Stepwise regression Backward selection
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Stepwise regression and backward selection are statistical methods for selecting variables for regression models. Stepwise regression works by adding or removing variables from the model based on statistical criteria, while backward selection starts with all variables and removes the least significant variable in each step. Some advantages of stepwise regression include its ability to handle many potential predictor variables and provide insight into variable quality. Backward selection is a simpler approach that removes variables one by one until all remaining variables are statistically significant.
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Stepwise regression Backward selection
- 1. ASSIGNMENT OF PREDICTIVE BUSINESS ANALYTICS Presented by : Himanshu TOPIC: Stepwise regression Backward selection
- 2. INDEX What is regression? What is stepwise regression? Advantages of stepwise regression. How Stepwise Regression Works. What is Backward selection? Overview
- 3. WHAT IS REGRESSION? Regression is a method to determine the statistical relationship between a dependent variable and one or more independent variables. The change in dependent variable is associated with the change in the independent variables.
- 5. STEPWISE REGRESSION stepwise regression is a method of fitting regression models in which the choice of predictive variables is carried out by an automatic procedure. In each step, a variable is considered for addition to or subtraction from the set of explanatory variables based on some prespecified criterion. Usually, this takes the form of a forward, backward, or combined sequence of F-tests or t-tests
- 6. ENERGY CONSUMPTION: XYZ COMPANY VARIABLE Units Value Equipment run time Equipment age Equipment size Staff size Temperature outside Time of year
- 7. ADVANTAGES OF STEPWISE REGRESSION Advantages of stepwise regression include: The ability to manage large amounts of potential predictor variables, fine-tuning the model to choose the best predictor variables from the available options. It’s faster than other automatic model-selection methods. Watching the order in which variables are removed or added can provide valuable information about the quality of the predictor variables.
- 8. HOW STEPWISE REGRESSION WORKS FORWARD SELECTION BACKWARD SELECTION
- 9. BACKWARD SELECTION backward selection is a method of fitting regression models in which the choice of predictive variables is carried out by an automatic procedure. It involves starting with all candidate variables, testing the deletion of each variable using a chosen model fit criterion, deleting the variable (if any) whose loss gives the most statistically insignificant deterioration of the model fit, and repeating this process until no further variables can be deleted without a statistically significant loss of fit. Backward selection was introduced in the early 1960s (Marill & Green, 1963). It is one of the main approaches of stepwise regression.
- 10. ENERGY HOUSE VARIABLE Units Value Equipment run time Equipment age Equipment size Staff size Temperature outside Time of year
- 15. OVERVIEW OF BACKWARD SELECTION Backward selection is the simplest of all variable selection procedures and can be easily implemented without special software. In situations where there is a complex hierarchy, backward elimination can be run manually while taking account of what variables are eligible for removal. 1. Start with all candidate variables in the model. 2. Remove the variable with the largest p-value, that is, the variable that is the least statistically significant. 3. The new (p - 1)-variable model is t, and the variable with the largest p-value is removed. 4. Continue until a stopping rule is reached. For instance, we may stop when all remaining variables have a significant p-value de fined by some significance threshold.