In: Statistics and Probability
Four factors, each with two levels were studied in a study of the performance of a process: time (A), humidity (B), pressure (C) and the temperature (D). One replicate was run a design 2⁴ and response is presented in the following table.
For this experiment use standard levels for each factor (1,2).
Use MINITAB to set up and analyze the experiment up to 2-level interactions. (i.e. AB, BC, etc…) Note: the response is in not randomized runs. Explain the results!
Response |
5000 |
2268 |
1183 |
1792 |
2023 |
1575 |
2800 |
1575 |
7000 |
4375 |
1183 |
4032 |
2527 |
4500 |
2023 |
3703 |
Here, 4 Factors 2 levels 1 replicate = 16 runs
3 2
To get the answers for the below , you need the data for the time (A), humidity (B), pressure (C) and the temperature (D).
Follow the steps mentioned, once Minitab is installed :
- Type your data in Minitab
- Go to Stat , then DOE, Factorial, Analyze Factorial Design
- Click Terms, and Under Include terms in the model up through order , choose 2 ( since its a 2 level factor)
- Click ok
- Once the Minitab displays the ANOVA table, read the p value against each factors and their interactions to see what factors are significant. P value <0.05 , the conclusion is that the effects are statistically significant.
- Check the ANOVA table.
Check the R-Squared to see in the model fits the data well.
Interpretation of the Dependent Variable (Response Variable) (Graphs made in R software)
We don't see any specific structure which shall fit a response model. There is a huge variation in the data points.
QQ Plot