ABSTRACT
Regression analysis examines the relationship between two or more variables and derives a representative equation expressing the relationship between these variables. One of the variables is called the dependent variable because it is 'dependent' or a consequence of the other variables in the equation. These other variables are called independent variables because each of the independent variables is considered a uniquely distinct dataset. Simple linear regression can be useful in examining cost reduction and product improvement types of new products. Nonlinear regression is particularly applicable in modeling the life cycle of new products when using a variation of time series regression where time is the sole source for independent variables. When conducting the regression analysis, the constant in the regression model can be ignored because it provides the scale for the model and is not used for relative comparisons. Aside from customer analysis, logistic regression can be used to evaluate new product development projects.
