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5 Data Analysis

Missing Data

In MATLAB, NaN (Not a Number) values represent missing data. NaN values

allow variables with missing data to maintain their structure—in this case,

24-by-1 vectors with consistent indexing across all three intersections.

Check the data at the third intersection for

NaN values using the MATLAB

isnan function:

c3 = count(:,3); % Data at intersection 3

c3NaNCount = sum(isnan(c3))

c3NaNCount =

isnan

returns a logical vector the same size as c3, w ith entries indicating the

presence (

1)orabsence(0)ofNaN v a lues for each of the 24 elements in the

data. In this case, the logical values sum to

0,sotherearenoNaN values

in the data.

NaN values are i ntroduced into the data in the section on “Outliers” on page

5-4.

See “Removing and Interpolating Missing Values” in the M ATLAB Data

Analysis d o cu mentatio n for more inf ormation on handling missing da ta in

MATLAB.

Outliers

Outliers are data values that are dramatically different from patterns in

the rest of the data. They may be due to measurement error, or they may

represent sig ni ficant features in the data . Identify ing outliers, and de c iding

what to do with the m , depends on an under standing of the data and its source.

One common method for identifying outliers is to look for v alues m ore than

a certain number of standard deviations

from the mean μ. The following

code plots a histog ram of the data at the third intersection together with

lines at μ and μ + n

,forn =1,2:

bin_counts = hist(c3); % Histogram bin counts

N = max(bin_counts); % Maximum bin count

mu3 = mean(c3); % Data mean

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MATLAB POLYSPACE RELEASE NOTES Manuale Utente Pagina 174