Research Methodology - Analysis Of Variance
ANALYSIS OF VARIANCE (ANOVA)
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For managerial decision making, sometimes one has to carry out tests of significance.
ANALYSIS
OF VARIANCE (ANOVA)
For managerial decision making, sometimes one has to carry out tests of significance. The analysis of variance is an effective tool for this purpose. The objective of the analysis of variance is to test the homogeneity of the means of different samples.
According to R.A. Fisher, “analysis of variance is the separation of variance ascribable to one group of causes from the variance ascribable to other groups”.
The technique of ANOVA is mainly used for the analysis and interpretation of data obtained from experiments. This technique is based on three important assumptions, namely
1. The parent population is normal.
2. The error component is distributed normally with zero mean and constant variance.
3. The various effects are additive in nature.
The technique of ANOVA essentially consists of partitioning the total variation in an experiment into components of different sources of variation. These sources of variations are due to controlled factors and uncontrolled factors. Since the variation in the sample data is characterized by means of many components of variation, it can be symbolically represented in the mathematical form called a linear model for the sample data.
Introduction
For managerial decision making, sometimes one has to carry out tests of significance. The analysis of variance is an effective tool for this purpose. The objective of the analysis of variance is to test the homogeneity of the means of different samples.
Definition
According to R.A. Fisher, “analysis of variance is the separation of variance ascribable to one group of causes from the variance ascribable to other groups”.
Assumptions of ANOVA
The technique of ANOVA is mainly used for the analysis and interpretation of data obtained from experiments. This technique is based on three important assumptions, namely
1. The parent population is normal.
2. The error component is distributed normally with zero mean and constant variance.
3. The various effects are additive in nature.
The technique of ANOVA essentially consists of partitioning the total variation in an experiment into components of different sources of variation. These sources of variations are due to controlled factors and uncontrolled factors. Since the variation in the sample data is characterized by means of many components of variation, it can be symbolically represented in the mathematical form called a linear model for the sample data.
Tags : Research Methodology - Analysis Of Variance
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