How do I make a control chart in Matlab?

First load the sample data.

  1. load parts.
  2. st = controlchart(runout,’rules’,’we2′);
  3. x = st.mean; cl = st.mu; se = st.sigma./sqrt(st.n); hold on plot(cl+2*se,’m’)
  4. R = controlrules(‘we2’,x,cl,se); I = find(R)
  5. I = 6×1 21 23 24 25 26 27.

What is XBAR Matlab?

The crossbars (X-bars) provides flexibility to connect device inputs, outputs, and internal resources in a variety of configurations using Input X-BAR, Output X-BAR, and ePWM X-BAR.

Can you make a chart in Matlab?

Several charts are available in MATLAB, including the heatmap chart, which visualizes matrix values overlaid on colored grid squares, and the geobubble chart, which provides a quick way to plot discrete data points on a map (Figure 1).

What are Shewhart rules?

Tests for special-cause variation determine when a process needs further investigation. There are various tests that can be used in conjunction with a control chart to identify special-cause variation: Test. Rule.

Which one is a control chart?

The control chart is a graph used to study how a process changes over time. Data are plotted in time order. A control chart always has a central line for the average, an upper line for the upper control limit, and a lower line for the lower control limit. These lines are determined from historical data.

How do you do a moving average in Matlab?

M = movmean( A , k ) returns an array of local k -point mean values, where each mean is calculated over a sliding window of length k across neighboring elements of A .

How do you plot data on a graph in MATLAB?

Load and Plot Data from Text File Each data column in the file represents data for one intersection. Import data into the workspace using the load function. Loading this data creates a 24-by-3 matrix called count in the MATLAB workspace. Get the size of the data matrix.

How do you count runs on a run chart?

A run is a series of points in a row on one side of the median. A non-random pattern or signal of change is indicated by too few or too many runs or crossings of the median line. To determine the number of runs above and below the median, count the number of times the data line crosses the median and add one.