ANOVA analysis is derived from ANalysis of Variance and is used bets where the dependent variable is categorical. In the field of science, this type of analysis is frequent where samples from two or more subjects are analyzed for the inherent properties that they have (Explorable, 2012).
In this case, the size of the maple leaves being dependent on the location of the tree will be used. Maple trees under the shades of taller trees are smaller than the maple leaves from the trees out in the open and even smaller are the maple tree leaves from the median of parking lots. In order to put such a hypothesis to test, 5 groups of 10 maple leaves from the three locations will be collected.
Group I will be the leaves from the shade, group II will be the leaves from the open fields and group III will be those from the parking lots. In such a scientific analysis, the ANOVA analysis comes in appropriately. Apparently it is likely that the groups could be broadly similar, for instance the range between the largest leaves and the smallest in group I could a large fraction of the leaves in the other two groups. In finer morphological details the leaves could probably be different for instance varying lows, highs, edges and hence each group is likely to have a different average size. Here, it is worth noting that even though there could be no real effect on the location on the sizes of the leaves from varied locations (hence the null hypothesis) there is a possibility that there could still be varying leaf size. From the ANOVA analysis, it is safe to indicate that the hypothesis hitherto is wrong and the null hypothesis is correct.
Regression analysis
In this kind of analysis, the patterns quantified are spatial. It is the best and most commonly used kind of analysis in social and behavioral sciences. In this analysis, the dependent variable is continuous (Statrek, 2012).
One of the instances where the regression analysis can be used is in investigating whether there are areas in the U.S.A. where people are persistently dying young. Once the 'where' question has been answered, it is worth the asking 'why' questions since from there possible variables will be put into the equation. For instance in our example here, questions like 'why are there places in the U.S. where people die young? What might be casing these deaths? And so on.
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