what is simple linear regression equation

#What is simple linear regression equation professional

You survey a group of 500 college graduates whose ages range from 23 to 30 and ask them to mark down their income. You're a research assistant and your project is to discover the relationship between the age of college graduates and the income they earn. U = regression residual (or the vertical distance between a data point and the regression line) The equation to calculate simple regression is as follows:ī = slope (or the graph line's steepness)Ī = intercept (or where the line intersects an axis) The value of the unknown variable at a specific value of the known variable. You can use this model when you want to:ĭiscover the strength of the relationship between two variables This simple model of regression uses a single independent variable to attempt to predict the outcome of a dependent variable. Related: 16 Must-Have Data Analyst Skills (Plus Job Responsibilities) Simple regression The main purpose of a regression model is to find the best-fit straight or linear line that decreases the differences between the predicted value, X, and the known output value, Y. Regression is one of the most long-established statistical procedures which you can use to predict unknown variables reliably. The known variable, Y, is known as an independent or explanatory variable, while the variable you wish to predict, X, is known as the dependent or response variable.

It's a simple and commonly used process when trying to determine the value of a variable based on the value of another. Linear regression, or regression, is a statistical process of finding and explaining a linear relationship between variables. Related: What Does a Data Analyst Do? What is linear regression? In this article, we explain what regression is, introduce two main types of regression with their equations and examples and list the applications and assumptions of regression.

#What is simple linear regression equation professional

Learning more about regression and its uses can be helpful for your career and professional development. In statistical terms, regression is a way to examine the relationship between two variables. If you currently work or have an interest in working with statistics and data, it's important to be aware of statistical methods, such as linear regression.