A quality engineer for a drug manufacturer wants to determine the shelf life for a medication. The concentration of the active ingredient in the medication decreases over time. The engineer wants to determine when the concentration reaches 90% of the intended concentration. The engineer randomly selects 8 batches of medication from a larger population of possible batches and tests one sample from each batch at nine different times.
To estimate the shelf life, the engineer does a stability study. Because the batches are a random sample from a larger population of possible batches, batch is a random factor instead of a fixed factor.
Choose Stat > Regression > Stability Study > Stability Study.
In Response, enter Drug%.
In Time, enter Month.
In Batch, enter Batch.
In Lower spec, enter 90.
Click Options.
In the drop-down list, select Batch is a
random factor.
Click OK and then click Graphs.
Under Residuals Plots, choose Four in one.
Click OK in each dialog box.
Interpret the results
The p-value that compares the models with and without the Month by Batch interaction is 0.059. Because the p-value is less than the significance level of 0.25, the analysis uses the model with the Month by Batch interaction. The shelf life, which is approximately 53 months, is an estimate of the how long the engineer can be 95% confident that 95% of the drug is above the lower specification limit. The estimate applies to any batch that the engineer randomly selects from the process.
The marginal residuals may not follow a normal distribution with constant variance. The points on the normal probability plot do not follow the line well. One reason for the nonnormal behavior of the marginal residuals is that, when the final model includes the batch by time interaction, the variance of the marginal residuals depends on the time variable and may not be constant. You can use the conditional residuals to check the normality of the error term in the model.
Factor Information
Factor
Type
Number of Levels
Levels
Batch
Random
8
1, 2, 3, 4, 5, 6, 7, 8
Model Selection with α = 0.25
Model
-2 LogLikelihood
Difference
P-Value
Month Batch Month*Batch
128.599
Month Batch
133.424
4.82476
0.059
Variance Components
Source
Var
% of Total
SE Var
Z-Value
P-Value
Batch
0.527409
72.91%
0.303853
1.735739
0.041
Month*Batch
0.000174
0.02%
0.000142
1.224102
0.110
Error
0.195739
27.06%
0.036752
5.325932
0.000
Total
0.723322
Model Summary
S
R-sq
R-sq(adj)
0.442424
96.91%
96.87%
Coefficients
Term
Coef
SE Coef
DF
T-Value
P-Value
Constant
100.060247
0.268706
7.22
372.378347
0.000
Month
-0.138766
0.005794
7.22
-23.950196
0.000
Random Effect Predictions
Term
BLUP
StDev
DF
T-Value
P-Value
Batch
1
1.359433
0.313988
12.45
4.329567
0.001
2
0.395375
0.313988
12.45
1.259203
0.231
3
0.109151
0.313988
12.45
0.347629
0.734
4
-0.409322
0.313988
12.45
-1.303623
0.216
5
-0.135643
0.313988
12.45
-0.432001
0.673
6
-1.064736
0.313988
12.45
-3.391006
0.005
7
0.049420
0.313988
12.45
0.157394
0.877
8
-0.303678
0.313988
12.45
-0.967164
0.352
Month*Batch
1
0.006281
0.008581
10.49
0.731925
0.480
2
0.019905
0.008581
10.49
2.319537
0.042
3
-0.013831
0.008581
10.49
-1.611742
0.137
4
0.003468
0.008581
10.49
0.404173
0.694
5
0.001240
0.008581
10.49
0.144455
0.888
6
0.000276
0.008581
10.49
0.032144
0.975
7
-0.010961
0.008581
10.49
-1.277272
0.229
8
-0.006378
0.008581
10.49
-0.743220
0.474
Marginal Fits and Diagnostics for Unusual Observations
Obs
Drug%
Fit
DF
Resid
Std Resid
10
101.564000
99.643950
7.04368
1.920050
2.375254
R
31
100.618000
98.811354
7.05273
1.806646
2.213787
R
55
98.481000
96.729866
8.87383
1.751134
2.033482
R
Shelf Life Estimation
Lower spec limit = 90 Shelf life = time period in which you can be 95% confident that at least 95% of response is above lower spec limit Shelf life for all batches = 53.1818
Check the conditional residuals
Choose Stat > Regression > Stability Study > Stability Study.
Click Graphs.
In Residuals for plots, select Conditional regular.