In the realm of Business Intelligence (BI), understanding the intricacies of data analysis is crucial for making informed decisions. One such complexity is the use of dummy variables in multiple ...

Dummy variables can be tested for their significance and relevance in regression models by using various statistical tests and criteria, such as the T-test, F-test, R-squared, and Adjusted R-squared.

Profit is the dependent variable. The investor wants to see if there is a correlation between the expenditure on the predictors (independent) and the outcome (profit). In the dataset, we see a ...

Most times regression analysis requires that we have independent and dependent variables that are continuous (e.g., examining the effects of attendance on a numerical class grade). However, regression ...

A regression equation with a zillion dummy variables in it is hard to read and has little generalizable business value. For example, instead of having a factor “city” with many different levels/values ...

This paper applies a simple multiple regression based model to analyze financial data. The model uses a dummy variable as the dependent variable which could be interpreted as a predictor. The ...

Multiple Regression. You can create multiple regression models quickly using the fit variables dialog. You can use diagnostic plots to assess the validity of the models and identify potential outliers ...

This problem was solved by principal component regression (PCR), but the PCR model resulted heterogeneous errors. PCR model was modified to overcome the errors with adding dummy variables to the model ...

A variance inflation factor (VIF) provides a measure of multicollinearity among the independent variables in a multiple regression model. Detecting multicollinearity is important because while ...

Multiple linear regression (MLR) is a statistical technique that uses several explanatory variables to predict the outcome of a response variable.