ESP1107 Computing and Statistics
Karl Erik BIRGERSSON Engineering Science Programme and Department of Chemical and Biomolecular Engineering, Faculty of Engineering, National University of Singapore E-mail:
[email protected] Office: E2-02-35
David John NOTT Department of Statistics & Applied Probability , Faculty of Science, National University of Singapore E-mail:
[email protected] Office: S16 -07-109
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COURSE INFORMATION LECTURES One 2-hour lecture and one 1 hour lecture / week Read the recommended textbooks and reference books Work with assignments
TUTORIALS One 1-hour tutorial / week with tutor
ASSESSMENT Continuous assessment component (40%) Two assignments (10% each, total 20%) Midterm examination (20%) 2-hour closed-book examination (60%)
WEBSITE Course Information: http://ivle.nus.edu.sg/
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OBJECTIVE OF COURSE The aim of this course is to learn: basic concepts in programming numerical analysis basics in probability and statistics that are essential in the acquisition, processing and interpretation of data. Most importantly, the course provides the basic concepts for scientific and engineering studies.
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LEARNING OUTCOME At the end of this computing and statistics course, you are expected to be able to: • Write C programs to solve “simple” problems. • Solve mathematical problems with the most common numerical methods. • Understand and be able to apply the basics of statistical analysis.
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Syllabus for COMPUTING part Introduction to Computer Systems and Software Computer hardware, computer languages, computer software.
Introduction to C Program structure, constants and variables, assignment statements, standard input and output, mathematical functions, character functions.
Control Structures and Data Files Algorithm development, structures, data files.
conditional
expressions,
selection
statements,
loop
Modular Programming with Functions Modularity, functions, macros, recursion.
Arrays, Pointers and Strings One-dimensional arrays, sorting and search algorithms, two-dimensional and higher dimensional arrays, addresses, pointers, character strings, dynamic memory allocation.
Structures Structures, functions with structures, arrays of structures, dynamic data structures.
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Syllabus for PROBABILITY & STATISTICS part Uncertainty and Probability Concept of probability, discrete random variables – binomial, Poisson, geometric, continuous random variables – normal, uniform, exponential
Simple Data Descriptions and Visualization Measures of location – mean, median; measures of variation – range, variance, median absolute deviation; skewness, kurtosis, quantiles, simple plots – histograms, boxplots, scatter plots, q-q plots.
Modeling of Data Linear regression – simple and multiple; nonlinear curve fitting, analysis of variance
Simulation Generation of random numbers, Monte Carlo integration.
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Syllabus for NUMERICAL METHODS part Error analysis Round-off errors, computer arithmetic, errors in scientific computation.
Solutions of equations of one variable The bisection method, the secant method, Newton’s method, error analysis and accelerating convergence.
Interpolation and Polynomial Approximation Lagrange polynomials, Hermite interpolation, spline interpolation.
Numerical Integration and Differentiation Basic quadrature rules, composite quadrature rules, improper integrals, numerical differentiation.
Numerical solution of Initial-Value Problems Taylor methods, Runge-Kutte methods.
Linear and Nonlinear Systems Gaussian elimination, Jacobi and Gauss-Seidel methods, Newton’s method for systems.
Linear Finite Difference Methods Linear finite difference methods. 7
Text books and references Textbook J.D. Faires & R. Burden, Numerical Methods, 3rd ed., Brooks/Cole, Thomson Learning Inc, US, 2003
References D. M. Etter, Engineering Problem Solving with C, 3rd ed., Pearson Prentice Hall, US, 2005 H. A. Koan, T. T. Choy, Programming Methodology Using C, 2nd ed., Pearson Prentice Hall, Singapore, 2004 W. H. Press, Numerical Recipes in C, 2nd ed., Cambridge University Press, Cambridge, UK, 2002 R.L. Burden & J.D. Faires, Numerical Analysis, 8th ed., Brooks/Cole, Thomson Learning Inc, US, 2005 Douglas C. Montgomery & George C. Runger, Applied Statistics and Probability for Engineers, 4th ed., Wiley, New York, 2006. 8
Time schedule 2007 Day
Monday
Tuesday
Wednesday
Thursday
Friday
Time
Module Code
Module Title
Type
Venue
0900 - 1000
PC1433
Mechanics and Waves
Lecture
EA 06-05
1300 - 1400
PC1433
Mechanic and Waves (Grp 1)
Tutorial
WS2
1100 - 1200
MA1507
Advanced Calculus
Lecture
E3A
1300 - 1400 1400 - 1500
MA1507
Advanced Calculus (Grp 2) Advanced Calculus (Grp 3)
Tutorial
E3A
0900 - 1100
ESP1107
Computing and Statistics
Lecture
E3A
1300 - 1400
CM1503
Organic Compunds and Their Transformation
Lecture
E1 06-09
1000 - 1100
PC1433
Mechanics and Waves (Grp 2)
Tutorial
WS2
1100 - 1200
MA1507
Advanced Calculus (Grp 1)
Tutorial
E3A
1300 - 1500
MA1507
Advanced Calculus
Lecture
E3A
0900 - 1100
PC1433
Mechanics and Waves
Lecture
E3A
1300 - 1400
ESP1107
Computing and Statistics
Lecture
E3A
1400 - 1500 1500 - 1600 1600 - 1700
ESP1107
Computing and Statistics (Grp 1) Computing and Statistics (Grp 2) Computing and Statistics (Grp 3)
Tutorial
E3A
0900 - 1100
CM1503
Organic Compunds and Their Transformation
Lecture
E1 06-08
1200 - 1300
CM1503
Organic Compunds and Their Transformation
Tutorial
E1 06-04
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Schedule of Lectures and Tutorials Week
Lectures
Tutorials
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L01: Overview of course; introduction to computer hardware & software; introduction to C L02: Introduction to C
T01: Compiler, editors, simple c programs
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L03: Control structures and data files L04: Modular programming with functions
T02: Control structures, data files, functions
3
L05: Introduction to statistics, uncertainty and probability, discrete random variables L06: Continuous random variables
T03: Probability and discrete random variables
4
L07: Descriptive measures of data L08: Simple data visualizations
T04: Continuous random variables and descriptive measures of data
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L09: Arrays, pointers and strings L10: Arrays, pointers and strings
T05: Mixed operations, arrays
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L11: Structures; error analysis L12: Error analysis
T06: Arrays, pointers, error analysis
Recess week (Sat 22 Sep – Sun 30 Sep) Hand in first assignment
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Schedule of Lectures and Tutorials Week
Lectures
Tutorials
7
L13: Solutions of equations of one variable L14: Interpolation and polynomial approximation
T07: Error analysis, solutions of equations of one variable
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L15: Interpolation and polynomial approximation L16: Midterm examination
T08: Interpolation and polynomial approximation
9
L17: Linear regression – simple and multiple L18: Nonlinear curve fitting
T09: Linear regression
10
L19: Simulation of random numbers, Monte Carlo integration L20: Analysis of variance (ANOVA)
T10: Nonlinear curve fitting, Monte Carlo integration
11
L21: Numerical integration and differentiation; L22: Numerical solution of initial-value problems
T11: Numerical integration and differentiation
12
L23: Linear and nonlinear systems. L24: Linear finite difference methods
T12: Numerical solution of initialvalue problems
L25: Recapitulate L26: Recapitulate
T13: Recapitulate
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Hand in second assignment Reading week (Sat 17 Nov – Fri 23 Nov) 2-hour examination 11
