BACKGROUND OF THE STUDY
Variance measures the variability or difference from a mean or
response. A variance value of 0 indicates that all values within a set
of numbers are identical. Statisticians use variance to see how
individual numbers or values relate to each other. Estimating variance
components in statistics refers to the processes involved in
efficiently calculating the variability within responses or values.
Variance component are estimated when
- A new improved trait is discovered
- Variances or variability changes or alternate overtime due to environmental or genetic changes.
- A new trait is about to be defined or explained
A cardinal objective of many genetic surveys is the estimation of
variance components associated with individual traits. Heritability,
the proportion of variation in a trait that is contributed by average
effects of genes, may be calculated from variance components. The
heritability of a trait gives an indication of the ability of a
population to respond to selection, and thus, the potential of that
population to evolve (Lande & Shannon, 1996). Estimates of variance
components are common in the discipline of animal breeding and
production, where this information on the variance components is used in
the development of selection regimes to improve economically important
traits (Lynch & Walsh, 1998). A requirement for estimating
variance components is knowledge of the relationship structure of the
population. In a natural population, variance components are also of
considerable interest for evolutionary studies (Boag, 1983) and also
for conservation purposes. In natural populations, however, information
on relationships may be unreliable or unavailable. These estimates of
relationships may be combined with phenotypic information gathered from
the same individuals, allowing inferences to be made about variance
components (Ritland, 1996; Mousseau et al., 1998).
Molecular data are used to infer relationships between animals on
a pair-wise basis, because this provides the least complex level at
which relationships may be estimated, while still allowing a population
to be divided into several relationship classes. Estimates of
pair-wise relationships are then combined with a pair-wise measure of
phenotypic information. Several methods of estimating variance
components have been studied, but for the purpose of clarity four
different methods of estimating these variance components will be
evaluated in this research work. They are;
- The ANOVA method
- The Maximum likelihood method
- The Restricted maximum likelihood method
- The Quasi maximum likelihood method.
STATEMENT OF THE GENERAL PROBLEM
They have been general contradictions on the appropriate method to
use in the estimating the variance components of animals. So this
problem has led us into this research to ascertain the relatively best
or appropriate method to be used in estimating these variance
components in farm animals.
OBJECTIVE OF THE STUDY
The major objective of this study is to determine the best method
to be used between the methods enumerated above in estimating variance
components of farm animals.
SIGNIFICANCE OF THE STUDY
A major significance of this study is to unravel the relatively
best methods among the methods highlighted above with a view to
advising animal breeders, producers and animal researchers on the best
method to be used in estimating variance components as which relatively
better method has generated a lot of controversies over time .
SCOPE OF THE STUDY
The scope of the study is centered on the methods of estimating
variance components in farm animals, to know which of the methods is
relatively better in estimation.
DEFINITION OF TERMS
- Variance: the amount by which something changes or is different from something else.
- Estimation: a judgment or opinion about the value or quality of somebody or something.
- Traits: a particular quality in someone’s personality.
- Genetic: the units in the cells of livings that controls its physical characteristics.
- Components: one of several parts of which something is made.
HYPOTHESIS TO BE TESTED
H0: there is no significant difference between the methods of estimating variance components.
H1: there is a significant difference between the methods of estimating variance components.
Level of significance: 0.05
Decision rule: reject H0 if p-value is less than the level of significance. Accept H0 if otherwise.
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