Regression is a further step of correlation. We always put dependent variable on Y-axis and independent variable on X-axis. If the user needs to predict the final grade based on the strengths of the mid-terms grade in the next session, he can design a linear regression model on the previous data. The assumption being higher the grade for mid-term would result in a higher grade for final-term.Ī scatter plot is created using the final grade and mid-term grade variables. A random sample of 15 students in his class was selected with a data that includes their mid-term grade and final grade. He believes that higher the grade for mid-term, higher the final grade. Let us take a case where a professor is trying to show his students the importance of mid-term test. If there is more than one variable it is called Multiple Linear Regression. When there is just one independent variable it is called Simple Linear Regression.
There can also be more than one independent variable. The wheat production variable is the dependent variable and the amount of rainfall is the independent variable.
The independent variable is used in a regression model to estimate the value of the dependent variable.įor example, we take two kinds of variables such as amount of rainfall and wheat production. Independent Variable – An Independent Variable is the variable related to the dependent variable in a regression equation. This variable is assumed to be functionally related to the independent variable. It is important to know the following types of variables as well:ĭependent Variable – A Dependent Variable is the variable to be predicted or explained in a regression model. Linear Regression is based on Ordinary Least Square Regression.