Regression Analysis And Regression Analysis

This paper will describe three combinations of independent variables that could be used testing regression analysis and the difference between correlation and regression. It will also explain the outcomes of regression analysis, and how I could use these in my future career.

Regression Analysis
Introduction

When looking at Regression Analysis, there are different areas that are important to learn to be able to understand Regression Analysis. A few topics that one must understand is Independent Variables, Dependent Variables, Correlation, and Regression.

Independent and Dependent Variables

Independent variables are a variable you have control over, something that you can manipulate and control. The dependent variable

According to Editorial Board, Correlation is an observed relationship between two variables, and regression is a statistical procedure related to correlation that allows you to predict values of one variable when you know the values of a correlated variable. (Editorial Board, 2017). The difference between correlation and regression, is correlation measures the strength of association and the regression line is a prediction equation that estimates the values given for x and y. Regression analysis includes any techniques for modeling and analyzing trends between a dependent variable and an independent variable. Regression and correlation analyze and make predictions. According to KARUNA, the effect of correlation is to reduce the range of uncertainty. The prediction based on correlation analysis is likely to be more variable and near to reality. (KARUNA,

For example, by being able to understand regression analysis, I have the knowledge of where to promote my business. By taking flyers to the most traveled areas in my town, will bring in more business and increase my income. So, the independent variable would be taking flyers and the dependent variable is promote my business. The one way to promote my business is by passing out flyers. So there needs to be a correlation between my variables to gain profits in my business. Correlation is a statistical measure of the linear relationship between two variables. Regression examines linear prediction of Y by X and must meet all the requirements of correlation. An example of how I would use correlation and regression in my future career would be, by achieving getting my degree, I will make more money and bring in more responsibilities. Correlation and regression analysis can not be interpreted as establishing a cause and effect relationship. They can only indicate how variables are associated with each other.

Conclusion

In conclusion, by describing the three independent and dependent variables shows the relationship between each variable. Even though all three were positive correlations, with other variables could make it a negative correlation. The correlation and regression analysis are related in the sense that both deal with relationships among