Quantification Methods make category data availavle in the methods for quantity data. It uses the dummy variable method to transform category data into quantity data.

- Quantification Methods
**1**- To use Multi-Regression Analysis - Quantification Methods
**2**- To use Discriminant Analysis - Quantification Methods
**3**- To use Principal Component Analysis - Quantification Methods
**4**- To use Multi Dimensional Scaling

Quantification methods are used to use category data in the methods of quantity data. But if we do not understand the weakness of quantification methods, the analysis will not go well.

As an example is the case of the method 1.

This is the easy case, X is a category of the name, "A" and "B".
Dummy variable is made as "X = 1 (for A), X = 0 (for B)".

By the method 1, we may get an equation,

Y = X + 1.

It means that

Y = 2 ...(for A)

Y = 1 ...(for B)

The category data is used to in the y-intercept.

In more complicated data including quantity X and category X, multi regression analysis uses category X to adjust y-intercept. Slope factors are not adjusted by category X. So if we need to adjust slope factors by category factors, quantification methods 1 is not useful.

If we need to analyze the model including some slope factors, we need to make the new variable. The product by the quality X and the dummy variable is the new variable

Multi regression analysis including the new variable can deal with the some slopes.

It is the hypothesis testing of the slopes to select to include the new variable in the model or not,

NEXT Factor Analysis

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