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Table of Content

REGRESSION ANALYSIS


The process of determining the relationships among the dependent and independent variable is known as regression analysis. It is a statistical tool which measures the process for example the effect of increase of the price on demand.

TYPES OF REGRESSION ANALYSIS

Simple regression

Multiple regression

Simple regression–Simple regression refers to that regression analysis where analysis is done with only one single variable. During this regression one suggests the relationships between the variables of interest for example education and money. If a person is highly educated then in casual terms the person will earn more and more money but if the person is less educated, then he or she will earn at equal terms.

Multiple regressions – Multiple regression refers to that regression analysis where analysis is done with multiple influences on a dependent variable. It invites various factors to enter in the process so that each factor is judged separately on very common basis. For example in the above given example experience is an independent variable in the field of education. Some people have long term experience, so they gain more money in comparison to the people who have less experience and try hard to achieve their target. Sometimes experience gives birth to knowledge. A person with high experience can have less knowledge in comparison to the person having less experience and more knowledge. These are multiple factors in an independent variable of experience.


REGRESSION MODELS

Regression models are made in those situations where one variable is extracted with the information given for one or more than one variable. It declares the predictions for the past, present and future with the past and present events. It is economical in terms of time and money.. It should be noted that these models don’t make the decisions of the people but tend to provide the opportunities and options for the choice. In order to use them wisely, one should understand that how they home work. Here is an example for the use of regression models.

Taking admission in a college – A student of class XII decides to take admission in Oxford University in London with 90% marks. As he reads the criteria of the university on the internet he gets an idea that he is scoring at least 3% less for the desired course. He then takes a look at the other courses related to that particular field. He visualises that they are quite far away in terms of his interest. Then he switches to the other Universities in London which are providing the same course but he is not satisfied. When he visualises the Universities in America he gets an idea that he can give an entrance test with the attained marks. He also visualises the Indian universities but in the last he decides that he will start a diploma course with giving an improvement exam and then take the admission in Oxford University in London.

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