Computing with Descriptive Statistics - MATLAB & Simulink (2024)

% Load the sample dataload count.dat% Find the maximum value in each columnmx = max(count)% Calculate the mean of each columnmu = mean(count)% Calculate the standard deviation of each columnsigma = std(count)

mx = 114 145 257mu = 32.0000 46.5417 65.5833sigma = 25.3703 41.4057 68.0281

[mx,indx] = max(count)

mx = 114 145 257indx = 20 20 20

min(count(:))ans = 7

% Get the size of the count matrix[n,p] = size(count)% Compute the mean of each columnmu = mean(count)% Create a matrix of mean values by% replicating the mu vector for n rowsMeanMat = repmat(mu,n,1)% Subtract the column mean from each element% in that columnx = 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 Remove Linear Trends from Timetable Data.

1. Load and plot the data:

   load count.dat[n,p] = size(count);% Define the x-valuest = 1:n;% Plot the data and annotate the graphplot(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.

2. 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.

   Note

   The Data Statistics dialog box displays a range, which is the difference between the minimum and maximum values in the selected data set. The dialog box does not display the range on the plot.

3. Select a different data set in the Data Statistics for list: Station 2.

   This displays the statistics for the X and Y data of the Station 2 data set.

4. 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.

   

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.

1. In the MATLAB Figure window, click the (Edit Plot) button in the toolbar.

   This step enables plot editing.

2. 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 Inspector, where you can modify the appearance of the line used to represent this statistic.

   

3. In the Property Inspector window, specify the line and marker styles, sizes, and colors.

   Tip

   Alternatively, right-click the statistic on the plot, and select an option from the shortcut menu.

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 Data Statistics for list in the Data Statistics dialog box.

1. In the Data Statistics dialog box, click the Save to Workspace button.

2. In the Save Statistics to Workspace dialog box, select options to save statistics for either X data, Y data, or both. Then, enter the corresponding variable names.

   In this example, save only the Y data. Enter the variable name as Loc2countstats.

   

3. Click OK.

   This step saves the descriptive statistics to a structure. The new variable is added to the MATLAB workspace.

1. In the Figure window, select File > Generate Code.

   This step creates a function code file and displays it in the MATLAB Editor.

2. 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.

3. Generate some new, random count data:

   rng('default')randcount = 300*rand(24,3);

4. Reproduce the plot with the new data and the recomputed statistics:

   countplot(t,randcount)