Introduction
Stepwise regression is defined as a step-by-step construction of a regression model that includes an automatic selection of variables that are independent. The basic concept is to collect information that is relevant to arrive at an informed decision which is a very common tradition followed in the world of investment.
The stepwise regression is a detailed step-by-step repeated building of a regression system which is involving an automatic choice of variables that are independent. The availability of statistical software modules has made stepwise regression a possibility even when there are several hundreds of independent variables.
Understanding Stepwise Regression
Stepwise regression is achievable in two ways; 1. by trying out various independent variables, one at a time, by incorporating them in the regression model 2. Incorporating all the potential independent variable in the regression model and filtering out those that are not significant.
The analysis of regression that uses both multivariate and linear are extensively used in the world of investment today. The main idea is mostly to find out trends that were in place before and are likely to occur again in the coming days. A basic linear regression, for instance, may consider the price-to-earnings ratios and share returns over a considerable number of years to find out if the share with a low P/E ratio will offer better returns (variables are dependent).
The main issue with the approach of this kind is that the market conditions tend to change considerably and the relations which were made in the past may not mandatorily need to hold true in the future or present.
- The most popular tests are t-tests and F-tests.
- These tests consume very less time and hence saving a considerable amount of time.
- The main goal is to find those independent variables that significantly impact the dependent variables.
