Presentation on theme: "Linear VS Non-Linear. One of the most common statistical modeling tools used, regression is a technique that treats one variable as a function of another."— Presentation transcript:
Linear VS Non-Linear
One of the most common statistical modeling tools used, regression is a technique that treats one variable as a function of another. The result of a regression analysis is an equation that can be used to predict a response from the value of a given predictor. Regression is often used in experimental tests where … one tests whether there is a significant increase or decrease in the response variable ….
One tool used in the real world to help make business decisions and determine the results of scientific experiments is regression analysis. You use regression analysis to see if one thing (like the periods of time a store is open) strongly affects another thing (like how much money the store makes). There are many types of regression analysis. Two of those are linear and nonlinear.
Linear Regression Model The relationship between the two variables is directly proportional. Directly Proportional: If one value increases, the other increases as well. The function that passes through the middle of the scatterplot is called the line of best fit. scatterplot line of best fit
There are many types of nonlinear regressions due to the fact that they are anything that is not linear. Quadratic Regression Cubic Regression Quartic Regression Power Regression Exponential Regression Logarithmic Regression Logistic Regression