RegressionAnalysisEstimatingRelationships.ppt

Regression Analysis: Estimating Relationships Purpose of Regression Analysis Procedure for Building Regression Models Selecting Independent Variables: Scatter Plots Selecting Independent Variables: Correlation Analysis Simple Linear Regression Example of Simple Linear Regression: Defining Objective(s) Example of SLR: Select Independent Variable Example of SLR: Collect and Organize Data Example of SLR: Estimate Coefficients Example of SLR: Testing the Model Example of SLR: Implementing and Using the Model * Regression Analysis is a study of relationship between a set of independent variables and the dependent variable. Independent variables are characteristics that can be measured directly (example the area of a house). These variables are also caled predictor variables (used to predict the dependent variable) or explanatory variables (used to explain the behavior of the dependent variable). Dependent variable is a characteristic whose value depends on the values of independent variables. Y = B0 + B1*X1 + B2*X2 + …… +/- E Dependent Variable Independent Variable Random Error Constant term Coefficients Now Future/Unknown Past / Experience / Known Explanation:Use regression analysis to develop a mathematical model to explain the variance in the dependent variable based on values of independent variables. Prediction: If the regression model adequately explains the dependent variable, use the model to predict values of the dependent variable. time Explain Selling Price of a house (dependent) based on its characteristics (independents). If the model is valid, use it for prediction. Develop Regression Model using known data (sample) Selling Price = 40,000 + 100(Sq.ft) + 20,000(#Baths) If the above model is reliable and valid, Use this model to predict the Selling Price of any house based on its area (Sq.ft.) and the number of bathrooms (#Baths) The constant term (40,000) is the fixed price of the house. This is not dependent on the values of the variables con