Hi Deeksha, The linear model is used for attempting linear regression. In linear regression data are modeled using linear predictor functions and unknown model parameters are estimated from the data. Such models are called linear models.Regression is a data mining function that predicts a number. Profit, sales, mortgage rates, house values, temperature or distance can be all predicted using regression techniques. For instance: a regression model can be used to predict the value of a house based on the number of rooms, location, lot size and other factors. A regression task begins with a data set in which target values are known. For example, a regression model that predicts the house values could be based on observed data of various houses over a period of time. In addition to the value, the data might track the age of the house, square footage, number of rooms, taxes, proximity to the shopping centers and so on. House value would be the target and other values will be the predictors and data for each house will constitute a case. In the model build(training) process, a regression algorithm estimates the value of the target as a function of the predictors for each case in the build data. These relationships between predictors and target are summarized in a model, which can then be applied to a different data set in which the target values are unknown. Regression models are tested by computing the various statistics that calculates the difference between the predicted and expected values. The historical data for a regression model is typically divided into two data sets-one for building the model and the other for testing the model. A linear regression technique is used if the relationship between the predictors and target can be approximated with a straight line.