A variable control chart monitors a process using continuous numerical measurements rather than defect counts. It reveals stability, consistency, small shifts, and trends over time, helping assess whether performance remains within control limits and specified tolerances. section 14.3 section 14.0
Choose charts according to the process characteristic being controlled:
- Pair an x-bar chart with an r-chart to monitor process mean and variability. section 14.3
- Use s-charts, I-MR charts, or EWMA charts among the available approaches for continuous measurements and process variation. section 14.4
- Interpret control limits, mean, and standard deviation together; relying on one chart can produce incomplete conclusions. section 14.4
Piston-ring diameter monitoring
A pandas DataFrame named pistons contains 200 diameter measurements divided into 40 batches of five observations. Import the data with pd.read_csv(), inspect it with info(), and use the sample groups to construct and interpret variable control charts. The measurements have a mean diameter of 74.003605 and a standard deviation of 0.011417. section 14.3
>>> print(pistons.info()) <class 'pandas.core.frame.DataFrame'> RangeIndex: 200 entries, 0 to 199 Data columns (total 2 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 diameter 200 non-null float64 1 sample 200 non-null int64 dtypes: float64(1), int64(1) memory usage: 3.3 KB None
Inspect the measurement columns, sample identifiers, row count, and numeric data types before chart construction. section 14.3
