Finite Population and Other Models

Hardeo Sahai and Mohammed I. Ageel
Additional contact information
Hardeo Sahai: University of Puerto Rico
Mohammed I. Ageel: King Saud University, Abha Campus

Chapter 9 in The Analysis of Variance, 2000, pp 461-482 from Springer

Abstract: Abstract As discussed earlier, so far in this volume we have been primarily concerned with random effects models or Model II based on the infinite population theory, that is, when the treatments included in the experiment are assumed to be a random sample from a population of treatments having infinite size or when the experimenter selects the levels at random from a large number of possible levels of a factor usually considered as infinite. However, as described in Section 1.4, there are situations when the treatments selected may be a sample from a finite population and then the assumptions of an infinite population may be inappropriate. For example, in a large laboratory, there could be a total of 10 analysts and the data obtained on just three of them could be used to make inferences concerning a new method for the determination of arginine content as used by the entire group of 10 analysts.

Keywords: Variance Component; Percent Confidence Interval; Finite Population; Conservative Test; Infinite Population (search for similar items in EconPapers)
Date: 2000
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4612-1344-4_9

Ordering information: This item can be ordered from
http://www.springer.com/9781461213444

DOI: 10.1007/978-1-4612-1344-4_9

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().