1. Multiple Comparisons in Factorial Experiments
If Main Effects are significant AND Interactions are NOT significant:
Use multiple comparisons on factor main effects (factor means).
If Interactions ARE significant:
1) Multiple comparisons on main effect level means
should NOT be done as they are meaningless.
2) Should instead perform multiple comparisons among
all factorial means of interest.

2. Multiple Comparisons in Factorial Experiments
In addition, interactions must be decomposed to determine what they mean
A significant interaction between two variables means that one factor value changes as a function of the other, but gives no specific information
The most simple and common method of interpreting interactions is to look at a graph

3. Problems in factorial experiment
In some two-factor experiments the level of one factor, say B, is not really similar with the other factor.
There are multifactor experiments that address common economic and practical constraints encountered in experimentation with real systems.
There is no link from any sites on one area to any sites on another area.
Nested and Split-plot design

4. Cross and nested Factorials design
The levels of factor A are said to be crossed with the level of factor B if every level of A occurs in combinations with every level of B
Factorials design
The levels of factor B are said to be nested within the level of factor A if the levels of B can be divided into subsets (nests) such that every level in any given subset occurs with exactly one level of A
Nested design

5. Agricultural Field Trial
Investigate the yield of a new variety of crop
Factors
Insecticides
Fertilizers
Experimental Units
Farms
Fields within farms
Experimental Design ?
Fertilizers can be applied to individual fields;
Insecticides must be applied to an entire farm
from an airplane

6. Agricultural Field Trial
Farms
Insecticides applied to farms
One-factor ANOVA
Main effect: Insecticides
MSE: Farm-to-farm variability

7. Agricultural Field Trial
Fertilizers applied to fields
One-factor ANOVA
Main Effect: Fertilizers
MSE: Field-to-field variability
Fields

8. Agricultural Field Trial
Farms
Fields
Insecticides applied to farms, fertilizers to fields
Two sources of variability
Insecticides subject to farm-to-farm variability
Fertilizers and insecticides x fertilizers subject to field-to-field variability

9. Nested Design
Factorial design when the levels of one factor (B) are similar, but not identical to each other at different levels of another factor (A). b1 b3 a1 a2 b2 b4

10. Nested Design

11. Nested Design
A factor B is considered nested in another factor, A if the levels of factor B differ for different levels of factor A.
The levels of B are different for different levels of A.
Synonyms indicating nesting:
Hierarchical, depends on, different for, within, in, each

12. Examples - Nested

13. Examples - Nested

14. Examples - Crossed

15. Examples - Crossed

16. Examples - Nested

17. Two-Stage Nested Design Statistical Model and ANOVA

18. Two-Stage Nested Design Statistical Model and ANOVA

19. Residual Analysis
Calculation of residuals.

20. m-Stage Nested Design

21. m-Stage Nested Design
Test statistics depend on the type of factors and the expected mean squares.
Random.
Fixed.

22. Expected Mean Squares
Assume that fixtures and layouts are fixed, operators are random – gives a mixed model (use restricted form).

23. Alternative Analysis
If the need detailed analysis is not available, start with multi-factor ANOVA and then combine sum of squares and degrees of freedom.
Applicable to experiments with only nested factors as well as experiments with crossed and nested factors.
Sum of squares from interactions are combined with the sum of squares for a nested factor – no interaction can be determined from the nested factor.

24. Alternative Analysis

25. Further phenomena in Experimental Design
In a single factor experiment has different features, such as:
Multi-locations
Repeated measurements
Factorial experiment can have either of these features:
Two hierarchically nested factors, with additional crossed factors occurring within levels of the nested factor
Two sizes of experimental units, one nested within the other, with crossed factors applied to the smaller units
Split-Plot Design

26. Split-plot Design
There are numerous types of split-plot designs, including the Latin square split plot design, in which the assignment of the main treatments to the main plots is based on a Latin square design.
A split-plot design can be conceptualized as consisting of two designs: a main plot design and a subplot design.
The main plot design is the protocol used to assign the main treatment to the main units. In a completely randomized split-plot design, the main plot design is a completely randomized design, in a randomized complete block design, by contrast, the main plot design is a RCBD.
The subplot design in a split-plot experiment is a collection of a RCBD, where a is the number of main treatment. Each of these RCBDs has b treatments arranged in r blocks (main plots), where b is the number of sub treatment.

27. Split-Plot Design Whole-Plot Experiment : Whole-Plot Factor = A
Level a1
Level a2
Level a2
Level a1

28. Split Plot Designs Analysis of Variance Table

29. Split-Plot Design Split-Plot Experiment : Split-Plot Factor = B
Level a1
Level a2
Level a2
Level a1

30. Split Plot Designs Analysis of Variance Table

31. Agricultural Field Trial

32. Agricultural Field Trial
Insecticide 2
Insecticide 1
Insecticide 2
Insecticide 2
Insecticide 1
Insecticide 1

33. Agricultural Field Trial
Insecticide 2
Insecticide 1
Insecticide 2
Fert A
Fert B
Fert A
Fert B
Fert B
Fert B
Fert A
Fert A
Fert B
Fert A
Fert B
Fert A
Fert A
Insecticide 2
Fert B
Fert A
Fert A
Fert B
Fert A
Fert B
Fert B
Fert B
Fert B
Fert A
Fert A
Fert B
Fert B
Fert A
Fert B
Fert A
Fert B
Fert A
Fert A
Fert A
Fert A
Fert A
Fert B
Fert B
Fert B
Fert A
Fert B
Insecticide 1
Insecticide 1

34. Agricultural Field Trial
Whole Plots = Farms
Large Experimental Units
Split Plots = Fields
Small Experimental Units

35. Agricultural Field Trial
Whole Plots = Farms
Large Experimental Units
Whole-Plot Factor = Insecticide
Whole-Plot Error = Whole-Plot Replicates
Split Plots = Fields
Small Experimental Units
Split-Plot Factor = Fertilizer
Split-Plot Error = Split-Plot Replicates

36. The Split-Plot Design
a multifactor experiment where it is not practical to completely randomize the order of the runs.
Example – paper manufacturing
Three pulp preparation methods.
Four different temperatures.
The experimenters want to use three replicates.
How many batches of pulp are required?

37. The Split-Plot Design
Pulp preparation method is a hard-to-change factor.
Consider an alternate experimental design:
In replicate 1, select a pulp preparation method, prepare a batch.
Divide the batch into four sections or samples, and assign one of the temperature levels to each.
Repeat for each pulp preparation method.
Conduct replicates 2 and 3 similarly.

38. The Split-Plot Design
Each replicate has been divided into three parts, called the whole plots.
Pulp preparation methods is the whole plot treatment.
Each whole plot has been divided into four subplots or split-plots.
Temperature is the subplot treatment.
Generally, the hard-to-change factor is assigned to the whole plots.
This design requires 9 batches of pulp (assuming three replicates).

39. The Split-Plot Design

40. The Split-Plot Design
There are two levels of randomization restriction.
Two levels of experimentation

41. Experimental Units in Split Plot Designs
Possibilities for executing the example split plot design.
Run separate replicates. Each pulp prep method (randomly selected) is tested at four temperatures (randomly selected).
Large experimental unit is four pulp samples.
Smaller experimental unit is a an individual sample.
If temperature is hard to vary select a temperature at random and then run (in random order) tests with the three pulp preparation methods.
Large experimental unit is three pulp samples.

42. The Split-Plot Design
Another way to view a split-plot design is a RCBD with replication.
Inferences on the blocking factor can be made with data from replications.

43. The Split-Plot Design Model and Statistical Analysis
Sum of squares are computed as for a three factor factorial design without replication.

44. RCBD Model

45. The Split-Plot Design Model and Statistical Analysis
There are two error structures; the whole-plot error and the subplot error

46. Split-Plot Design Whole-Plot Experiment : Whole-Plot Factor = A
Level a1
Level a2
Level a2
Level a1

47. Split-Plot Design Split-Plot Experiment : Split-Plot Factor = B
Level a1
Level a2
Level a2
Level a1

48. Split-Plot Design Split-Plot Experiment : Split-Plot Factor = B
Level a1
Level a2
Level a2
Level a1