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Regression Analysis is a significant modeling technique used by statistics experts to establish relationships between variables. These types of models are incredibly significant in a way that they can be used by experts to predict future results by comparing the data to already existing data and results.

There are several Regression Analysis methods in use today and linear regression is the most popular. Other methods used in Regression Analysis include:
  • Logistic Regression
  • Ridge Regression
  • Polynomial Regression
  • Lasso Regression
  • Stepwise Regression
  • Elastic Net Regression
Regression Analysis As It Is Today Is Mostly Done By Computer Software As Well And Some Of The Commonly Used Include:
  • SPSS
  • JMP
  • NCSS
  • PSPP


For students studying statistics and data science, maneuvering your way around most of the Regression Analysis assignments and projects can be a tough challenge for various reasons. 

From the above-mentioned methods and techniques, it is clear that Regression Analysis is not a walk in the park for many individuals, especially learners. Most of these methods involve complex math computation and formulae that can get confusing. Also, most of the software used to visualize these models require technical know-how if one is to use them effectively to their benefit.

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