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چکیده
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The linear regression model is a good method for predicting one variable (the response variable) in relation to other variables (auxiliary variables or independent variables). The model’s fundamental assumption is that the response variable contains real numbers. However, in practice, we frequently encounter cases where the response variable is restricted to specific ranges, such as data in the form of percentages and percentages restricted to (0, 1). Ratio and percentage data, on the other hand, are frequently skewed, and inference based on the assumption that the data is symmetric can be misleading. The beta regression model, defined in the (0, 1) range, is a suitable regression model for this type of data. The response variable is assumed to follow the beta distribution in this regression model. In this thesis, we present the beta regression model and the method for estimating model parameters when the independent variables are orthogonal and non-orthogonal. In first case, we will use the maximum likelihood estimators and for second one, we will use the common method of combating the multicollinearity in regression model such as Ridge and Liu estimator. In the following, by using Monte Carlo simulation, the mean squared error criterion and efficiency of the estimators are calculated. Finally, the applications of these estimators on the real life data set is examined.
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