# Category: Regression

• ## How to Eliminate Respondent Data to Get a Better Regression Model

Eliminate Respondent. It is still discussing regression; this time I will try to give a suggestion on the steps or efforts that must be taken if all the normal regression steps have been taken but the results are not as expected or hypothesized. You can use the technique of eliminating respondent data that can disturb…

• ## Did you know that Dummy variable like level in experiment?

Dummy variable in regression are slightly different from other variables, both in data processing and when reading regression results. Linear regression, or multiple regression, is a function that explains the relationship between independent variables and dependent variables. One dependent variable (Y) is usually influenced by several independent variables (X). For example, the production variable is…

• ## This is All About Logistic Regression

Logistic regression is a type of regression that relates one or more independent variables (independent variables) to a dependent variable that is a category, usually 0 and 1. This type of categorical independent variable is what distinguishes logistic regression from multiple regression or other linear regression. Category values are usually written as 0 and 1.…

• ## How to Eliminate Variables in Regression

Eliminate Variables in Regression. Regression is the most popular statistical technique that explains the relationship between an independent variable and its dependent variable, either simultaneously or individually. Those of you who are used to using it must be familiar with R-square or R-square adj, where this one indicator explains the goodness of the model issued…

• ## One of the Sample Elasticities in Regression

Elasticities in Regression. Elasticity is the percentage change in the dependent variable that results from a percentage change in the independent variable. Elasticity is excellent for reflecting causal relationships and calculating the amount of impact due to changes in certain variables. There are many kinds of elasticities, including demand elasticity, supply elasticity, production elasticity, and…

• ## Find here a secret tip: Ironing of Regression.

Ironing of Regression. The need for data fitting techniques in regression is because regression is synonymous with linear. This is because the standard pattern of regression is a straight line. Where Y = a +bx +e … with a as a constant, b is a coefficient that reflects the relationship between variables x and Y,…