Attribute charts: p chart is also known as the control chart for proportions. It is generally used to analyze the proportions of non-conforming or defective items in a process. It uses binomial distribution to measure the proportion of defectives or non confirming units in a sample. In p-chart, proportions are plots on the y-axis and the number of samples on the x-axis. The centerline of p chart (p̅) is the total number of defectives or non-conforming units divided by the total number of items sampled. Selection of Control chartThe control chart is a graph used to study how process changes over time. A control chart always has a central line for average, an upper line for upper control limit, and lower line for the lower control limit. The control limits are ±3σ from the centerline. Selection of appropriate control chart is very important in control charts mapping, otherwise ended up with inaccurate control limits for the data. X̅ and R chart are used for measurable quantities such as length, weight height. Attribute control charts are used for attribute data. In other words, the data that counts the number of defective items or the number of defects per unit. For example number of tubes failed on a shop floor. Unlike variable charts, only one chart is plotted for attributes. Why and When do you use a p Chart?p chart is one of the quality control charts is used to assess trends and patterns in counts of binary events (e.g., pass, fail) over time. p charts are used when the subgroups are not equal in size and compute control limits based on the binomial distribution. There are basically four types of control charts that exist for attribute data. np chart is for the number of defectives, and u chart is for the number of defects per unit, c chart is for the number of defects. Similarly, the p chart plots the proportion of defective items. Assumptions of Attribute charts: p chart
p chart formulas
How do you Create a p Chart
Example of using a p Chart in a Six Sigma projectExample: ABC manufacturing produces thousands of tubes every day. A Quality inspector randomly drawn variable samples for 20 days and reported the defective tubes for each sample size. Based on the given data, prepare the control chart for fraction defective and determine the process in statistical control? Calculate each sub groups non conformities rate= np/n
Compute p̅ = total number of defectives / total number of samples =Σnp/Σn =346/23040= 0.01502
Calculate upper control limit (UCL) and low control limit (LCL). Since the sample sizes are unequal, the control limits vary from sample interval to sample interval. Plot the graph with proportion on the y-axis, number of samples on the x-axis. Draw center line (p̅), UCL and LCL. Interpret the chart: The proportion of defectives on day 13 is higher than the upper control limit (UCL). Therefore the process is out of control. Black belts or statisticians to identify the root cause for the cause and take appropriate corrective action to bring the process in control. P Chart Excel TemplateUses of p chart
Videos of Attribute Charts: p Charts
Additional Helpful Links Attribute Charts: p ChartsAttribute Chart: P chart Which is the control chart for fraction defective?P-chart (Proportion or Fraction Defective Chart):
It is used to monitor and control the fraction produced in a process that is defective or non-conforming.
What control chart is used to monitor the process mean?A control chart used to monitor the process mean is the: x-bar chart.
What type of control chart would be used to monitor the number of defectives in the output of a process for making iron castings?Explanation: The p-chart or the Control Chart for Fraction Nonconforming is used to plot “the number of defectives in the output of any manufacturing process” data, on a control chart.
What type of control chart would be used to monitor the number of defectives for a process with a constant sample size?np chart is also known as the control chart for defectives (d-chart) . It is generally used to monitor the number of non-conforming or defective items in the measurement process.
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