Decision Science in Action

Multicollinearity is defined as the linear relationship between two or more independent variables while performing regression analysis between a dependent variables and set of independent variables. Multicollinearity presents a severe problem during regression modelling. Inclusion of independent variables having linear relationship with each other leads to parameter estimation with higher standard error. This in turn leads to inaccurate parameter estimation. Furthermore, due to inaccurate parameters regression model becomes unstable. The unstable models performs badly on the validation and test samples. When model is unstable, its performance deteriorate very fast compared to stable model over the period, though model is scored on the data of sample of same population. In such a situation an analyst must investigate for the multicollinearity, before finalizing the model. Next question is how to investigate and which variable should be kept if some variables are found to ...

In the statistics, we use data to derive the information. It helps in business, research and governance.  We also develop to predict the value or behaviour of any dependent variable based on the historical data. We have following categories of the model based on the type of the variable When the dependent variable is continuous variable: 1.OLS  Linear Regression Model :  When the dependent variable is continuous variable and independent variables is/are continuous variable(s) 2.ANOVA:  When the dependent variable is continuous variable and independent variables is/are categorical variable(s) 3.ANCOVA :  When the dependent variable is continuous variable and as independent variables, we have   continuous  as well as categorical variables as independent variable. When the dependent variable is categorical variable: 1. Maximum Likelihood Logistic Regression Model :  When the dependent variable is categorical variable and independent ...