Bu Linear Algebra With Applications 6e Ssg
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Ssg- Linear Algebra with Applications 6e Student Sol Man
Author | : Williams, Angela Aprn Edd(c) |
Publisher | : |
Total Pages | : 0 |
Release | : 2007-08-24 |
Genre | : |
ISBN | : 9780763755881 |
Bu- Linear Algebra
Author | : Cheney |
Publisher | : |
Total Pages | : 154 |
Release | : 2008-06-20 |
Genre | : Algebras, Linear |
ISBN | : 9780763763206 |
MATLAB Control Systems Engineering
Author | : Cesar Lopez |
Publisher | : Apress |
Total Pages | : 170 |
Release | : 2014-09-22 |
Genre | : Computers |
ISBN | : 1484202899 |
MATLAB is a high-level language and environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java. MATLAB Control Systems Engineering introduces you to the MATLAB language with practical hands-on instructions and results, allowing you to quickly achieve your goals. In addition to giving an introduction to the MATLAB environment and MATLAB programming, this book provides all the material needed to design and analyze control systems using MATLAB’s specialized Control Systems Toolbox. The Control Systems Toolbox offers an extensive range of tools for classical and modern control design. Using these tools you can create models of linear time-invariant systems in transfer function, zero-pole-gain or state space format. You can manipulate both discrete-time and continuous-time systems and convert between various representations. You can calculate and graph time response, frequency response and loci of roots. Other functions allow you to perform pole placement, optimal control and estimates. The Control System Toolbox is open and extendible, allowing you to create customized M-files to suit your specific applications.
Regression
Author | : N. H. Bingham |
Publisher | : Springer Science & Business Media |
Total Pages | : 293 |
Release | : 2010-09-17 |
Genre | : Mathematics |
ISBN | : 1848829698 |
Regression is the branch of Statistics in which a dependent variable of interest is modelled as a linear combination of one or more predictor variables, together with a random error. The subject is inherently two- or higher- dimensional, thus an understanding of Statistics in one dimension is essential. Regression: Linear Models in Statistics fills the gap between introductory statistical theory and more specialist sources of information. In doing so, it provides the reader with a number of worked examples, and exercises with full solutions. The book begins with simple linear regression (one predictor variable), and analysis of variance (ANOVA), and then further explores the area through inclusion of topics such as multiple linear regression (several predictor variables) and analysis of covariance (ANCOVA). The book concludes with special topics such as non-parametric regression and mixed models, time series, spatial processes and design of experiments. Aimed at 2nd and 3rd year undergraduates studying Statistics, Regression: Linear Models in Statistics requires a basic knowledge of (one-dimensional) Statistics, as well as Probability and standard Linear Algebra. Possible companions include John Haigh’s Probability Models, and T. S. Blyth & E.F. Robertsons’ Basic Linear Algebra and Further Linear Algebra.