the noise in one single 100-point data set,
whereas the Monte Carlo simulation reports the average of
many data sets. Of course, the algebraic method is simpler
and faster to compute than the other methods. However, an
algebraic propagation of errors solution is not always possible to
obtain, whereas the Monte Carlo and bootstrap methods do not
depend on an algebraic solution and can be applied readily to more
complicated curve-fitting situations, such as non-linear iterative least squares,
as will be seen later.

**Effect of the number of data points on least-squares fit
precision**. The spreadsheets EffectOfSampleSize.ods or
EffectOfSampleSize.xlxs,
which collect the results of many runs of TestLinearFit.m with different
numbers of data points ("NumPoints"), demonstrates that the
standard deviation of the slope and the intercept *decrease *if
the number of data points is *increased*; specifically, the
*standard deviations are inversely proportional to the square
root of the number of data points*.

It's very important that the
noisy signal not be smoothed
before the least-squares calculations, because doing so
will not improve the
reliability of the least-squares results, but it will cause both
the algebraic propagation-of-errors and the bootstrap calculations
to seriously underestimate the standard deviation of the
least-squares results. You can demonstrate using the most recent
version of the script TestLinearFit.m
by setting SmoothWidth in line 10 to something higher than 1,
which will smooth the data before the least-squares calculations.
This has no significant effect on the actual standard
deviation as calculated by the Monte Carlo method, but it
does significantly reduce the predicted standard deviation
calculated by the algebraic propagation-of-errors and (especially)
the bootstrap method. For similar reasons, if the noise is pink rather than white,
the bootstrap error estimates will also be low. Conversely,
if the noise is blue,
as occurs in processed signals that have been subjected to some
sort of differentiation process
or that have been deconvoluted from
some blurring process, then the errors predicted by the algebraic
propagation-of-errors and the bootstrap methods will be *high*.
(You can prove this to yourself by running TestLinearFit.m with pink and blue
noise modes selected in lines 23 and 24). Bottom line: error
prediction works best for *white *noise.

In some cases a fundamentally non-linear relationship can be
transformed into a form that is amenable to polynomial curve
fitting by means of a coordinate transformation (e.g. taking the
log or the reciprocal of the data), and then least-squares method
can be applied to the resulting linear equation. For example, the
signal in the figure below is from a simulation of an exponential
decay (X=time, Y=signal intensity) that has the mathematical form
Y = **a** exp(**b**X), where **a** is the Y-value at
X=0 and **b** is the decay constant. This is a fundamentally
non-linear problem because Y is a non-linear function of the
parameter **b**. However, by taking the natural log of both
sides of the equation, we obtain ln(Y)=ln(**a**) + **b**X.
In this equation, Y is a linear
function of both parameters ln(**a**) and **b**, so it
can be fit by the least squares method in order to
estimate ln(**a**) and **b**, from which you get a by computing exp(ln(**a**)).
In this particular example, the "true" values of the
coefficients are **a** =1 and **b** = -0.9, but
random noise has been added to each data point, with a
standard deviation equal to 10% of the value of that data point,
in order to simulate a typical experimental measurement in
the laboratory. An estimate of the values of ln(**a**)
and b, given only the
noisy data points, can be determined by least-squares curve
fitting of ln(Y) vs X.

The best fit equation, shown by the green solid line in the
figure, is Y =0.959 exp(- 0.905 X), that is, **a**
= 0.959 and **b** = -0.905, which are reasonably close to
the expected values of 1 and -0.9, respectively. Thus, even in the
presence of substantial random noise (10% relative standard
deviation), it is possible to get reasonable estimates of the
parameters of the underlying equation (to within about 4%). The
most important requirement is that the model be good, that is,
that the equation selected for the model accurately describes the
underlying behavior of the system (except for noise). Often that
is the most difficult aspect, because the underlying models are
not always known with certainty. In Matlab and Octave, is
fit can be performed in a single line of code: polyfit(x,log(y),1),
which returns [b log(a)]. (In
Matlab and Octave, "log" is the natural log, "log10" is the
base-10 log).

Other examples of non-linear relationships that can be linearized
by coordinate transformation include the logarithmic (Y = **a**
ln(**b**X)) and power (Y=**a**X^{b})
relationships. Methods of this type used to be very common back in
the days before computers, when fitting anything but a straight
line was difficult. It is still used today to extend the range of
functional relationships that can be handled by common linear
least-squares routines available in spreadsheets hand-held
calculators. (Only a few non-linear relationships can be
handled this way, however. To fit any arbitrary custom function, you may have to
resort to the more difficult non-linear
iterative curve fitting method).

Fitting
Gaussian and Lorentzian peaks. An interesting
example of the use of transformation to convert a non-linear
relationship into a form that is amenable to polynomial curve
fitting is the use of the natural log (ln) transformation to
convert a positive Gaussian peak, which has the
fundamental functional form exp(-x^{2}), into a parabola of the form -x^{2}, which can be fit with a second order
polynomial (quadratic) function (y = **a** + **bx** + **c**x^{2}). The equation for a Gaussian
peak is y = h*exp(-((x-p)./(1/(2*sqrt(ln(2)))*w)) ^2)), where h is
the peak height, p is the x-axis
location of the peak maximum, w is
the full width of the peak at half-maximum, and 0.6005612
is the approximate value of 1/(2*sqrt(ln(2))). The natural
log of *y* can
be shown to be log(**h**)-(4 **p**^2 log(2))/**w**^2+(8
**p ***x* log(2))/**w**^2-(4* x*^2 log(2))/**w**^2,
which is a quadratic form in the independent variable x because
it is the sum of x^2, x, and constant terms. Expressing each of
the peak parameters **h**,**p**, and **w** in terms
of the three quadratic coefficients, a
little algebra will show that all three parameters of the
peak (height, maximum position, and width) can be calculated
from the three quadratic coefficients **a**, **b**,
and **c**;
the peak height is given by exp(**a**-**c***(**b**/(2***c**))^2),
the peak position by -**b**/(2***c**), and the peak
half-width by 2.35482/(sqrt(2)*sqrt(-**c**)). (This is called
"Caruana's Algorithm; see Streamlining Digital
Signal Processing: A Tricks of the Trade Guidebook, Richard G. Lyons, ed., page 298).

One advantage of this type of Gaussian curve fitting, as opposed to simple visual estimation, is illustrated in the figure on the left. The signal is a Gaussian peak with a true peak height of 100 units, a true peak position of 100 units, and a true half-width of 100 units, but it is sparsely sampled only every 31 units on the x-axis. The resulting data set, shown by the red points in the upper left, has only 6 data points on the peak itself. If we were to take the maximum of those 6 points (the 3rd point from the left, with x=87, y=95) as the peak maximum, that would not be very close to the true values of peak position (100) and height (100). If we were to take the distance between the 2nd the 5th data points as the peak width, we'd get 3*31=93, again not very close to the true value of 100.

However, taking the natural log of the data (upper right) produces a parabola that can be fit with a quadratic least-squares fit (shown by the blue line in the lower left). From the three coefficients of the quadratic fit, we can calculate much more accurate values of the Gaussian peak parameters, shown at the bottom of the figure (height=100.57; position=98.96; width=99.2). The plot in the lower right shows the resulting Gaussian fit (in blue) displayed with the original data (red points). The accuracy of those peak parameters (about 1% in this example) is limited only by the noise in the data. This above figure was created in Matlab (or Octave), using this script. (The Matlab/Octave function gaussfit.m performs the calculation for an x,y data set. You can also download a spreadsheet that does the same calculation; it's available in OpenOffice Calc (Download link, Screen shot) and Excel formats). Note: in order for this method to work properly, the data set must not contain any zeros or negative points; if the signal-to-noise ratio is very poor, it may be useful to pre-smooth the data slightly to prevent this problem. Moreover, the original Gaussian peak signal must be a single isolated peak with a zero baseline, that is, must tend to zero far from the peak center. In practice, this means that any non-zero baseline must be subtracted from the data set before applying this method. A more general approach to fitting Gaussian peaks, which works for data sets with zeros and negative numbers and also for data with multiple overlapping peaks, is the non-linear iterative curve fitting method.

A similar method can be derived for a Lorentzian
peak, which has the fundamental form y=**h**/(1+((x-**p**)/(0.5***w**))^2),
by fitting a quadratic to the reciprocal of y. As for
the Gaussian peak, all three parameters of the peak (height **h**,
maximum position **p**, and width **w**) can be calculated
from the three quadratic coefficients **a**, **b**, and **c**
of the quadratic fit: **h**=4***a**/((4***a*****c**)-**b**^2), **p**=
-**b**/(2***a**),
and
**w**= sqrt(((4***a*****c**)-**b**^2)/**a**)/sqrt(**a**).
Just as for the Gaussian case, the data set must not contain any
zero or negative y values. The Matlab/Octave function lorentzfit.m performs the calculation
for an x,y data set, and the Calc and Excel spreadsheets LorentzianLeastSquares.ods
and LorentzianLeastSquares.xls
perform the same calculation. (By the way, a quick way to test
either of the above methods is to use this simple peak data set: x=5,
20, 35 and y=5, 10, 5, which has a height, position, and width
equal to 10, 20, and 30, respectively, for a single symmetrical
peak of any shape, assuming a baseline of zero).

In order to apply the above methods to signals containing *two
or more* Gaussian or Lorentzian peaks, it's necessary to
locate all the peak maxima first, so that the proper groups of
points centered on each peak can be processed with the algorithms
just discussed. That is discussed in the section on Peak Finding and
Measurement.

But there is a downside to using coordinate transformation methods to convert non-linear relationships into simple polynomial form, and that is that the noise is also effected by the transformation, with the result that the propagation of error from the original data to the final results is often difficult to predict. For example, in the method just described for measuring the peak height, position, and width of Gaussian or Lorentzian peaks, the results depends not only on the amplitude of noise in the signal, but also on how many points across the peak are taken for fitting. In particular, as you take more points far from the peak center, where the y-values approach zero, the natural log of those points approaches negative infinity as y approaches zero. The result is that the noise of those low-magnitude points is unduly magnified and has a disproportional effect on the curve fitting. This runs counter the usual expectation that the quality of the parameters derived from curve fitting improves with the square root of the number of data points (CurveFittingC.html#Noise). A reasonable compromise in this case is to take only the points in the top half of the peak, with Y-values down to one-half of the peak maximum. If you do that, the error propagation (predicted by a Monte Carlo simulation with constant normally-distributed random noise) shows that the relative standard deviations of the measured peak parameters are directly proportional to the noise in the data and inversely proportional to the square root of the number of data points (as expected), but that the proportionality constants differ:

relative standard deviation of the peak height = 1.73*noise/sqrt(N),

relative standard deviation of the peak position = noise/sqrt(N),

relative standard deviation of the peak width = 3.62*noise/sqrt(N),

where noise is the standard deviation of the noise in the data and N in the number of data points taken for the least-squares fit. You can see from these results that the measurement of peak position is most precise, followed by the peak height, with the peak width being the least precise. If one were to include points far from the peak maximum, where the signal-to-noise ratio is very low, the results would be poorer than predicted. These predictions depend on knowledge of the noise in the signal; if only a single sample of that noise is available for measurement, there is no guarantee that sample is a representative sample, especially if the total number of points in the measured signal is small; the standard deviation of small samples is notoriously variable. Moreover, these predictions are based on a simulation with constant normally-distributed white noise; had the actual noise varied with signal level or with x-axis value, or if the probability distribution had been something other than normal, those predictions would not necessarily have been accurate. In such cases the bootstrap method has the advantage that it samples the actual noise in the signal.

You can download the Matlab/Octave code for this Monte Carlo simulation from http://terpconnect.umd.edu/~toh/spectrum/GaussFitMC.m; view screen capture. A similar simulation (http://terpconnect.umd.edu/~toh/spectrum/GaussFitMC2.m, view screen capture) compares this method to fitting the entire Gaussian peak with the iterative method in Curve Fitting 3, finding that the precision of the results are only slightly better with the (slower) iterative method.

Note 1: If you are reading this online, you can right-click on any of the m-file links above and select Save Link As... to download them to your computer for use within Matlab/Octave.

Note 2: In the curve
fitting techniques described here and in the next two sections,
there is no requirement that the x-axis interval between data
points be uniform, as is the assumption in many of the other
signal processing techniques previously covered. Curve
fitting algorithms typically accept a set of arbitrarily-spaced
x-axis values and a corresponding set of y-axis values.

The least-squares best fit for an x,y data set can be computed
using only basic arithmetic. Here are the relevant equations
for computing the slope and intercept of the first-order best-fit
equation, y = intercept + slope*x, as well as the predicted
standard deviation of the slope and intercept, and the coefficient
of determination, R^{2},
which is an indicator of the "goodness of fit". (R^{2} is 1.0000 if
the fit is perfect and less than that if the fit is imperfect).

n = number of x,y data points sumx = Σx sumy = Σy sumxy = Σx*y sumx2 = Σx*x meanx = sumx / n meany = sumy / n slope = (n*sumxy - sumx*sumy) / (n*sumx2 - sumx*sumx) intercept = meany-(slope*meanx) ssy = Σ(y-meany)^2 ssr = Σ(y-intercept-slope*x)^2 R ^{2} = 1-(ssr/ssy)Standard deviation of the slope =
SQRT(ssr/(n-2))*SQRT(n/(n*sumx2 - sumx*sumx))Standard deviation of the intercept =
SQRT(ssr/(n-2))*SQRT(sumx2/(n*sumx2 - sumx*sumx)) |

(In these equations, Σ represents summation; for example, Σx
means the sum of all the x values, and Σx*y means the sum of all
the x*y products, etc). The last two lines predict the
standard deviation of the slope and the intercept, based only on
that data sample, assuming that the deviations from the line are
random and normally distributed. These are estimates of the
variability of slopes and intercepts you are likely to get if you
repeated the data measurements over and over multiple times under
the same conditions, assuming that the deviations from the
straight line are due to *r**andom variability* and not
systematic error caused by non-linearity. If the deviations are
random, they will be slightly different from time to time, causing
the slope and intercept to vary from measurement to measurement,
with a standard deviation predicted by these last two equations.
However, if the deviations are caused by systematic non-linearity,
they will be the same from from measurement to measurement, in
which case the prediction of these last two equations will not be
relevant, and you might be better off using a.polynomial fit such
as a quadratic or cubic. The reliability of these standard
deviation estimates depends on assumption of random deviations and
also on the number of data points in the curve fit; they improve
with the square root of the number of points.

These calculations could be performed
step-by-step by hand, with the aid of a calculator or a
spreadsheet, with a program
written in any programming language, or with a Matlab or Octave script. A similar
set of equations can be written to fit a second-order
(quadratic or parabolic) equations to a set of data.

Popular spreadsheets can
perform the math described above easily; the spreadsheets pictured above (LeastSquares.xls and LeastSquares.odt) for linear
fits and (QuadraticLeastSquares.xls and QuadraticLeastSquares.ods) for quadratic fits, utilize the
expressions given above to compute and plot linear and quadratic
(parabolic) least-squares fit, respectively.

Modern spreadsheets also have
built-in facilities for computing polynomial least-squares curve
fits of *any* order. For example, the LINEST function in
both Excel
and OpenOffice
Calc can be used to compute polynomial and other curvilinear
least-squares fits. In addition to the best-fit polynomial
coefficients, the LINEST function also calculates at the same time
the standard error
values, the
determination coefficient (R^{2}), the standard error value for
the *y* estimate, the F statistic, the
number of degrees of freedom,
the regression sum of squares, and the residual sum of
squares. The disadvantage of LINEST, compared to working out the
math using the series of mathematical expressions described above,
is that it is more difficult to adjust to a variable number of
data points and to remove suspect data points.

For the application to analytical calibration and measurement,
there are specific
versions of these spreadsheets that also calculate the
concentrations of the unknowns (download complete set as CalibrationSpreadsheets.zip).
The quadratic version CalibrationQuadraticB.xlsx
computes the concentration standard deviation (column **L**)
and percent relative standard deviation (column **M**) using
the bootstrap method.
Of course these spreadsheet can be used for just about any
measurement calibration application; just change the labels of the
columns and axes to match your particular application.

There is also a set
of spreadsheets that perform Monte Carlo simulations of
several widely-used analytical calibration methods, including
first-order (straight line) and second order (curved line) least
squares fits. Typical systematic and random errors in both
signal and in volumetric measurements are included, for the
purpose of demonstrating how non-linearity, interferences, and
random errors combine to influence the final result.

SPECTRUM, the freeware signal-processing application for Mac OS8, includes least-squares curve fitting for linear (straight-line), polynomials of order 2 through 5, and exponential, logarithmic, and power relationships.

Matlab and Octave have simple built-in functions for least-squares curve fitting: polyfit and polyval. For example, if you have a set of x,y data points in the vectors "x" and "y", then the coefficients for the least-squares fit are given by

If the optional input argument "polyorder" is provided, plotit fits a polynomial of order "polyorder" to the data and plots the fit as a green line and displays the fit coefficients and the goodness-of-fit measure R

You can use plotit.m to linearize and plot other

y =

y =

y=

Don't forget that in Matlab/Octave, "log" means

The plotit function also has a built-in bootstrap routine that computes coefficient error estimates by the bootstrap method and returns the results in the matrix "BootResults" (of size 5 x polyorder+1). You can change the number of bootstrap samples in line 48. The calculation is triggered by including a third output argument, e.g. [coef, RSquared, BootResults]= plotit(x,y,polyorder). This works for any polynomial order. For example:

>> x=0:100;

>> y=100+(x*100)+100.*randn(size(x));

>> [coef, RSquared,BootResults]=plotit(x,y,1);

computes straight line with an intercept and slope of 100, plus random noise with a standard deviation of 100, then fits a straight line to that data and prints out a table of bootstrap error estimates, with the slope in the first column and the intercept in the second column:

Bootstrap Results

Bootstrap Mean: 100.359 88.01638

Bootstrap STD: 0.204564 15.4803

Bootstrap IQR: 0.291484 20.5882

Percent RSD: 0.203832 17.5879

Percent IQR: 0.290441 23.3914

The variation plotfita

My downloadable function trypoly(x,y) fits the data in x,y with a series of polynomials of degree 1 through length(x)-1 and returns the coefficients of determination (R2) of each fit as a vector, allowing you to evaluate how polynomials of various orders fit your data. To plot as a bar graph, write

>> x=50:150;y=100.*gaussian(x,100,100)+10.*randn(size(x));

>> [Height,Position,Width]=gaussfit(x,y)

returns [Height,Position,Width] clustered around 100,100,100. A similar function for Lorentzian peaks is lorentzfit.m,which takes the form

[Height,Position,Width]=lorentzfit(x,y).

An expanded variant of the gaussfit.m function is bootgaussfit.m, which does the same thing but also optionally plots the data and the fit and computes estimates of the random error in the height, width, and position of the fitted Gaussian function by the bootstrap sampling method. For example:

>>
x=50:150;y=100.*gaussian(x,100,100)+10.*randn(size(x));

>> [Height,Position,Width,BootResults]=bootgaussfit(x,y,1);

does the same as the previous example but also displays error
estimates in a table and returns the 3x5 matrix BootResults. Type "help
bootgaussfit" for help. >> [Height,Position,Width,BootResults]=bootgaussfit(x,y,1);

Height Position Width

Bootstrap Mean: 100.84 101.325 98.341

Bootstrap STD: 1.3458 0.63091 2.0686

Bootstrap IQR: 1.7692 0.86874 2.9735

Percent RSD: 1.3346 0.62266 2.1035

Percent IQR: 1.7543 0.85737 3.0237

It's important that the noisy
signal (x.y) not be smoothed if
the
bootstrap error predictions are to be accurate. Smoothing the data
will cause the bootstrap method to seriously underestimate the
precision of the results.

The downloadable Matlab-only functions iSignal.m
and ipf.m,
whose principal functions are fitting *peaks*, also have a
function for fitting *polynomials *of any order (**Shift-o**).

Recent versions of Matlab have a convenient tool for interactive manually-controlled (rather than programmed) polynomial curve fitting in the Figure window. Click for a video example: (external link to YouTube).

The *Matlab Statistics Toolbox* includes two types of
bootstrap functions, "bootstrp" and "jackknife". To open the reference
page in Matlab's help browser, type "doc bootstrp" or "doc
jackknife".

Last updated October, 2014. This page is maintained by Prof. Tom O'Haver , Department of Chemistry and Biochemistry, The University of Maryland at College Park. Comments, suggestions and questions should be directed to Prof. O'Haver at toh@umd.edu.

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