Henry De Graft Acquah Knihy

The book provides an Introduction to linear programming with applications to economic analysis. Some of the major areas covered in this book includes Linear Algebra Basics, Formulation of Linear Programming Problems, Basic Concepts and Terminologies, The Structure of Linear Programming, Methods for Solving Linear Programming Problems, and Duality and Sensitivity Analysis. Applications to economic analysis are demonstrated using the R programming language. Economic analysis emphasises farm resource allocation problems involving revenue maximization, cost minimization, profit maximization, optimal crop enterprise combinations, factor substitution, crop rotation, quality difference in labour, and buying or selling resources among others. This book is a practical text that is comprehensive and suitable for teaching, self-study and reference.

This first edition employs an 11-chapter structure, guiding users through quantitative methods in a coherent manner. The initial chapter introduces basic concepts, including statistics, population and sample, scales of measurement, and data types. The second chapter covers frequency distribution and graphs, while the third provides detailed insights into measures of central tendency, such as mean, mode, median, and their relationships, alongside geometric, harmonic, and weighted means. Chapter four focuses on measures of variability, discussing range, mean deviation, variance, standard deviation, and coefficient of variation, as well as distribution shapes. The fifth chapter defines probability and explores classical, empirical, and subjective approaches, including rules of probability and Bayes’ theorem. Chapter six emphasizes probability distributions, covering random variables, density and mass functions, and key distributions like Binomial, Poisson, and Normal. The seventh chapter introduces point and interval estimation, followed by hypothesis testing in chapter eight. Non-parametric tests and their assumptions are examined in chapter nine. Chapter ten discusses correlation and linear regression analysis, while the final chapter introduces generalized linear models, focusing on logistic and Poisson regression. Practical application examples using R programming are provided throughout chapters 2 to 11, making this text com