Mathematical software
Date: 2018-10-24 Views: 17

Course name: Mathematical software

Course number: SMI2123204     Credit: 2

Course Description

Mathematical software begins at the third semester, and this course is a professional course of applied mathematics. Through the Matlab software learning, the students should master the basic commands, mapping function, programming language, regression analysis and basic modeling method, which provide software support for subsequent courses calculation method, data analysis.

  

Course Learning Objectives

The student learning objectives are what student would be able to know and to do on the completion of this course. In details are:

1 Mastering various processing methods of large sample data;

2 Mastering the mapping methods of space curve and curved surface in the space;

3 Mastering the programming methods of conditional statements, the statements of the loop;

4 Learning to solve the integral equations or differential equations;

5 Mastering the Matlab command of multiple linear regression;

6 Mastering the basic methods of parameter estimation and hypothesis testing.

  

Pre-course:  Database application, Mathematical analysis, Advanced algebra

  

Textbooks and References:

Textbooks: Hu Shouxin, Li Bainian. The mathematics experiment with Matlab. Science Press, 2004.

References: Li Jicheng, Dai Yonghong. The mathematics experiment. Xi'an Jiao Tong University press, 2003.

  

Course Assessment

Form

Weight (%)

Attendance and homework

20%

Mid - term exam

0%

Final exam

80%

Course schedule

Weeks

Teaching content

Chapter

1-4

  

Lecture 1  Processing methods of large sample data

1.The basic   matrix operations

2.The   distance and angle cosine of vectors

3. Property and processing method of data

Chapter 1

5

Lecture 2 Mapping of the functions

4. Mapping of the 2-D functions

5. Mapping of the 3-D functions

Chapter 2

6

Lecture 3 Programming of Matlab

6. Programming of Matlab   software

Chapter 3

7-9

Lecture   4 The numerical analysis method

7. The numerical   solutions of the linear equations and the nonlinear equations

8. Linear interpolation   and surface interpolation

9. Calculation of the   calculus for the function

Chapter 4

10-13

Lecture 5  The method of regression   analysis

10. Single-element   regression model

11. Multiple linear   regression model

Chapter 5

14-15

Lecture 6 Statistical methods

12. Descriptive statistic

13. Parameter estimation   and hypothesis testing

Chapter 6

16-17

Lecture 7 Review   and final exam