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Finding outliers in a data set Detecting unusual numbers or outliers in a data set is important in many disciplines, because the outliers identify interesting phenomena, extreme events, or invalid experimental results. A simple method to check if a data value is an outlier is to see if the value is a significant number of standard deviations away from the mean of the data set. For example, is an outlier if where Hx is the data set mean, x is the data set standard deviation, and N is the number of standard deviations deemed significant. Assign outlierData with all values in userData that are numberStdDevs standard deviations from userData’s mean. Hint: use logical indexing to return the outlier data values. Ex: If userData is [9, 50, 51, 49, 100 and numberStdDevs is 1, then outlierData is [9, 1001.
Expert Answer
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Sample Output:
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%Define the function getOutliers() with parameters userData
%and numberStdDevs.
function outlierData = getOutliers(userData, numberStdDevs)
%Create an array of x values of size 5 and initialize
%it wit 0 values.
arr_x_val = zeros(1,5);
%Calculate the mean of the user data given.
U_k = mean(userData);
%Calculate standard deviation of the user data given.
sigma_k = std(userData);
%Create an array to store the outlier data.
outlierData = [];
%Traverse the array userData.
for index = 1:length(userData)
%Check if the user data – mean is greater than the
%number of standatd deviation into data set
%standard deviation.
if (abs(userData(index)-
U_k)>(numberStdDevs*sigma_k));
%Update the value into the array of x values.
arr_x_val(index) = 1;
%Update the value into the array outLier data
%with the current user data value.
outlierData = [outlierData userData(index)];
%End the if statement.
end
%End the for loop.
end
%End the function getOutliers().
end
%Code to call the function getOutliers().
outlierData = getOutliers([9, 50, 51, 49, 100], 1)
outlierData = getOutliers([76, 79, 84, 68, 85, 23, 105, 47, 97, 96, 39], 1)
outlierData = getOutliers([76, 79, 84, 68, 85, 23, 105, 47, 97, 96, 39], 0.5)
