Linear regression is a model relationship between two quantitative variables, a dependent and one or more independent variables. The model uses a line to draw through those variables' data points. That is know as a regression line. This concept is evaluating the relationship between the variables that it is measuring, and is used to look for predictions or patterns in those variables. A dependent variable is called the responsive variable or the output. An independent variable is a predictor or the input. A great example in my life where I could apply linear regression is during my work place's shifting period from our busy to non-busy season. Some Monday's are more busy than our Friday's that week, and vice versa. As the seasons become colder though, the patter will start to show fewer busy days, and less foot traffic. This allows us to use previous years to help predict when the slow season will start for us and when to start ordering less on our truck that we receive twice a week. At one point last winter, we became so slow that we could only order our truck once a week, as to meet the order amount requirement