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August 2012

This month's newsletter is the first in a multi-part series on using

the ANOVA

are using are shown in the table below.

Operator Part Results

A

1 3.29 3.41 3.64

2 2.44 2.32 2.42

3 4.34 4.17 4.27

4 3.47 3.5 3.64

5 2.2 2.08 2.16

B

1 3.08 3.25 3.07

2 2.53 1.78 2.323 4.19 3.94 4.34

4 3.01 4.03 3.2

5 2.44 1.8 1.72

C

1 3.04 2.89 2.85

2 1.62 1.87 2.04

3 3.88 4.09 3.67

4 3.14 3.2 3.11

5 1.54 1.93 1.55

The ANOVA Table for Gage R&R

In most cases, you will use computer software to do the calculations. Since this is a

relatively simple Gage R&R, we will show how the calculations are done. This

helps understand the process better. The software usually displays the results in

an ANOVA table. The basic ANOVA table is shown in the table below for the

following where k = number of operators, r = number of replications, and n=

number of parts.

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The first column is the source of variability. Remember that a Gage R&R study is a

study of variation. There are five sources of variability in this ANOVA approach: the

operator, the part, the interaction between the operator and part, the equipment

and the total.

The second column is the degrees of freedom associated with the source of

variation. The third column is the sum of squares. The calculations with these two

columns were covered in the first part of this series.

The fourth column is the mean square associated with the source of variation. The

mean square is the estimate of the variance for that source of variability (not

necessarily by itself) based on the amount of data we have (the degrees of

freedom). So, the mean square is the sum of squares divided by the degrees of

freedom. We wi l l use the mean squ are informat ion to est imate the var iance of

each source of var iat ion this is th e key to analyzing the Gage R&R resul ts.

The fifth column is the F value. This is the statistic that is calculated to determine if

the source of variability is statistically significant. It is based on the ratio of two

variances (or mean squares in this case).

The ANOVA Table Results

The data was analyzed using the SPC for Excel software. The results for theANOVA table are shown below.

Source df SS MS F p Value

Operator 2 1.630 0.815 100.322 0.0000

Part 4 28.909 7.227 889.458 0.0000

Operator by Part 8 0.065 0.008 0.142 0.9964

Equipment 30 1.712 0.057

Total 44 32.317

http://www.spcforexcel.com/anova-gage-rr-part-1http://www.spcforexcel.com/anova-gage-rr-part-1http://www.spcforexcel.com/anova-gage-rr-part-1

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Note that there is an additional column in this output the p values. This is the

column we want to examine first. If the p value is less than 0.05, it means that the

source of variation has a significant impact on the results. As you can see in the

table, the operator by part source is not significant. Its p value is 0.9964. Many

software packages contain an option to remove the interaction if the p value isabove a certain value most often 0.25. In that case, the interaction is rolled into

the equipment variation. We will keep it in the calculations here though it has little

impact since its mean square is so small.

The next column we want to look at is the mean square column. This column is an

estimate of the variance due to the source of variation. So,

MSOperators = 0.815

MSParts = 7.227

MSOperators*Parts = 0.008

MSEquipment = 0.057You might be tempted to assume, for example, that the variance due to the

operators is 0.815. However, this would be wrong. There are other sources of

variation present in all put one of these variances. We must use the Expected

Mean Square to find out what other sources of variation are present. We will use 2

to denote a variance due to a single source.

Expected Mean Squares

As stated above, the mean square column contains a variance that is related to the

source of variation in the first column. To find the variance of each source ofvariation, we have to use the expected mean square (EMS). The expected mean

square represents the variance that the mean square column is estimating.

There are algorithms that allow you to generate the expected mean squares. This

is beyond the scope of this newsletter. So, we will just present the expected mean

squares.

Lets start at the bottom with the equipment variation. This is really the within

variation (also called error). It is the repeatability portion of the Gage R&R study.

The expected mean square for equipment is the repeatability variance. The

repeatability variance is the mean square of the equipment from the ANOVA table.

Now consider the interaction expected mean square which is given by:

Note that the EMS for the interaction tern contains the repeatability variance as

well as the variance of the interaction between the operators and parts. This is

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what is estimated by the mean square of the interaction. The parts expected mean

square is shown below.

Note that the EMS for parts contains the variances for repeatability, the interactionand parts. This is what is estimated by the mean square for parts. And last, the

expected mean square for the operators is given by:

The EMS for operators contains the variances for repeatability, the interaction and

operators. This is what the mean square for operators is estimating.

The Variances of the Components

We can solve the above equations for each individual 2. Repeatability is already

related directly to the mean square for equipment so we dont need to do anything

there. The other three can be solved as follows:

We can now do the calculations to estimate each of the variances.

Note that the value of the variance for the interaction between the operators and

parts is actually negative. If this happens, the variance is simply set to zero.

% Gage R&R

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The Measurement Systems Analysis manual published by AIAG (www.aiag.org)

provides the following definition: The measurement system variation for

repeatability and reproducibility (or GRR) is defined as the following:

GRR2=EV

2+ AV

2

where EV is the equipment variance and AV is the appraiser (or operator)variance. Thus:

The total variance is the sum of the components:

We can use the total variance to determine the % contribution of each source to

the total variance. This is done by dividing the variance for each source by the total

variance. For example, the % variation due to GRR is given by:

The results for all the sources of variation are shown in the table below.

Source Variance% ofTotalVariance

GRR 0.1109 12.14%

Equipment(Repeatability)

0.0571 6.25%

Operators(Reproducibility)

0.0538 5.89%

Interaction 0.0000 0.00%

Parts 0.8021 87.86%

Total 0.9130 100.00%

Based on this analysis, the measurement system is responsibility for 12.14% of the

total variance. This may or may not be acceptable depending on the process and

what your customer needs and wants. Note that this result is based on the total

variance. It is very important that the parts you use in the Gage R&R study

represent the range of values you will get from production.One of the major problems people have with Gage R&R studies is selecting

samples that do not truly reflect the range of production. If you have to do that, you

can begin to look at how the results compare to specifications. We will take a look

at that next month as we compare the ANOVA method to the Average and Range

method for analyzing a Gage R&R experiment. You could also use a variance

calculated directly from a month's worth of production in place of the total variance

in the analysis.

http://www.aiag.org/http://www.aiag.org/http://www.aiag.org/http://www.aiag.org/

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Summary

In this newsletter, we continued our exploration of the using ANOVA to analyze a

Gage R&R experiment. We completed the ANOVA table, presented the expected

mean squares and how to use those to estimate the variances of the components,

and showed how to determine the %GRR as a percent of the total variance.In the next newsletter, we will compare the ANOVA method to the Average and

Range method for Gage R&R.

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