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136                        Computational Statistics Handbook with MATLAB


                             ing, such as regression, is done using bivariate data, the analyst should
                             always look at a scatterplot to see what type of relationship is reasonable. We
                             will explore this further in Chapters 7 and 10.
                              A scatterplot can be obtained easily in MATLAB using the plot command.
                             One simply enters the marker style or plotting symbol as one of the argu-
                             ments. See the help on plot for more information on what characters are
                             available. By entering a marker (or line) style, you tell MATLAB that you do
                             not want to connect the points with a straight line, which is the default. We
                             have already seen many examples of how to use the plot function in this
                             way when we constructed the quantile and q-q plots.
                              An alternative function for scatterplots that is available with MATLAB is
                             the function called scatter. This function takes the input vectors x and y
                             and plots them as symbols. There are optional arguments that will plot the
                             markers as different colors and sizes. These alternatives are explored in
                             Example 5.11.

                             Example 5.11
                             We first generate a set of bivariate normal random variables using the tech-
                             nique described in Chapter 4. However, it should be noted that we find the
                             matrix R in Equation 4.19 using singular value decomposition rather than
                             Cholesky factorization. We then create a scatterplot using the plot function
                             and the scatter function. The resulting plots are shown in Figure 5.16 and
                             Figure 5.17.

                                % Create a positive definite covariance matrix.
                                vmat = [2, 1.5; 1.5, 9];
                                % Create mean at (2,3).
                                mu = [2 3];
                                [u,s,v] = svd(vmat);
                                vsqrt = ( v*(u'.*sqrt(s)))';
                                % Get standard normal random variables.
                                td = randn(250,2);
                                % Use x=z*sigma+mu to transform - see Chapter 4.
                                data = td*vsqrt+ones(250,1)*mu;
                                % Create a scatterplot using the plot function.
                                % Figure 5.16.
                                plot(data(:,1),data(:,2),'x')
                                axis equal
                                % Create a scatterplot using the scatter fumction.
                                % Figure 5.17.
                                % Use filled-in markers.
                                scatter(data(:,1),data(:,2),'filled')
                                axis equal
                                box on



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