The relationship between two variables can be examined with the help of Regression Analysis. It is one of the most useful tool use in Statistics which helps to take the right decisions. Curious to know? What is the meaning of Regression Analysis and how you can use it in making the right decisions? Then read this blog and understand how it works in different models. Moreover, you can also hire a Regression Analysis assignment help without any hassle.
Regression Analysis: With the help of regression analysis, you can predict which factors are affecting the most and which not. Let’s explore these two terms first:
Dependent Variable: This is the key factor that you are trying to predict or understand. It is generally named as Predictor.
Independent Variable: This variable called a target shows the impact on the dependent variable.
There are many types of regression analysis model which work differently from each other. The common regression models are Simple regression, Calibration model, Polynomial regression, regression model selection, multiple regression and so forth.
Let us Explore How Regression Analysis is Done in
1. Simple Regression Model
In this model, there is a single predictor variable named X and single response variable Y. It named Simple defines that this model deals with a single predictor variable. However, when you will study multiple regression, there will be more than one variable is used. For instance, if you want to see the historical levels from the past four years, then you can calculate with this model.
2. Box-Cox Transformation
When the response variable doesn’t go with the normal distribution, at that time is needed to use box-cox transformation. Here, the transformation is done on Y. Optimal power is determined with the help of STATgraphics. It is surely an appropriate model is used when no other model is working. To get more detail on the topic, you can hire Regression Analysis assignment help.
3. Polynomial Regression
If you are going to solve the nonlinear equation, then you can use polynomial regression. For interpolative purposes also it is the best model to choose. Several kinds of function can be determined with this model.
4. Calibration Models
In this model, the number of given samples are estimated and an equation is made to fit the value of known reference or known values. The equation is then used to determine the value of unknown samples. You can determine the value of X from Y after going through the sample.
5. Regression Model Selection
When the number of the predictor is not abundant, then you can use regression models which have a combination of predictor 1, predictor 2, predictor 3 and so forth. This model is rightly named as fit-to-all. For STATGRAPHICS, this model chose schemes which help to get accurate values of Mallows’ Cp statistics or adjusted R-Squared.
These are some of the models which every student must have knowledge of. However these models are not enough, there are others too. Get complete knowledge of this topic, by hiring an academic guide. Their experts are well versed and experience in this topic.
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