1. Introduction to Regression Models. 2. Simple Linear Regression. 3. A Review of Matrix Algebra ... more 1. Introduction to Regression Models. 2. Simple Linear Regression. 3. A Review of Matrix Algebra and Important Results of Random Vectors. 4. Multiple Linear Regression Model. 5. Specification Issues in Regression Models. 6. Model Checking. 7. Model Selection. 8. Case Studies in Linear Regression. 9. Nonlinear Regression Models. 10. Regression Models for Time Series Situations. 11. Logistic Regression. 12. Generalized Linear Models and Poisson Regression. Brief Answers to Selected Exercises. Statistical Tables. References.
Statisticians have devised many tools for application and these are available to be utilized for ... more Statisticians have devised many tools for application and these are available to be utilized for general business improvement and industrial problem solving. However, there is a wide gap between the available tools and what are practiced in business and industrial organizations. Thus it is important for statisticians to direct serious attention to bridging this gap if statistics is to be relevant in business and industry and to the society at large. In this paper we look at some ideas for implementation of statistical methods in business and industry.
Every experimenter has at some time or other faced data which seem to contain some deviant or “ou... more Every experimenter has at some time or other faced data which seem to contain some deviant or “outlying” observations. The problem of outliers in data is an old one and was one of the first to receive a statistical treatment.
Communications in Statistics - Simulation and Computation, 2015
Abstract We consider a stochastic frontier regression model with a time dependent efficiency proc... more Abstract We consider a stochastic frontier regression model with a time dependent efficiency process, which is assumed to follow an exponential autoregressive sequence. The likelihood for the model is derived in the context of a bivariate exponential distribution. Bayesian method is suggested for the estimation of parameters. We apply the model and the estimation procedure to a panel of US airlines data and show empirically that the model is dynamic in the sense that it reveals improvement in the efficiency or reduction in the inefficiency over time.
Quality Improvement Through Statistical Methods, 1998
The automotive Quality System Requirements standard QS-9000 is often presented as “ISO 9000 plus ... more The automotive Quality System Requirements standard QS-9000 is often presented as “ISO 9000 plus specific automotive interpretations”. However, QS-9000 and ISO 9000 are fundamentally different in their approach and philosophy. While ISO 9000 defines quality system requirements to control product quality through inspection and control, QS-9000’s approach is to prevent poor quality first by focusing on the design and then by controlling the manufacturing processes that produce the product.
Some Risks in the Construction and Analysis of Supersaturated Designs B. Abraham a , H. Chipman b... more Some Risks in the Construction and Analysis of Supersaturated Designs B. Abraham a , H. Chipman b & K. Vijayan c a Department of Statistics and Actuarial Science and Institute for Improvement in Quality and Productivity , University of Waterloo , Waterloo , Ontario , N2L 3Gl , Canada E-mail: b Department of Statistics and Actuarial Science and Institute for Improvement in Quality and Productivity , University of Waterloo , Waterloo , Ontario , N2L 3Gl , Canada E-mail: c Department of Mathematics , University of Western Australia , Nedlands , Western Australia , Australia E-mail: Published online: 12 Mar 2012.
1. Introduction to Regression Models. 2. Simple Linear Regression. 3. A Review of Matrix Algebra ... more 1. Introduction to Regression Models. 2. Simple Linear Regression. 3. A Review of Matrix Algebra and Important Results of Random Vectors. 4. Multiple Linear Regression Model. 5. Specification Issues in Regression Models. 6. Model Checking. 7. Model Selection. 8. Case Studies in Linear Regression. 9. Nonlinear Regression Models. 10. Regression Models for Time Series Situations. 11. Logistic Regression. 12. Generalized Linear Models and Poisson Regression. Brief Answers to Selected Exercises. Statistical Tables. References.
Statisticians have devised many tools for application and these are available to be utilized for ... more Statisticians have devised many tools for application and these are available to be utilized for general business improvement and industrial problem solving. However, there is a wide gap between the available tools and what are practiced in business and industrial organizations. Thus it is important for statisticians to direct serious attention to bridging this gap if statistics is to be relevant in business and industry and to the society at large. In this paper we look at some ideas for implementation of statistical methods in business and industry.
Every experimenter has at some time or other faced data which seem to contain some deviant or “ou... more Every experimenter has at some time or other faced data which seem to contain some deviant or “outlying” observations. The problem of outliers in data is an old one and was one of the first to receive a statistical treatment.
Communications in Statistics - Simulation and Computation, 2015
Abstract We consider a stochastic frontier regression model with a time dependent efficiency proc... more Abstract We consider a stochastic frontier regression model with a time dependent efficiency process, which is assumed to follow an exponential autoregressive sequence. The likelihood for the model is derived in the context of a bivariate exponential distribution. Bayesian method is suggested for the estimation of parameters. We apply the model and the estimation procedure to a panel of US airlines data and show empirically that the model is dynamic in the sense that it reveals improvement in the efficiency or reduction in the inefficiency over time.
Quality Improvement Through Statistical Methods, 1998
The automotive Quality System Requirements standard QS-9000 is often presented as “ISO 9000 plus ... more The automotive Quality System Requirements standard QS-9000 is often presented as “ISO 9000 plus specific automotive interpretations”. However, QS-9000 and ISO 9000 are fundamentally different in their approach and philosophy. While ISO 9000 defines quality system requirements to control product quality through inspection and control, QS-9000’s approach is to prevent poor quality first by focusing on the design and then by controlling the manufacturing processes that produce the product.
Some Risks in the Construction and Analysis of Supersaturated Designs B. Abraham a , H. Chipman b... more Some Risks in the Construction and Analysis of Supersaturated Designs B. Abraham a , H. Chipman b & K. Vijayan c a Department of Statistics and Actuarial Science and Institute for Improvement in Quality and Productivity , University of Waterloo , Waterloo , Ontario , N2L 3Gl , Canada E-mail: b Department of Statistics and Actuarial Science and Institute for Improvement in Quality and Productivity , University of Waterloo , Waterloo , Ontario , N2L 3Gl , Canada E-mail: c Department of Mathematics , University of Western Australia , Nedlands , Western Australia , Australia E-mail: Published online: 12 Mar 2012.
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