Identify the independent and dependent variables in their examples. A simple linear regression is defined as when a model is attempted between two variables by fitting a linear equation to observed date. The key differences between simple linear regression and a multiple regression is the variables. Multiple linear regression has only one y and two or more x variables. Whereas simple linear regression has only one of each variable. A dependent variable represents a quantity whose value depends on how the independent variable is manipulated. An independent variable is defined as a variable that represents a quantity that is being manipulated in an experiment. To answer the example the independent variables would be the size, location, condition, number of bedrooms. First example: I want to see if different Ethernet cables could fix a clients slow internet speed. The independent variable would be switching Ethernet cables. The dependent variable would be the speed of the internet. Second example: How many files do I need to delete on a clients computer to free up space on their hard drive. The independent variable would be how many files I delete and the dependent variable would be the amount of space on the hard drive. References: Dependent and Independent Variables Review. Khan Academy, Khan Academy, https://www.khanacademy.org/math/pre-algebra/pre-algebra-equations-expressions/pre-algebra-dependent-independent/a/dependent-and-independent-variables-review. Gaurav Bansal, http://blog.uwgb.edu/bansalg/statistics-data-analytics/linear-regression/what-is-difference-between-simple-linear-and-multiple-linear-regressions/.
