Page 201 - Computational Statistics Handbook with MATLAB

188 Computational Statistics Handbook with MATLAB

(
,
,
(a) X ∼ N 02) and Y ∼ N 01)
(
,
(
(b) X ∼ N 51) and Y ∼ N 01)
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How can you tell from the q-q plot that the scale and the location
parameters are different?
5.7. Write a MATLAB program that permutes the axes in a parallel coor-
dinates plot. Apply it to the iris data.
5.8. Write a MATLAB program that permutes the order of the variables
and plots the resulting Andrews curves. Apply it to the iris data.
5.9. Implement Andrews curves using a different set of basis functions as
suggested in the text.
5.10. Repeat Example 5.16 and use rotate3d (or the rotate toolbar button)
to rotate about the axes. Do you see any separation of the different
types of insects?
5.11. Do a scatterplot matrix of the Iris versicolor data.
5.12. Verify that the two vectors used in Equations 5.24 and 5.25 are
orthonormal.
5.13. Write a function that implements Example 5.17 for any data set. The
user should have the opportunity to input the labels.
5.14. Define a trivariate normal as your volume, f xy z,,( ). Use the
MATLAB functions isosurface and isocaps to obtain contours of
constant volume or probability (in this case).
5.15. Construct a quantile plot using the forearm data, comparing the
sample to the quantiles of a normal distribution. Is it reasonable to
model the data using the normal distribution?
5.16. The moths data represent the number of moths caught in a trap over
24 consecutive nights [Hand, et al., 1994]. Use the stem-and-leaf to
explore the shape of the distribution.
5.17. The biology data set contains the number of research papers for
1534 biologists [Tripathi and Gupta, 1988; Hand, et al., 1994]. Con-
struct a binomial plot of these data. Analyze your results.
5.18. In the counting data set, we have the number of scintillations in
72 second intervals arising from the radioactive decay of polonium
[Rutherford and Geiger, 1910; Hand, et al., 1994]. Construct a Pois-
sonness plot. Does this indicate agreement with the Poisson distribu-
tion?
5.19. Use the MATLAB Statistics Toolbox function boxplot to compare
box plots of the features for each species of iris data.
5.20. The thrombos data set contains measurements of urinary-thrombo-
globulin excretion in 12 normal and 12 diabetic patients [van Oost, et
al.; 1983; Hand, et al., 1994]. Put each of these into a column of a

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