Nalini Ravishanker, Zhiyi Chi and Dipak K. Dey are Professors in the Department of Statistics at the University of Connecticut, Storrs, USA.

• 1. A Review of Vector and Matrix Algebra. 2. Properties of Special Matrices. 3. Generalized Inverses and Solutions to Linear Systems. 4. The General Linear Model. 5. Multivariate Normal and Related Distributions. 6. Sampling from the Multivariate Normal Distribution. 7. Inference for the General Linear Model-I. 8. Inference for the General Linear Model-II. 9. Multiple Linear Regression Models. 10. Fixed-Effects Linear Models. 11. Random-Effects and Mixed-Effects Models. 12. Generalized Linear Models. 13. Special Topics. 14. Miscellaneous Topics. Appendices.

"A First Course in Linear Model Theory is an excellent graduate-level textbook that comprehensively covers the now classical linear regression model. Its well-structured organization, thorough mathematical review, and clear presentation of core concepts make it an excellent, self-contained resource for a first course in linear models, both for instructors and students. Moreover, the book offers numerous examples, several exercises (some with solutions), R code, and detailed proofs for key results, making it also a good resource for self-study."Carlos Cinelli, University of Washington USA, The American Statistician, October 2023.

Nalini Ravishanker, Zhiyi Chi and Dipak K. Dey are Professors in the Department of Statistics at the University of Connecticut, Storrs, USA.

• 1. A Review of Vector and Matrix Algebra. 2. Properties of Special Matrices. 3. Generalized Inverses and Solutions to Linear Systems. 4. The General Linear Model. 5. Multivariate Normal and Related Distributions. 6. Sampling from the Multivariate Normal Distribution. 7. Inference for the General Linear Model-I. 8. Inference for the General Linear Model-II. 9. Multiple Linear Regression Models. 10. Fixed-Effects Linear Models. 11. Random-Effects and Mixed-Effects Models. 12. Generalized Linear Models. 13. Special Topics. 14. Miscellaneous Topics. Appendices.

"A First Course in Linear Model Theory is an excellent graduate-level textbook that comprehensively covers the now classical linear regression model. Its well-structured organization, thorough mathematical review, and clear presentation of core concepts make it an excellent, self-contained resource for a first course in linear models, both for instructors and students. Moreover, the book offers numerous examples, several exercises (some with solutions), R code, and detailed proofs for key results, making it also a good resource for self-study."Carlos Cinelli, University of Washington USA, The American Statistician, October 2023.

Nalini Ravishanker, Zhiyi Chi and Dipak K. Dey are Professors in the Department of Statistics at the University of Connecticut, Storrs, USA.

• 1. A Review of Vector and Matrix Algebra. 2. Properties of Special Matrices. 3. Generalized Inverses and Solutions to Linear Systems. 4. The General Linear Model. 5. Multivariate Normal and Related Distributions. 6. Sampling from the Multivariate Normal Distribution. 7. Inference for the General Linear Model-I. 8. Inference for the General Linear Model-II. 9. Multiple Linear Regression Models. 10. Fixed-Effects Linear Models. 11. Random-Effects and Mixed-Effects Models. 12. Generalized Linear Models. 13. Special Topics. 14. Miscellaneous Topics. Appendices.

"A First Course in Linear Model Theory is an excellent graduate-level textbook that comprehensively covers the now classical linear regression model. Its well-structured organization, thorough mathematical review, and clear presentation of core concepts make it an excellent, self-contained resource for a first course in linear models, both for instructors and students. Moreover, the book offers numerous examples, several exercises (some with solutions), R code, and detailed proofs for key results, making it also a good resource for self-study."Carlos Cinelli, University of Washington USA, The American Statistician, October 2023.