Advanced Statistics:Linear Regression, Part II: Multiple Linear Regression

Part II Multiple Linear Regression

Some Modifications to Calculate Regression Coefficients in Multiple Linear Regression

In a multiple linear regression model, there are instances where one has to update the regression parameters. In such models as new data become available, by adding one row to the design matrix, the least-squares estimates for the parameters must be updated to reflect the impact of the new data. We will modify two existing methods of calculating regression coefficients in multiple linear regres...

Linear Regression Under Multiple

This dissertation studies the least squares estimator of a trend parameter in a simple linear regression model with multiple changepoints when the changepoint times are known. The error component in the model is allowed to be autocorrelated. The least squares estimator of the trend and the variance of the trend estimator are derived. Consistency and asymptotic normality of the trend estimator a...

Interval linear regression

‎In this paper‎, ‎we have studied the analysis an interval linear regression model for fuzzy data‎. ‎In section one‎, ‎we have introduced the concepts required in this thesis and then we illustrated linear regression fuzzy sets and some primary definitions‎. ‎In section two‎, ‎we have introduced various methods of interval linear regression analysis‎. ‎In section three‎, ‎we have implemented nu...

Fixed Bed Column Modeling of Cd(II) Adsorption on Bone Char Using Backward Bayesian Multiple Linear Regression

In this work, the backward Bayesian multiple linear regression (BBMLR) as a new approach is presented to predict the adsorption efficiency (AE) of Cd(II) ions by ostrich bone char (OBC) in the fixed bed adsorption with four operational variables consisting of pH (2-9), inlet Cd(II) concentration (Co= 25-100 mg/L), bed depth (h= 3-9 cm) and feed flow rate (Q= 0.5-30 mL/min). The performance of t...