Attribute charts are a kind of control chart where you display information on defects and defectives. Helps you visualize the enemy – variation!

Just like the name would indicate, Attribution Charts are for attribute data – data that can be counted – like # of defects in a batch.

If your process can be measured in attribute data, then attribute charts can show you exactly where in the process you’re having problems, if any.

Types of Attribute Charts

There are four types of Attribute Charts:

• p Charts (proportion charts)
• np Charts
• C Charts
• u Charts

Using Attribute Charts

Attribute charts are used for charting either-or conditions over time for either static samples sizes (ex 10 samples every week) or varying sample sizes.

Six Sigma certification exams like to throw curveballs about how and when to apply certain attribute charts to different situations. Here’s a quick way for you to determine which chart to use in which situation.

How to Pick Which Attribute Chart to Use for Defects or Defectives

Assuming that 1 or more defects in a product makes that product entirely defective, you can use the following guide to pick which one to use.

How to Pick Which Attribute Chart to Use for Displaying Raw Counts or Percentages

• Display Raw Counts:
  • np chart
  • c chart
• Display Percentages:
  • p chart
  • u chart

How to Pick Which Attribute Chart to Use for Static vs Variable Sample Size

• Static (constant) sample size:
  • np
  • c
• Variable sample size:
  • p
  • u

Different Attribute Charts

There are 4 main attribute charts. Let’s take a close look at each.

Type of Attribute Chart	What Gets Displayed?	What Kind of Sample Size?	Type of Data Used	Example
NP	# times something happens	Constant size	happens/ does not happen	# transactions in a static sample set with one or more errors.
P	% of Samples in which something happens	Constant or Varying size	happens/ does not happen	Sum of all transactions with an error per month charted month-over-month.
C	# times total something happens	Constant size	has a condition OR has more than one condition	# errors in all transactions in a static sample set. (transaction can have more that one kind of error.)
U	% of total in which something happens	Constant or Varying size	has a condition OR has more than one condition	Sum of all errors across all transactions per month charted month-over-month.

Attribute Charts: p Chart (proportion chart)

What is a p Chart:

• Evaluates the stability of a process when we are evaluating the proportion of defects vs in good order as a percentage.
• The plot shows the percentage of defectives.

When to Use a p Chart:

• Sample sizes are NOT equal.
• Have discrete data.

How to Use a p Chart:

Step 1) Measure P-bar:

p bar = the fraction rejected = total defectives / total inspected.

Step 2) Find Control Limits:

3 SD = 3 (SQRT((pbar * (1-pbar))/n))

N refers to a SINGLE instance of a sample size, not the # of sample sizes (or rows) listed. Since there are multiple sample sizes, we use the largest one on the list – the worst case. (You can establish UCL & LCL with the best case to get a different interpretation.

Upper control limit = pbar + 3 SD

Lower control limit = pbar – 3 SD

Attribute Charts: np Chart

What are np Charts:

• Evaluates the stability of a process when we are evaluating the proportion of defects as a raw number.
• The plot shows the # of defectives.

When to Use np Charts:

np Charts are for monitoring the number of times a something binary happens (normally an error or defect). You’re also dependent on the sample size because you. You’re looking for a binary case to trigger adding the point to the graph – like the hamburger was either cooked or undercooked.

• Sample sizes are equal or constant.
• Subgroups are the same size.
• Attributes are discrete and binary (ex. yes vs no; up vs down)

How to Use np Charts:

Step 1) Calculate p as above.

Step 2) Calculate np.

np bar = total # defective / total samples.

The total samples are the # of rows listed.

Step 3) Calculate the control limits

UCL = np bar + 3 * (SQRT(npbar*(1-pbar)))

LCL = np bar – 3 * (SQRT(npbar*(1-pbar)))

Attribute Charts: C Charts

What they do:

• Evaluates the stability of counted data
• Measures defects per unit.  Helpful if you have a list of # of defects per unit ID.
• The plot shows the # of defectives.

When to Use:

• Total opportunity population is large compared to # defects.
• When you cannot count “not a defect.”
• Data type is discrete but each count has an equal opportunity of coming up.

c bar = total # defects / # units

UCL = c bar + 3 * (SQRT(c))

LCL =c bar – 3 * (SQRT(c))

Attribute Charts: u Chart

What are u Charts:

• Evaluates the stability of counted data
• Measuring variable defects per unit. Helpful for when you have lots of varying sample size.
• The plot shows the % of defectives.

When to Use u Charts:

• Sample size varies – ex. Multiple types of a defect.

How to use u Charts

Step 1) Calculate the number of defects per unit in each lot.

u = c / n = number of defects in the lot / # of units in the lot.

Then repeat this for all of the lots.

Step 2) Calculate u bar

u bar = total defects in all of the lots total / total # units in all of the lots combined.

Step 3) Calculate UCL & LCL for EACH lot size

Ex. if you have lot sizes of 1, 2, 3, and 4 – you must create an UCL & LCL for each of them!

UCL = ubar + 3* (SQRT(ubar / n))  where n is the # of items in the lot size

LCL = ubar – 3* (SQRT(ubar / n))

This makes the c chart look like a control chart married with a box plot.

Videos about Attribute Charts

ASQ Six Sigma Black Belt Attribute Charts Questions

Question: Which of the following control charts is most appropriate for monitoring the number of defects on different sample sizes?

(A) u
(B) np
(C) c
(D) p

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ASQ Six Sigma Green Belt Attribute Chart Questions

Which of the following control charts is used to monitor discrete data?

(A) p
(B) I & mR
(C) X Bar
(D) X Bar – R

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