Librería: Reader's Corner, Inc., Raleigh, NC, Estados Unidos de America
EUR 44,12
Cantidad disponible: 1 disponibles
Añadir al carritoHardcover. Condición: Fine. Estado de la sobrecubierta: No DJ. First Edition, First Printing. With previous owner's name, otherwise a fine first edition, first printing hardcover copy, no DJ, green spine.
Librería: Reader's Corner, Inc., Raleigh, NC, Estados Unidos de America
EUR 53,13
Cantidad disponible: 1 disponibles
Añadir al carritoHardcover. Condición: As New. Estado de la sobrecubierta: No DJ. First Edition, First Printing. This is a fine, as new, first edition, first printing hardcover copy, no DJ, green spine. 528 pages with laid in errata and clarifications sheet.
Librería: BennettBooksLtd, Los Angeles, CA, Estados Unidos de America
EUR 102,87
Cantidad disponible: 1 disponibles
Añadir al carritohardcover. Condición: New. In shrink wrap. Looks like an interesting title!
Librería: Mispah books, Redhill, SURRE, Reino Unido
EUR 93,26
Cantidad disponible: 1 disponibles
Añadir al carritoHardcover. Condición: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 94,74
Cantidad disponible: 1 disponibles
Añadir al carritoGebundene Ausgabe. Condición: Sehr gut. Gebraucht - Sehr gut SG - leichte Beschädigungen oder Verschmutzungen, ungelesenes Mängelexemplar, gestempelt - Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory. This much-needed work presents the relevant aspects of the theory of matrix algebra for applications in statistics. It moves on to consider the various types of matrices encountered in statistics, such as projection matrices and positive definite matrices, and describes the special properties of those matrices. Finally, it covers numerical linear algebra, beginning with a discussion of the basics of numerical computations, and following up with accurate and efficient algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors.