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While conducting some research, there are a few questions that the researcher has to deal with. They might wonder if the groups are different? What are the variables, based on which, the groups different? If such questions have occurred in your head, then the discriminant analysis will help you to answer those.
If you are a student of statistics or associated with any other related profession, you might need to gather enough information on discriminant analysis. Read this blog to learn about it in details.
Discriminant analysis can be defined as the statistical method which helps researchers understand how several or one independent variable is connected. Dependent variable can be considered as the variable which is predicted from the independent variables’ values.
The technique of discriminant analysis lets the researcher examine the research data when the dependent variable is categorical by nature. By the term, ‘categorical’, it is meant that the dependent variable has several categories.
The discriminant analysis is similar to the analysis of variance (ANOVA) and the regression analysis.
The main purpose of Discriminant analysis is to form discriminant functions. These functions are independent variables’ combination that will perfectly discriminate between the dependent variable’s categories. The researcher gets to assess if the groups have considerable differences as per the independent variables. Other than this, the classification’s accuracy is also evaluated by the discriminant analysis.
A researcher can usually predict to which category or group a subject belongs. For example, if a recruiting manager wishes to know if the candidates will be high performers or low performers, a researcher can utilise the discriminant analysis. In short, the objectives of discriminant analysis can be summed up as follows:
There are various types of discrimination analysis,
This method is extremely simple and requires very little mathematical implications. Through multiple discriminant analysis, an analyst can consider different stocks and highlight data pints, an important factor for a specific type of analysis. When accurately applied, this technique assists in factoring variables like price, in accordance with the values with which historical volatility and consistency are calculated.
To conduct a discriminant analysis, you need to go through various steps. These steps are as follows:
Next, the sample needs to be divided into two parts. One part, which is known as the analysis sample or estimation, is utilised for assessing the discriminant function. The remaining part, known as the validation sample or holdout, is kept for validating the function.
Situations in which the discriminant analysis can be used are as follows:
The major assumptions of discrimination analysis are given below:
To learn more about discriminant analysis.
So, this is all the basic information you need to know about the discriminant analysis. This information will help you carry out any academic or professional task.
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