Regression Model Validation Assignment Help

Regression analysis has worldwide popularity in the field of analysis and the development of empirical methods. After regression model is found, one proceeds to use the model for prediction, or control, or the mechanism which has generated the data. Most people have the tendency to check the validity of the model.

The basic purpose of the regression model is to describe the rapport between a set of predictor variables and one or more responses.The regression models usage in practical life can act as a guide to us for many validation techniques.

VALIDATION TECHNIQUES

When a model is prepared then mostly checkers go for validation model procedures which can be used and it is practical to do so. The following procedures are useful in checking the validity of the regression models

• Comparison of the model predictions and coefficients with physical theory.
• Collection of new data to check model predictions.
• Comparison of results with theoretical models and simulated data.
• For the reservation of a part or the portion of the data which is available to obtain the measure which is independent and that is of model prediction accuracy.

Check on model predictions and coefficients with physical theory – A check on model predictions and coefficients should be made as soon as the model is developed. If the negative predictions are released theoretically with wrong sign, thenit is estimated that the model is a result of poor estimation. Marquardt and Snee has provided three potential models for this data: a) A nine- term full quadratic model fitted by least squares; b) A five – term subset of the quadratic model fitted by least squares; c) A nine – term full quadratic model developed by ridge regression techniques.

Collection of new data to check model predictions – Another method of model validation is the collection of statics data which can be compared with the predictions of the model. If the data is collected in a proper form, then it provides an overall check on the entire model construction process.

Comparison of results with theoretical models and simulated data – In this method an empirical model is prepared to provide a simulated data developed from a theoretical model.

Data splitting – The data provided is divided into two parts namely estimation data and prediction data. If data is collected in a sequence at proper time, then data has an estimation set and the prediction set. For example, Cady and Allen used the press algorithm to develop a corn yield prediction equation from four years of data published by Laird and Cady.

PROCESS OF BUILDING A REGRESSION MODEL

Model building can be done in three to four steps –

• Data collection and preparation – data can be collected by controlled experiments and observational studies.
• Reduction of explanatory or predictor variables – this refers to variable selection for each design of the model.
• Model refinement and selection – In this checking is done that whether the model is fit for validation or not.
• Model validation – At last validity is checked.