# Multiple Regression Analysis

The main of multiple regression analysis is to gain more knowledge about the equation between different autonomous or predictive variables and dependent variables. For instance, if we take the example of the real estate industry, a realtor will prepare a list containing details about the size of each house, the number of bedrooms, the average income in different neighbourhoods and a rough rating of the appeal of the house.

After this information has been prepared for different houses, it would be useful to see if and the manner in which these measures are related to the final price charged for a certain house. You could realize for instance that the number of bedrooms in a house is a better determinant of the price of the house, rather than if the house is attractive to look at.

You might also see the correlation between the price of a house and the locality. Working professionals make use of multiple regression analysis also to establish amounts which would serve as equitable compensations for employees. For example, the degree of responsibility or number of people under supervision of that particular employee would be taken into account to determine an equitable compensation. The analyst will also a do a multiple regression by performing a survey of the market and looking at the compensation and benefits that employees in similar positions get.

Further, multiple regression analysis is greatly helpful for those doing research in the natural and social sciences. Usually, multiple regression techniques provide scope for scholars to pose and also respond to a question such as : ‘what would be the best predictor of a certain phenomenon’. For instance, those involved in the field of education might be interested in knowing what are the best predictors of success, at school. Similarly, psychologists may be interested in determining which kind of personality variable is suited to social adjustment. Sociologists may wish to know which social indictor is the best determinant of whether a new group of immigrants will be able to adapt , or be absorbed in society.

The basic aim of multiple regression is to study the equation between independent and dependent variables. In case there is a relationship between the two, knowing about this can help enhance our precision in estimating values for the variable which is dependent.

Multiple regression analysis can be categorized into three kinds. Each kind of analysis is meant to address a certain type of question. Standard multiple regression is utilized to assess the equation between a group of autonomous variables and a dependent variable. Hierarchical or sequential regression is utilized to assess the equation between a group of autonomous variables and a dependent variable, after limiting the impact of other autonomous variables on the dependent variable.

The purpose of statistical or stepwise multiple regression is to pinpoint the subset of autonomous variables which have the clearest relationship with a dependent variable.

Researchers should acquaint themselves with multiple regression analysis, since it can be a useful tool for comparison.