University Press of America
Pages: 544
•
Trim: 8½ x 11
978-0-7618-6171-3 • Paperback • August 2013 • $101.99 • (£78.00)
Bayo Lawal is professor of statistics at Kwara State University, Nigeria. He received his bachelor of science degree with honors in mathematics from the Ahmadu Bello University, Nigeria, and his master’s degree in biometry from the University of Reading, UK. His PhD in statistics is from the University of Essex, UK. Lawal has taught for several years at the University of Ilorin, Nigeria; St. Cloud State University, St. Cloud, Minnesota; and Temple University in Philadelphia. He has also served as chair of the Departments of Statistics at St. Cloud State University and at the University of Ilorin. He served as dean of the School of Sciences in Auburn University at Montgomery between 2004 and 2008, as well as the dean of the School of Arts and Sciences at the American University of Nigeria between 2008 and 2011. He currently serves as head of the Department of Statistics and Mathematical Sciences at Kwara State University.
Felix Famoye is a professor and a consulting statistician in the Department of Mathematics at Central Michigan University in Mount Pleasant, Michigan. He received his bachelor of science degree with honors in statistics from the University of Ibadan, Nigeria, and his PhD in statistics from the University of Calgary under the Canadian Commonwealth Scholarship Program. He received the College of Science and Technology Outstanding Teaching Award and the University Excellence in Teaching Award. He also received the College of Science and Technology Outstanding Research Award and the University President’s Award for Outstanding Research and Creative Activity. As a Fulbright scholar, he visited the University of Lagos in Nigeria.
1: Introduction
2: Simple Linear Regression
3: Inferences on Parameter Estimates
4: Mutiple Linear Regression
5: Regression Diagnostics and Remedial Methods
6: Multiple and Partial Correlations
7: Model Selection Strategies
8: Use of Dummy Variables in Regression Analysis
9: Polynomial Regression
10: Logistic Regression
11: Count Data Regression Models
12: Regression with Censored of Truncated Data
13: Nonlinear Regression
14: One-Way Analysis of Variance
15: Two-Factor Analysis of Variance
16: Analysis of Covariance
17: Randomized Complete Block Design
18: Non Orthogonal Classification
Applied Statistics . . . extensive coverage of regression and analysis of variance techniques combined with its exemplary demonstration of SAS coding through examples make it a valuable resource for a variety of statistics users. . . [W]hile Applied Statistics could effectively be used as a course textbook . . . [I]t may be even more attractive to researchers for use as a reference tool.
— The American Statistician