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It is a statistical tool that helps you to analyze the relationships between different variables. The key thing about regression analysis is to find the relationship between a dependent variable and multiple independent variables. Basically, the independent variables are called predictors. By using this regression analysis, the investigators will try to determine the relationship between one variable to another. For instance, there would be a drop in demand with the hike in prices. Apart from the relationship, the importance of the relationship that is estimated would be assessed thoroughly. There is a myriad of regression techniques used in econometrics, which are again used to evaluate trends. More importantly, this analysis is done to analyze customer behaviour and find the parameters that give the probability.

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This is the widely used application technique. This is used by businesses and academicians. In this type of technique, the dependent variable would be constant, whereas the independent variable would be constant as well as discrete. This will show up the link between a dependent variable and multiple predictor variables to give the best liner whose nature is actually linear. Basically, in simple linear regression, only one dependent variable and one predictor variable are used to establish a relationship, whereas, in multiple linear regression, a single dependent variable is used to establish a relationship between one or more predictor variables. Our statisticians will prepare a detailed report about regression analysis. If you want help in completing this assignment, contact our **Regression homework help** experts.

This type of analysis is called by another name, i.e. Logit model which will gauge the relationship between the dependent variables as well as multiple independent variables. This model is used to estimate the probabilities with the help of a logistic function, which is called a cumulative logistic distribution. This type of technique can be considered a special case of a linear regression model.

This is called a non-linear regression. In this type of model, the relationship between dependent and independent (predictor) variables is established with the help of an nth-degree polynomial. This model will use the least square method.

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**Task:** Theoretical regression equation in terms of the variables

**Solution:**

**1. Theoretical Model I: **

The testing hypothesis is that the level of support for economic aid to poor countries is determined by the level of education and employment status of respondents. It is going to test whether the level of support is weak or low among the unemployed or someone employed in a low-level position, while the support is higher in those with a high level of education, and professional and higher-paying jobs.

The main way of income for people in most countries is through an employer. Only a few peoples are the owner of companies or someone like that level. So, if someone likes to help someone else, he or she should be economically strong enough to help. This is the general low in the world that we have seen. Therefore it is very important to have a job with someone to help others. Based on this, it can be assumed and made a hypothesis that the level of support for economic aid to poor countries is determined by the employment status of respondents. Also, people’s education level is very important to their level of thinning. Based on this, it can be assumed and made a hypothesis that the level of support for economic aid to poor countries is determined by the educational status of respondents.

**2. Theoretical Model II: **

Multiple linear regression was done using two predictor variables to test the hypothesis of the relationship described in the theoretical model I part. Prefer Canada-Econ.aid poor countries was used as the response variable and Employment status and the highest level of education were used as predictor variables. A theoretical model was as follows,

Y= β_0+ β_1 X_1+ β_2 X_2

Where;

Y = Prefer Canada-Econ.aid poor countries (q4b) (Response variable)

β_0 = Constant term

β_1= Regression coefficient of Employment Status

X_1 = Employment status (demo_a)

β_2= Regression coefficient of the Highest level of education

X_2 = Highest level of education (demo_g)

We are expected to have positive regression coefficients for both predictor variables. It is because we are expected to have a positive relationship between the response variable and each of predictor variable. Further explaining, the level of support for economic aid to poor countries should be increased when employment and educational level increases. If the employment and educational level of respondents increase in a country means that country’s people can help others.

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