The MATLAB^{®} Basic Fitting UI allows you to interactively:

Model data using a spline interpolant, a shape-preserving interpolant, or a polynomial up to the tenth degree

Plot one or more fits together with data

Plot the residuals of the fits

Compute model coefficients

Compute the norm of the residuals (a statistic you can use to analyze how well a model fits your data)

Use the model to interpolate or extrapolate outside of the data

Save coefficients and computed values to the MATLAB workspace for use outside of the dialog box

Generate MATLAB code to recompute fits and reproduce plots with new data

The Basic Fitting UI is only available for 2-D plots. For more advanced fitting and regression analysis, see the Curve Fitting Toolbox™ documentation and the Statistics and Machine Learning Toolbox™ documentation.

The Basic Fitting UI sorts your data in ascending order before fitting. If your data set is large and the values are not sorted in ascending order, it will take longer for the Basic Fitting UI to preprocess your data before fitting.

You can speed up the Basic Fitting UI by first sorting your
data. To create sorted vectors `x_sorted`

and `y_sorted`

from
data vectors `x`

and `y`

, use the MATLAB `sort`

function:

[x_sorted, i] = sort(x); y_sorted = y(i);

To use the Basic Fitting UI, you must first plot your data in
a figure window, using any MATLAB plotting command that produces
(only) *x* and *y* data.

To open the Basic Fitting UI, select **Tools >
Basic Fitting** from the menus at the top of the figure
window.

When you fully expand it by twice clicking the arrow button in the lower right corner, the window displays three panels. Use these panels to:

Select a model and plotting options

Examine and export model coefficients and norms of residuals

Examine and export interpolated and extrapolated values.

To expand or collapse panels one-by-one, click the arrow button in the lower right corner of the interface.

This example shows how to use the Basic Fitting UI to fit, visualize, analyze, save, and generate code for polynomial regressions.

The file, `census.mat`

, contains U.S. population
data for the years 1790 through 1990 at 10 year intervals.

To load and plot the data, type the following commands at the MATLAB prompt:

load census plot(cdate,pop,'ro')

The `load`

command adds the following variables
to the MATLAB workspace:

`cdate`

— A column vector containing the years from 1790 to 1990 in increments of 10. It is the predictor variable.`pop`

— A column vector with U.S. population for each year in`cdate`

. It is the response variable.

The data vectors are sorted in ascending order, by year. The plot shows the population as a function of year.

Now you are ready to fit an equation the data to model population growth over time.

Open the Basic Fitting dialog box by selecting

**Tools > Basic Fitting**in the Figure window.In the

**Plot fits**area of the Basic Fitting dialog box, select the**cubic**check box to fit a cubic polynomial to the data.MATLAB uses your selection to fit the data, and adds the cubic regression line to the graph as follows.

In computing the fit, MATLAB encounters problems and issues the following warning:

Polynomial is badly conditioned. Add points with distinct X values, select a polynomial with a lower degree, or select "Center and scale X data."

This warning indicates that the computed coefficients for the model are sensitive to random errors in the response (the measured population). It also suggests some things you can do to get a better fit.

Continue to use a cubic fit. As you cannot add new observations to the census data, improve the fit by transforming the values you have to

*z-scores*before recomputing a fit. Select the**Center and scale X data**check box in the dialog box to make the Basic Fitting tool perform the transformation.To learn how centering and scaling data works, see Learn How the Basic Fitting Tool Computes Fits.

Now view the equations and display residuals. In addition to selecting the

**Center and scale X data**and**cubic**check boxes, select the following options:**Show equations****Plot residuals****Show norm of residuals**

Selecting **Plot residuals** creates a subplot
of them as a bar graph. The following figure displays the results
of the Basic Fitting UI options you selected.

The cubic fit is a poor predictor before the year 1790, where
it indicates a decreasing population. The model seems to approximate
the data reasonably well after 1790. However, a pattern in the residuals
shows that the model does not meet the assumption of normal error,
which is a basis for the least-squares fitting. The **data
1** line identified in the legend are the observed *x* (`cdate`

)
and *y* (`pop`

) data values. The **cubic** regression
line presents the fit after centering and scaling data values. Notice
that the figure shows the original data units, even though the tool
computes the fit using transformed z-scores.

For comparison, try fitting another polynomial equation to the
census data by selecting it in the **Plot fits** area.

In the Basic Fitting dialog box, click the arrow button to
display the estimated coefficients and the norm of the residuals in
the **Numerical results** panel.

To view a specific fit, select it from the **Fit** list.
This displays the coefficients in the Basic Fitting dialog box, but
does not plot the fit in the figure window.

If you also want to display a fit on the plot, you must select
the corresponding **Plot fits** check box.

Save the fit data to the MATLAB workspace
by clicking the **Save to workspace** button on
the Numerical results panel. The Save Fit to Workspace dialog box
opens.

With all check boxes selected, click **OK** to
save the fit parameters as a MATLAB structure:

fit fit = type: 'polynomial degree 3' coeff: [0.9210 25.1834 73.8598 61.7444]

Now, you can use the fit results in MATLAB programming, outside of the Basic Fitting UI.

You can get an indication of how well a polynomial regression
predicts your observed data by computing the *coefficient
of determination,* or *R-square* (written
as R^{2}). The R^{2} statistic,
which ranges from 0 to 1, measures how useful the independent variable
is in predicting values of the dependent variable:

An R

^{2}value near 0 indicates that the fit is not much better than the model`y = constant`

.An R

^{2}value near 1 indicates that the independent variable explains most of the variability in the dependent variable.

To compute R^{2}, first compute a fit,
and then obtain *residuals* from it. A residual
is the signed difference between an observed dependent value and the
value your fit predicts for it.

residuals = y_{observed} -
y_{fitted}

After you have residual values, you can save them to the workspace,
where you can compute R^{2}. Complete the
preceding part of this example to fit a cubic polynomial to the census
data, and then perform these steps:

**Compute Residual Data and R ^{2} for
a Cubic Fit**

Click the arrow button at the lower right to open the Numerical results tab if it is not already visible.

From the

**Fit**drop-down menu, select`cubic`

if it does not already show.Save the fit coefficients, norm of residuals, and residuals by clicking

**Save to Workspace**.The Save Fit to Workspace dialog box opens with three check boxes and three text fields.

Select all three check boxes to save the fit coefficients, norm of residuals, and residual values.

Identify the saved variables as belonging to a cubic fit. Change the variable names by adding a

`3`

to each default name (for example,`fit3`

,`normresid3`

, and`resids3`

). The dialog box should look like this figure.Click

**OK**. Basic Fitting saves residuals as a column vector of numbers, fit coefficients as a struct, and the norm of residuals as a scalar.Notice that the value that Basic Fitting computes for norm of residuals is

`12.2380`

. This number is the square root of the sum of squared residuals of the cubic fit.Optionally, you can verify the norm-of-residuals value that the Basic Fitting tool provided. Compute the norm-of-residuals yourself from the

`resids3`

array that you just saved:mynormresid3 = sum(resids3.^2)^(1/2) mynormresid3 = 12.2380

Compute the

*total sum of squares*of the dependent variable,`pop`

to compute R^{2}. Total sum of squares is the sum of the squared differences of each value from the mean of the variable. For example, use this code:SSpop = (length(pop)-1) * var(pop) SSpop = 1.2356e+005

`var(pop)`

computes the variance of the population vector. You multiply it by the number of observations after subtracting 1 to account for degrees of freedom. Both the total sum of squares and the norm of residuals are positive scalars.Now, compute R

^{2}, using the square of`normresid3`

and`SSpop`

:rsqcubic = 1 - normresid3^2 / SSpop rsqcubic = 0.9988

Finally, compute R

^{2}for a linear fit and compare it with the cubic R^{2}value that you just derived. The Basic Fitting UI also provides you with the linear fit results. To obtain the linear results, repeat steps 2-6, modifying your actions as follows:To calculate least-squares linear regression coefficients and statistics, in the

**Fit**drop-down on the Numerical results pane, select`linear`

instead of`cubic`

.In the Save to Workspace dialog, append

`1`

to each variable name to identify it as deriving from a linear fit, and click**OK**. The variables`fit1`

,`normresid1`

, and`resids1`

now exist in the workspace.Use the variable

`normresid1`

(`98.778`

) to compute R^{2}for the linear fit, as you did in step 9 for the cubic fit:rsqlinear = 1 - normresid1^2 / SSpop rsqlinear = 0.9210

This result indicates that a linear least-squares fit of the population data explains 92.1% of its variance. As the cubic fit of this data explains 99.9% of that variance, the latter seems to be a better predictor. However, because a cubic fit predicts using three variables (

*x*,*x*, and^{2}*x*), a basic R^{3}^{2}value does not fully reflect how robust the fit is. A more appropriate measure for evaluating the goodness of multivariate fits is*adjusted R*. For information about computing and using adjusted R^{2}^{2}, see Residuals and Goodness of Fit.

R^{2} measures how well your polynomial
equation *predicts* the dependent variable, not
how *appropriate* the polynomial model is for your
data. When you analyze inherently unpredictable data, a small value
of R^{2} indicates that the independent variable
does not predict the dependent variable precisely. However, it does
not necessarily mean that there is something wrong with the fit.

**Compute Residual Data and R ^{2} for
a Linear Fit. **In this next example, use the Basic Fitting UI to perform a
linear fit, save the results to the workspace, and compute R

Click the arrow button at the lower right to open the Numerical results tab if it is not already visible.

Select the

**linear**check box in the**Plot fits**area.From the

**Fit**drop-down menu, select`linear`

if it does not already show. The Coefficients and norm of residuals area displays statistics for the linear fit.Save the fit coefficients, norm of residuals, and residuals by clicking

**Save to Workspace**.The Save Fit to Workspace dialog box opens with three check boxes and three text fields.

Select all three check boxes to save the fit coefficients, norm of residuals, and residual values.

Identify the saved variables as belonging to a linear fit. Change the variable names by adding a

`1`

to each default name (for example,`fit1`

,`normresid1`

, and`resids1`

).Click

**OK**. Basic Fitting saves residuals as a column vector of numbers, fit coefficients as a struct, and the norm of residuals as a scalar.Notice that the value that Basic Fitting computes for norm of residuals is

`98.778`

. This number is the square root of the sum of squared residuals of the linear fit.Optionally, you can verify the norm-of-residuals value that the Basic Fitting tool provided. Compute the norm-of-residuals yourself from the

`resids1`

array that you just saved:mynormresid1 = sum(resids1.^2)^(1/2) mynormresid1 = 98.7783

Compute the

*total sum of squares*of the dependent variable,`pop`

to compute R^{2}. Total sum of squares is the sum of the squared differences of each value from the mean of the variable. For example, use this code:SSpop = (length(pop)-1) * var(pop) SSpop = 1.2356e+005

`var(pop)`

computes the variance of the population vector. You multiply it by the number of observations after subtracting 1 to account for degrees of freedom. Both the total sum of squares and the norm of residuals are positive scalars.Now, compute R

^{2}, using the square of`normresid1`

and`SSpop`

:rsqlinear = 1 - normresid1^2 / SSpop rsqcubic = 0.9210

This result indicates that a linear least-squares fit of the population data explains 92.1% of its variance. As the cubic fit of this data explains 99.9% of that variance, the latter seems to be a better predictor. However, a cubic fit has four coefficients (

*x*,*x*,^{2}*x*, and a constant), while a linear fit has two coefficients (^{3}*x*and a constant). A simple R^{2}statistic does not account for the different degrees of freedom. A more appropriate measure for evaluating polynomial fits is*adjusted R*. For information about computing and using adjusted R^{2}^{2}, see Residuals and Goodness of Fit.

R^{2} measures how well your polynomial
equation *predicts* the dependent variable, not
how *appropriate* the polynomial model is for your
data. When you analyze inherently unpredictable data, a small value
of R^{2} indicates that the independent variable
does not predict the dependent variable precisely. However, it does
not necessarily mean that there is something wrong with the fit.

Suppose you want to use the cubic model to interpolate the U.S. population in 1965 (a date not provided in the original data).

In the Basic Fitting dialog box, click the button to specify a vector of

`x`

values at which to evaluate the current fit.In the

**Enter value(s)...**field, type the following value:1965

### Note

Use unscaled and uncentered

`x`

values. You do not need to center and scale first, even though you selected to scale`x`

values to obtain the coefficients in Predict the Census Data with a Cubic Polynomial Fit. The Basic Fitting tool makes the necessary adjustments behind the scenes.Click

**Evaluate**.The

`x`

values and the corresponding values for`f(x)`

computed from the fit and displayed in a table, as shown below:Select the

**Plot evaluated results**check box to display the interpolated value as a diamond marker:Save the interpolated population in 1965 to the MATLAB workspace by clicking

**Save to workspace**.This opens the following dialog box, where you specify the variable names:

Click

**OK**, but keep the Figure window open if you intend to follow the steps in the next section, Generate a Code File to Reproduce the Result.

After completing a Basic Fitting session, you can generate MATLAB code that recomputes fits and reproduces plots with new data.

In the Figure window, select

**File > Generate Code**.This creates a function and displays it in the MATLAB Editor. The code shows you how to programmatically reproduce what you did interactively with the Basic Fitting dialog box.

Change the name of the function on the first line from

`createfigure`

to something more specific, like`censusplot`

. Save the code file to your current folder with the file name`censusplot.m`

The function begins with:function censusplot(X1, Y1, valuesToEvaluate1)

Generate some new, randomly perturbed census data:

randpop = pop + 10*randn(size(pop));

Reproduce the plot with the new data and recompute the fit:

censusplot(cdate,randpop,1965)

You need three input arguments:

*x,y*values (`data 1`

) plotted in the original graph, plus an*x*-value for a marker.The following figure displays the plot that the generated code produces. The new plot matches the appearance of the figure from which you generated code except for the

*y*data values, the equation for the cubic fit, and the residual values in the bar graph, as expected.

The Basic Fitting tool calls the `polyfit`

function
to compute polynomial fits. It calls the `polyval`

function
to evaluate the fits. `polyfit`

analyzes its inputs
to determine if the data is well conditioned for the requested degree
of fit.

When it finds badly conditioned data, `polyfit`

computes
a regression as well as it can, but it also returns a warning that
the fit could be improved. The Basic Fitting example section Predict the Census Data with a Cubic Polynomial Fit displays
this warning.

One way to improve model reliability is to add data points. However, adding observations to a data set is not always feasible. An alternative strategy is to transform the predictor variable to normalize its center and scale. (In the example, the predictor is the vector of census dates.)

The `polyfit`

function normalizes by computing *z-scores*:

$$z=\frac{x-\mu}{\sigma}$$

where *x* is the predictor data, *μ* is
the mean of *x*, and *σ* is
the standard deviation of *x*. The *z*-scores
give the data a mean of 0 and a standard deviation of 1. In the Basic
Fitting UI, you transform the predictor data to *z*-scores
by selecting the **Center and scale x data** check
box.

After centering and scaling, model coefficients are computed
for the *y* data as a function of *z*.
These are different (and more robust) than the coefficients computed
for *y* as a function of *x*. The
form of the model and the norm of the residuals do not change. The
Basic Fitting UI automatically rescales the *z*-scores
so that the fit plots on the same scale as the original *x* data.

To understand the way in which the centered and scaled data is used as an intermediary to create the final plot, run the following code in the Command Window:

close load census x = cdate; y = pop; z = (x-mean(x))/std(x); % Compute z-scores of x data plot(x,y,'ro') % Plot data as red markers hold on % Prepare axes to accept new graph on top zfit = linspace(z(1),z(end),100); pz = polyfit(z,y,3); % Compute conditioned fit yfit = polyval(pz,zfit); xfit = linspace(x(1),x(end),100); plot(xfit,yfit,'b-') % Plot conditioned fit vs. x data

The centered and scaled cubic polynomial plots as a blue line, as shown here:

In the code, computation
of `z`

illustrates how to normalize data. The `polyfit`

function
performs the transformation itself if you provide three return arguments
when calling it:

[p,S,mu] = polyfit(x,y,n)

`p`

, now are based
on normalized `x`

. The returned vector, `mu`

,
contains the mean and standard deviation of `x`

.
For more information, see the `polyfit`

reference
page.