The technique actually examines the relationship between a single dependent variable and more than one independent variable. It is the most common type of method used. It shows a linear relationship holding the lowest sum of the squared variances. The assumptions such as normality, equal variance and linearity and finely and clearly examined by the researcher. A coefficient knows as beta are taken which is the marginal impacts of each variable. This technique is usually used by the firm for its forecasting. This technique is actually the variation of multiple regressions and allows the firms to predict different events. The technique used non-metric dependent variables and the actual objective of the technique is to achieve a probabilistic assessment of binary choice. The independent variable used by the researchers is either continuous or discrete. Later, a contingency table is formed holding the classification of observations and the observations and predicted results are matched. Then the researcher sum the events that are predicted to occur and they actually occur and sum the events that the researcher predicted not to occur and they actually do not occur. These two sums are added and divided by the total number of events. This shows the effectiveness of the model and helps to predict the choices.  .