Regression Analysis is a popular technique used for predictive modeling and data mining. It is a statistical measurement used in investing, finance and many other disciplines. The regression line is used to find how a dependent variable is numerically related to a series of independent variables.
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Types of Regression
Regression Analysis is segregated based on the different assumptions attached to it, the types of dependent and independent variable and the distribution that it follows.
1) Linear Regression. It is used to find the linear relationship between two continuous variables. It is commonly used for predictive analysis. When one predictor variable is linearly related with one response variable, then it is simple linear regression. When there are more than one predictor variables, then it is known as multiple linear regression. Simple linear model is not suitable for large amounts of data as it is sensitive to outliers, cross-correlation and multicollinearity.
2) Logistic Regression. Logistic is used when the response variable is categorical or discrete in nature. Categories may be two or more. In case of three or more categories, it is known as multinomial logistic regression. Logistic regression is used to predict the probability for nominal variable.
3) Polynomial Regression. Polynomial Regression is a form of linear regression in which the relationship between the independent variable and dependent variable is modeled as an nth degree polynomial.
4) Ridge Regression. This method of regression analysis is used as a remedial measure to remove collinearity amongst regression predictor variables in a model. Here, the value of coefficient shrinks, but does not reach zero. Ridge Regression adds a small squared bias factor to the variables. This squared bias factor introduces a small amount of bias into the model and reduces the variance. Hence, large parameters are penalized.
5) Lasso Regression. LASSO (Least Absolute Shrinkage Selector Operator) penalizes the absolute size of the regression coefficients. LASSO takes the relevant features and makes the irrelevant values zero to avoid overfitting. It makes the learning faster.
6) ElasticNet Regression. This is a combination of both Ridge and LASSO Regression. It encourages group effect for highly correlated variables, rather than zeroing some of them out like Lasso.
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Applications of Regression Analysis
Though there are a lot many applications, but the following mentions some of the most commonly used applications of the forecasting method in regression analysis.
a) Predictive Analytics. This involves forecasting future opportunities and risks. Insurance companies use predictive analysis to estimate the credit standing of policyholders. It also helps in finding a possible number of claims in a given time period.
b) Operation Efficiency. Companies can optimize their business process. For example, a call center can use regression analysis to track the relationships between waiting times of callers and the number of complaints they register. Say a factory manager can check the shelf life of baked breads, by checking the impact of oven temperature on loaves of bread baked in those ovens.
c) Supporting Decisions. Regression helps businesses adopt a logical angle in their management strategies. It reduces the humongous amount of raw data by transforming into meaningful insights. Thus leading the way to smarter and much more accurate decisions.
d) Correction of Errors. By using regression a manager could determine that an increase in work hours does not always lead to an increase in profits. This could help the manager avoid making a costly mistake. Suppose, a retail store manager feels that extending shopping hours will increase sales. But regression analysis may show that the modest rise in sales might not be enough to break-even the increased cost for labor and operating expenses.
e) Newer Insights. Businesses gather large volumes of unorganized data that has the potential to yield valuable insights. However, this data is useless without proper analysis. Regression analysis techniques can find a relationship between different variables by uncovering patterns that were previously unnoticed.
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For effective operations of any business, understanding the relationships between business happenings and other variables is very important. Thus, regression analysis of data is a vital factor to making sound decisions.
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