For example, by being able to understand regression analysis, I have the knowledge of where to promote my business. By taking flyers to the busiest areas of my city, I will get more business and increase my income. So, the independent variable would be taking flyers and the dependent variable would be promoting my business. The only way to promote my business is to hand out flyers. So there must be a correlation between my variables to make profits in my business. Correlation is a statistical measure of the linear relationship between two variables. Regression examines the linear prediction of Y versus X and must meet all correlation requirements. An example of how I would use correlation and regression in my future career would be, by earning my degree, I will earn more money and have more responsibilities. Correlation and regression analysis cannot be interpreted as establishing a cause and effect relationship. They can only indicate how variables are associated with each other. ConclusionIn conclusion, describing the three independent and dependent variables shows the relationship between each variable. Even if all three were positive correlations, the presence of other variables could make it negative. Correlation analysis and regression analysis are related in the sense that they both address the relationships between