This book provides the reader with user-friendly applications of normal distribution. In several variables it is called the multinormal distribution which is often handled using matrices for convenience. The author seeks to make the arguments less abstract and hence, starts with the univariate case and moves progressively toward the vector and matrix cases. The approach used in the book is a gradual one, going from one scalar variable to a vector variable and to a matrix variable. The author presents the unified aspect of normal distribution, as well as addresses several other issues, including random matrix theory in physics. Other well-known applications, such as Herrnstein and Murray's argument that human intelligence is substantially influenced by both inherited and environmental factors, will be discussed in this book. It is a better predictor of many personal dynamics — including financial income, job performance, birth out of wedlock, and involvement in crime — than are an individual's parental socioeconomic status, or education level, and deserve to be mentioned and discussed.
Contents:
- Review
- The Univariate Normal Distribution
- Limit Theorems
- Distributions Derived from the Normal
- Applications of the Univariate Normal Distribution
- Bayes Approach in Applied Probability
- Bivariate and Trivariate Normal Distributions
- Probability, Historical Survey, and Discussion
- Normal Theory in ?p
- Some Applications of the Multivariate Normal Distribution in Science and Engineering
- Functions of Matrices and Jacobians of Transformations
- Matrix Variate Distributions
- Complements and Supplementary Information
Readership: Economics and Finance academics and students; Mathematics and statistics academics and students.
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