Regression Analysis And Regression Analysis

In statistical modeling, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors').

We have learn in the study material about regression analysis and placing it within a context of model building as a whole. Regression analysis is an important technique allowing the analyst to explore relationships between a dependent variable and other independent variables. In doing this, the aim is to build a model which will allow us to understand the behavior of the dependent variable and make predictions of what values it

The next section of the unit removes this restriction and we examine an approach called multiple regression.

Definition of the multivariate model:

DEFINITION of 'Multivariate Model' A popular statistical tool that uses multiple variables to forecast possible outcomes. ... The Monte Carlo simulation is a widely used multivariate model that creates a probability distribution that helps define a range of possible investment outcomes.

To analyses the data in a multivariate model by using scatter plots and correlation data to explore pairwise relationships between the dependent and each of the independent variables. Multivariate regression procedure itself, uses Microsoft Excel to obtain the output. Although much more complex and demanding in terms of arithmetic, this procedure is based on an extension of the least squares method introduced earlier when discussing the simple linear case. In the next section we shall examine the effectiveness of a multivariate model, stressing the similarities to and differences from the procedures used with the simple

Below are the key factors that you should practice to select the right regression model:

1.Data exploration is an inevitable part of building predictive model. It should be you first step before selecting the right model like identify the relationship and impact of variables

2.To compare the goodness of fit for different models, we can analyses different metrics like statistical significance of parameters, R-square, Adjusted r-square, AIC, BIC and error term. Another one is the Mallow’s Cp criterion. This essentially checks for possible bias in your model, by comparing the model with all possible sub-models (or a careful selection of them).

3.Cross-validation is the best way to evaluate models used for prediction. Here you divide your data set into two group (train and validate). A simple mean squared difference between the observed and predicted values give you a measure for the prediction accuracy.

4.If your data set has multiple confounding variables, you should not choose automatic model selection method because you do not want to put these in a model at the same