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WEEK 4 DISCUSSION

WEEK 4 DISCUSSION

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Question One: Bivariate Regression

BRA is a sort of measurable analysis that can be utilized during the analysis and announcing phase of quantitative statistical surveying. It is regularly viewed as the easiest type of regression analysis, and is otherwise called Ordinary Least-Squares regression or direct regression (Chatterjee & Hadi, 2015).

Basically, BRA includes analyzing two variables to build up the quality of the connection between them. The two variables are habitually signified as X and Y, with one being an independent variable, while the other is a dependent variable.

Employments of BRA incorporate testing basic speculations, especially of affiliation and causality. Thusly it very well may be perceived how much simpler it moves toward becoming to know and anticipate an estimation of the dependent variable having known the independent variable. It tends to be extremely useful to analysts with constrained example data and who hence need to make expectations so as to make key decisions.

Question Two: Types of Regression Analysis

Regression analysis portrays the connection between a lot of independent variables and a dependent variable. There are various kinds of regression models that you can utilize. This decision regularly depends on the kind of information you have for the dependent variable and the sort of model that gives the best fit.

Linear and Logistic Regression Analysis are generally the principal calculations individuals learn in information science (Gunst, 2018). Because of their notoriety, a great deal of analysts even wind up imagining that they are the main type of Regression Analysis. The ones who are somewhat increasingly included feel that they are the most significant among all types of regression analysis.

Truly there are incalculable types of Regression Analysis, which can be performed. Each structure has its own significance and a particular condition where they are most appropriate to apply.

Some of the most commonly used types of Regression Analysis are:

Linear Regression

Logistic Regression.

Polynomial Regression.

Stepwise Regression.

Ridge Regression.

Lasso Regression.

ElasticNet Regression.

References

Chatterjee, S., & Hadi, A. S. (2015). Regression analysis by example. John Wiley & Sons.

Gunst, R. F. (2018). Regression analysis and its application: a data-oriented approach. Routledge.

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