Regression Analysis

Importance of Regression Analysis

As discussed, regression analysis helps us to find the relationship between variables. For example, regression analysis is irreplaceable in business analysis. It allows businesses to understand their data points. It helps them predict sales and understand inventory and supply and demand chains. It can help understand the reason for call drops. It helps in error corrections as it correctly analyses the results obtained from decisions. It can help identify new opportunities in the market.

This was about business and financial analysis. Regression analysis is helpful in healthcare too. It can be used by research professionals to monitor the dosage of drugs and see the reaction to patients' blood pressure. Therefore regression analysis has multiple crucial use cases.

Types of Regression Analysis

• Linear Regression
  It is one of the most widely used analytical techniques. It assumes that we have a continuous dependent variable, and the independent variable can be either discrete or continuous. The following equation represents a linear regression.
  Y = mx + c + e
  Y: dependent variable
  m: slope
  c: intercept
  e: error term
  If we have one independent variable, it is known as simple linear regression. If we have more than one independent variable, it is known as multiple linear regression. Linear regressions fit a linear line to minimize the distance between the line and the data points.
   
• Logistic Regression
  It is used to find the probabilities of success and failure of an event. It is used in cases where we have binary possibilities of dependent variables like true/false or 0/1. The range of the dependent variable is from 0 to 1.
   
• Polynomial Regression
  It is used in cases where the power of independent variables is more than 1. For example Y = m(x²) + c is a polynomial regression. Here, we get a regression curve and not a line.
   
• Stepwise Regression
  It is beneficial in cases where we have multiple independent variables. It maximizes the prediction capability with the minimum number of features (independent variables). It adds or subtracts independent variables as and when needed.
   
• Ridge Regression
  It is helpful in cases where the independent variables are highly correlated. Here, we introduce a shrinkage parameter, lambda, to reduce the variance.
   
• Lasso Regression
  Lasso regression is similar to Ride regression. It differs because ridge regression uses squared penalty terms, whereas lasso regression uses absolute error terms.
   
• ElasticNet Regression
  This regression combines lasso and ridge regression.
   

So, as we see multiple types of regression techniques available, we should select methods based on our needs. For example, lasso and ridge regression work well with high-dimensional, correlated data. We should experiment with different techniques taking care of their assumptions.

Frequently Asked Questions

1. What is regression analysis?
Regression analysis helps us to understand the relationship between variables.

2. What do we do in regression analysis?
In regression analysis, we try to fit a line or a curve to minimize the distance between data points and the line or the curve.

3. Why do we use regression?
Regression analysis helps understand the relationship between variables. For example, we can predict the sales of our business.

4. What are some real-life examples of regression analysis?
Researchers can use regression analysis to monitor the dosage of drugs and patients' blood pressure.

5. What is linear regression?
Linear regression finds the relationship between independent and dependent variables using a straight line.

Conclusion

This article discussed the introduction, importance, and types of regression analysis.

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