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Regression analysis is a statistical technique that uses a model to estimate the relationship between variables. There are two types of regression variables; independent variables and a dependent variable. An independent variable can is the variable that the researcher can easily manipulate to record or observe the outcome in a dependent/response variable. Examples of independent variables include age, height, and weight. Examples of dependent variables include the level of satisfaction, fashion interest level, and GPA amongst others. Once computed, the relationship between the variables involved is captured using a regression model. The analysis is used in many fields which include psychology, nursing, research papers, business, finance, accounting, medicine, engineering among other areas. Below is a baseline model where y is the dependent variable, xi are the independent variables, βi are the coefficients, n is the number of independent variables, and £ is the error.

y_{i} = β_{0}+β_{1}x_{1}+…+β_{n}x_{n-1}+£
where i=1,2,…,n

The type to be conducted mainly depends on the nature of the variables. The meaning of common types of regression analysis are briefly discussed below;

This is the most common, it is conducted when the variables are linear. There are two types of linear regression analysis: simple linear and multiple linear regression. A simple linear regression involves only one independent variable and one dependent variable. Multiple linear regression model has one dependent variable and two or more independent variables.

This is type is conducted to find the probability of an event. It is used when the dependent variable is binary (0/1, yes/no, True/false).

This is the type that is conducted if the power of independent variables is more than one. An example of a polynomial equation is as shown below

y
= β_{0}+β_{1}x²

This is a technique that is used when there is multicollinearity in the data involved.

This is a special type that is conducted with the intention of reducing variability and improving the accuracy of the model

It is used mainly to determine the degree of impact of particular independent variables on given dependent variables. In other words, it enables you to decide which factors matter most, the variables that you can ignore, and the impact of these factors on each other. However, it is important to note that the importance of conducting the analysis is highly dependent on the type of field. On the overall, it provides detailed insight which can be applied to improve products and services. We provide college statistics homework help which is timely and correct.

To conduct a regression analysis, you will need a definition of your research question from which you will choose your dependent and independent variables. Using these, you will then develop a hypothesis for the study, and you are good to go. However, it is important to note that you have to ensure that you meet all the assumptions. Some of the important assumptions are;

*Linearity
*-Ensure
that the variables are linear in nature

*Absence of multicollinearity –*Statistical tests such as VIF are conducted to check for multicollinearity

*Normality *– The normality assumption ensures that the data in question follows an approximate normal distribution. On most occasions, statistical tests such as the Shapiro-Wilk and Kolmogorov -Smirnov test are used. In addition, graphical representations such as histograms and scatter plot are used to check for the normality assumption

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