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It is written as per ""Higher Vocational Education Higher Mathematics Teaching Requirement"" by the Ministry of Education. It has in 3 parts, linear algebra, probability theory, and mathematical statistics, in 12 chapters, including determinant, matrix, n-dimensional vector and system of linear equations, diagonal proportion of matrix, quadric form, random event and its probability, stochastic variable and probability distribution, numerical characteristics of stochastic variable, sample and sample distribution, parameter estimation, hypothesis testing, variance analysis and analysis of regression, etc. The concise language, proper difficulty, and appropriate volume is suitable for higher vocational education school students and for engineers and technicians too.
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Linear Algebra and Probability and Statistics (Eleventh Five Year Plan of higher education teaching) Higher public teaching materials series(Chinese Edition)
Seller: liu xing, Nanjing, JS, China
Seller rating 5 out of 5 stars
paperback. Condition: New. Ship out in 2 business day, And Fast shipping, Free Tracking number will be provided after the shipment.Pages Number: 208 Publisher: Science Pub. Date :2008-02-01 version 1. This book is based on the Ministry of Education. Vocational Education and Higher Mathematics Teaching basic requirements. written. including linear algebra. probability theory and Part 3 of mathematical statistics. a total of 12 chapters. which are determinant. matrices. n-dimensional vector and linear equations. matrix diagonal ratio. quadratic. random events and their probability. random variables and their probability distribution. random variable the number of features. sample and sampling distribution. parameter estimation. hypothesis testing. analysis of variance and regression analysis. in addition. the book also gives reference to the answer of the chapter exercises. Book is concise language. content. moderate depth. capacity appropriate. as higher vocational school textbooks. but also for engineering and technical officers. Contents: Chapter 1 1.1 determinant determinant definition. nature and calculation of second-and third-order determinant 1.1.1 1.1.2 1.1.3 determinant of the nature of the determinant by row (column) started 1.1.4n order ranks exercises a type 1.2 Cramer's Rule 2.1 in Chapter 2 Matrix Matrix Matrix concept and the concept of operations 2.1.1 2.1.2 2.2 matrix rank of the matrix operations and matrix rank of the matrix elementary transformation 2.2.1 2.2.2 Matrix 2.3 2.2.3 elementary elementary transformation matrix and its inverse matrix inverse matrix Method to 2.3.1 Method to one of the concepts and the nature of the inverse matrix 2.3.2 2.3.3 Method to inverse orthogonal matrix of two 2.4 2.4.1 with the block matrix orthogonal matrix 2.4.2 Problem 2 block matrix in Chapter 3 and n-dimensional vector of linear equations 3.1n-dimensional vector 3.1.1n-dimensional vector is defined 3.1.2n 3.2-dimensional vector computing linear correlation vector group 3.2. a linear correlation with the linearly independent vectors linear phase 3.2.2 (no) closed 3.3 matrix to determine the law with a great group of linearly independent vectors a great group of rank 3.3.1 3.3.2 vectors linearly independent set of rank of 3.4 for solving linear equations in Chapter 4 Problem three matrix diagonalization 4.1 eigenvalues ??and eigenvectors of the similarity matrix 4.2 Diagonalization 4.3 vector with the inner product of vector inner product 4.3.2 4.3.1 4.3.3 linearly independent orthogonal vectors orthogonal normalized vectors 4.4 Diagonalization of a real symmetric matrix in Chapter 5 Exercise four quadratic and quadratic matrix 5.1 5.2 of the standard quadratic form 5.2. an orthogonal transformation of the quadratic form 5.2.2 for the standard method used with the standard form of quadratic positive definite quadratic form 5.3 Exercises Chapter 6. five random events and their probability of random events 6.1 6.1 6.1.1 random phenomenon. 2 random events and the relationship between sample space and event operations 6.1.3 6.1.4 6.2 frequency and probability of the conditional probability of the classical probability model 6.3 6.3.1 6.3.2 conditional probability and the total probability formula for the multiplication formula for the independence of events 6.4 6.5 Bernoulli probability model and the binomial probability formula 6.5.1 Bernoulli probability model the binomial probability formula 6.5.2 Chapter 7 Exercise six probability distribution of random variables and random variables and its distribution function 7.1 7.1.1 7.1.2 distribution function of random variables 7.2 Discrete 7.2.1 Discrete random variables and the probability distribution of random variables 7.2.2 of several commonly used discrete random variables and continuous random variables distributed 7.3 7.3.1 7.3.2 of several continuous random variables commonly used in continuous distributed random variables and two-dimensional random variable and its distribution 7.4 7.4.1 7.4.2. Seller Inventory # LQ0480