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If you need more advanced statistics features, you might want to use the Statistics Toolbox™ software.
Use the following MATLAB® functions to calculate the descriptive statistics for your data.
Statistics Function Summary
Average or mean value
Most frequent value
Variance, which measures the spread or dispersion of the values
The following examples apply MATLAB functions to calculate descriptive statistics:
This example shows how to use MATLAB functions to calculate the maximum, mean, and standard deviation values for a 24-by-3 matrix called count. MATLAB computes these statistics independently for each column in the matrix.
% Load the sample data load count.dat % Find the maximum value in each column mx = max(count) % Calculate the mean of each column mu = mean(count) % Calculate the standard deviation of each column sigma = std(count)
The results are
mx = 114 145 257 mu = 32.0000 46.5417 65.5833 sigma = 25.3703 41.4057 68.0281
To get the row numbers where the maximum data values occur in each data column, specify a second output parameter indx to return the row index. For example:
[mx,indx] = max(count)
These results are
mx = 114 145 257 indx = 20 20 20
Here, the variable mx is a row vector that contains the maximum value in each of the three data columns. The variable indx contains the row indices in each column that correspond to the maximum values.
To find the minimum value in the entire count matrix, reshape this 24-by-3 matrix into a 72-by-1 column vector by using the syntax count(:). Then, to find the minimum value in the single column, use the following syntax:
min(count(:)) ans = 7
Subtract the mean from each column of the matrix by using the following syntax:
% Get the size of the count matrix [n,p] = size(count) % Compute the mean of each column mu = mean(count) % Create a matrix of mean values by % replicating the mu vector for n rows MeanMat = repmat(mu,n,1) % Subtract the column mean from each element % in that column x = count - MeanMat
Note: Subtracting the mean from the data is also called detrending. For more information about removing the mean or the best-fit line from the data, see Detrending Data.
The Data Statistics dialog box helps you calculate and plot descriptive statistics with the data. This example shows how to use MATLAB Data Statistics to calculate and plot statistics for a 24-by-3 matrix, called count. The data represents how many vehicles passed by traffic counting stations on three streets.
This section contains the following topics:
Load and plot the data:
load count.dat [n,p] = size(count); % Define the x-values t = 1:n; % Plot the data and annotate the graph plot(t,count) legend('Station 1','Station 2','Station 3','Location','northwest') xlabel('Time') ylabel('Vehicle Count')
Note: The legend contains the name of each data set, as specified by the legend function: Station 1, Station 2, and Station 3. A data set refers to each column of data in the array you plotted. If you do not name the data sets, default names are assigned: data1, data2, and so on.
In the Figure window, select Tools > Data Statistics .
The Data Statistics dialog box opens and displays descriptive statistics for the X- and Y-data of the Station 1 data set.
Select a different data set in the Statistics for list: Station 2.
This displays the statistics for the X and Y data of the Station 2 data set.
Select the check box for each statistic you want to display on the plot, and then click Save to workspace.
For example, to plot the mean of Station 2, select the mean check box in the Y column.
This plots a horizontal line to represent the mean of Station 2 and updates the legend to include this statistic.
The Data Statistics GUI uses colors and line styles to distinguish statistics from the data on the plot. This portion of the example shows how to customize the display of descriptive statistics on a plot, such as the color, line width, line style, or marker.
Note: Do not edit display properties of statistics until you finish plotting all the statistics with the data. If you add or remove statistics after editing plot properties, the changes to plot properties are lost.
To modify the display of data statistics on a plot:
In the MATLAB Figure window, click the (Edit Plot) button in the toolbar.
This step enables plot editing.
Double-click the statistic on the plot for which you want to edit display properties. For example, double-click the horizontal line representing the mean of Station 2.
This step opens the Property Editor below the MATLAB Figure window, where you can modify the appearance of the line used to represent this statistic.
In the Property Editor, specify the Line and Marker styles, sizes, and colors.
This portion of the example shows how to save statistics in the Data Statistics GUI to the MATLAB workspace.
Note: When your plot contains multiple data sets, save statistics for each data set individually. To display statistics for a different data set, select it from the Statistics for list in the Data Statistics GUI.
In the Data Statistics dialog box, click the Save to workspace button.
In this example, save only the Y data. Enter the variable name as Loc2countstats.
This step saves the descriptive statistics to a structure. The new variable is added to the MATLAB workspace.
To view the new structure variable, type the variable name at the MATLAB prompt:
Loc2countstats = min: 9 max: 145 mean: 46.5417 median: 36 mode: 9 std: 41.4057 range: 136
This portion of the example shows how to generate a file containing MATLAB code that reproduces the format of the plot and the plotted statistics with new data.
In the Figure window, select File > Generate Code.
This step creates a function code file and displays it in the MATLAB Editor. The code can programmatically reproduce what you did interactively with the Data Statistics GUI and the Property Editor.
Change the name of the function on the first line of the file from createfigure to something more specific, like countplot. Save the file to your current folder with the file name countplot.m.
Generate some new, random count data:
randcount = 300*rand(24,3);
Reproduce the plot with the new data and the recomputed statistics: