Helps to select and generate effective designs for use in industrial experiments (R.W. Payne).

### Option

`STATEMENT` = text |
Saves a command to recreate the design |
---|

### No parameters

### Description

`AGINDUSTRIAL`

is a procedure which can be used interactively to form designs that are popular in industrial experiments. The process involves answering questions, posed by Genstat, first to select the particular type of design, then to give details such as names of factors, numbers of treatments, and so on. A range of subsidiary procedures may be called, depending on the type of design selected. If you wish to avoid some of the question-and-answer process, the subsidiary procedures can also be called directly. They all have options and parameters which provide an alternative way of supplying the information otherwise obtained by the various questions and, provided you supply all the required information, they can also be used in batch. The `STATEMENT`

option of `AGINDUSTRIAL`

allows you to save a Genstat text structure containing a command to use the relevant subsidiary procedure, and setting all the options and parameters required to recreate the design.

There are 6 types of design.

Factorial designs from a repertoire (with blocking) – these have several treatment factors and a single blocking factor (giving strata for blocks and plots within blocks). The blocks are too small to contain a complete replicate of the treatment combinations and so various interaction are confounded with blocks. (See procedure `AGDESIGN`

.)

Fractional factorial designs from a repertoire (with blocking) – again there are several treatment factors but the design does not contain every treatment combination and so some interactions are aliased; there can also be a blocking factor and some interactions will then be confounded with blocks. (See procedure `AGFRACTION`

.)

Balanced-incomplete-block designs – designs where the experimental units are grouped into blocks such that every pair of treatments occurs in an equal number of blocks. All comparisons between treatments are thus made with equal accuracy, so the design is balanced and, in particular, can be analysed by `ANOVA`

. Further details are given in the description of procedure `AGBIB`

.

Central composite designs – used to study multi-dimensional response surfaces; see procedure `AGCENTRALCOMPOSITE`

.

Box-Behnken designs – used to study multi-dimensional response surfaces; see procedure `AGBOXBEHNKEN`

.

Plackett Burman (main effect) designs – for estimating main effects of factors with two levels, using a minimum number of experimental units (Plackett & Burman 1946). Further details are given in the description of procedure `AGMAINEFFECT`

.

You will be asked to provide a seed to be used to randomize the design and then given the opportunity to print a plan. If the design can be analysed by `ANOVA`

, the procedures will define appropriate block and treatment formulae and then ask if you want to see the skeleton analysis-of-variance table (containing just source of variation, degrees of freedom and efficiency factors). Whether or not you choose to print any of this information, at the end of the whole process all the block and treatment factors necessary to define the design will be available – and they will have the identifiers that you have supplied in response to the various questions asked by the procedures.

Option: `STATEMENT`

. Parameters: none.

### Method

The `QUESTION`

procedure is used to find out what design is required. `AGINDUSTRIAL`

then calls either `AGDESIGN`

(for a factorial design), `AGFRACTION`

(for a fractional factorial design), `AGBIB`

(for a balanced-incomplete-block design), `AGCENTRALCOMPOSITE`

(for a central composite design), `AGBOXBEHNKEN`

(for a Box-Behnken design) or `AGMAINEFFECT`

(for a Plackett Burman main effect design). The designs are generated using `GENERATE`

and the other standard Genstat directives for calculation and manipulation. Some of the information needed to specify the designs is stored in backing-store files, and much of this was adapted from the standard designs of the program DSIGNX (Franklin & Mann 1986).

### References

Franklin, M.F. & Mann, A.D. (1986). *DSIGNX a Program for the Construction of Randomized Experimental Plans*. Scottish Agricultural Statistics Service, Edinburgh (revised edition).

Plackett, R.L. & Burman, J.P. (1946). The design of optimum factorial experiments. *Biometrika*, **33**, 305-325 & 328-332.

### See also

Commands for: Design of experiments.

### Example

CAPTION !t('AGINDUSTRIAL can only be run interactively,',\ 'and is used simply by typing AGINDUSTRIAL.')