3rd Semester
BSc.CSIT Third Semester Syllabus  TU Syllabus
BSc.CSIT Third Semester Syllabus Overview
Syllabus of BSc.CSIT Third Semester comprises five compulsory courses that include Data Structures and Algorithm, Numerical Method, Computer Architecture, Computer Graphics, and Statistics II. They are a total of 15 credit hours with a total of 500 full marks.
BSc.CSIT Third Semester course code is shown below in table:
SN  Course Code  Course Title  Credit Hrs.  Full Marks 
1  CSC206  Data Structure and Algorithm  3  100 
2  CSC207  Numerical Method  3  100 
3  CSC208  Computer Architecture  3  100 
4  CSC209  Computer Graphics  3  100 
5  STA210  Statistics II  3  100 
Total  15  500 
 BSc.CSIT Third Semester Syllabus Overview
 Course Title 1: Data Structures and Algorithm
 Course Title 2: Numerical Method
 Unit 1: Solution of Nonlinear Equations (8 Hrs.)
 Unit 2: Interpolation and Regression (8 Hrs.)
 Unit 3: Numerical Differentiation and Integration (8 Hrs.)
 Unit 4: Solving System of Linear Equations (8 Hrs.)
 Unit 5: Solution of Ordinary Differential Equations (8 Hrs.)
 Unit 6: Solution of Partial Differential Equations (5 Hrs.)
 Laboratory Works:
 Reference Books:
 Course Title 3: Computer Architecture
 Unit 2: Register Transfer and Microoperations (5 Hrs.)
 Unit 3: Basic Computer Organization and Design (8 Hrs.)
 Unit 4: Microprogrammed Control(4 Hrs.)
 Unit 5: Central Processing Unit (4 Hrs.)
 Unit 6: Pipelining (6 Hrs.)
 Unit 7: Computer Arithmetic (6 Hrs.)
 Unit 8: Input Output Organization (4 Hrs.)
 Unit 9: Memory Organization (4 Hrs.)
 Laboratory Works:
 Course Title 4: Computer Graphics
 Unit 1: Introduction of Computer Graphics (3 Hrs.)
 Unit 2: Scan Conversion Algorithm (6 Hrs.)
 Unit 3: TwoDimensional Geometric Transformations (5 Hrs.)
 Unit 4: ThreeDimensional Geometric Transformation (5 Hrs.)
 Unit 5: 3D Objects Representation (7 Hrs.)
 Unit 6: Solid Modeling (4 Hrs.)
 Unit 7: Visible Surface Detections (5 Hrs.)
 Unit 8: Illumination Models and Surface Rendering Techniques (5 Hrs.)
 Unit 9: Introduction to Virtual Reality (2 Hrs.)
 Unit 10: Introduction to OpenGL (3 Hrs.)
 Laboratory Works:
 Reference Books:
 Course Title: Statistics II
Course Title 1: Data Structures and Algorithm
Full Marks: 60 + 20 + 20
Course No: CSC206
Pass Marks: 24 + 8 + 8
Nature of the Course: Theory + Lab
Credit Hrs: 3
Semester: III
Course Description: This course includes the basic foundations in of data structures and algorithms. This course covers concepts of various data structures like stack, queue, list, tree and graph. Additionally, the course includes idea of sorting and searching.
Course Objectives:
 To introduce data abstraction and data representation in memory
 To describe, design and use of elementary data structures such as stack, queue, linked list, tree and graph
 To discuss decomposition of complex programming problems into manageable subproblems
 To introduce algorithms and their complexity
Course Contents:
Unit 1: Introduction to Data Structures & Algorithms (4 Hrs.)
 Data types, Data structure and Abstract date type
 Dynamic memory allocation in C
 Introduction to Algorithms
 Asymptotic notations and common functions
Unit 2: Stack (4 Hrs.)
 Basic Concept of Stack, Stack as an ADT, Stack Operations, Stack Applications
 Conversion from infix to postfix/prefix expression, Evaluation of postfix/ prefix expressions
Unit 3: Queue (4 Hrs.)
 Basic Concept of Queue, Queue as an ADT, Primitive Operations in Queue
 Linear Queue, Circular Queue, Priority Queue, Queue Applications
Unit 4: Recursion (3 Hrs.)
 Principle of Recursion, Comparison between Recursion and Iteration, Tail Recursion
 Factorial, Fibonacci Sequence, GCD, Tower of Hanoi(TOH)
 Applications and Efficiency of Recursion
Unit 5: Lists (8 Hrs.)
 Basic Concept, List and ADT, Array Implementation of Lists, Linked List
 Types of Linked List: Singly Linked List, Doubly Linked List, Circular Linked List.
 Basic operations in Linked List: Node Creation, Node Insertion and Deletion from Beginning, End and Specified Position
 Stack and Queue as Linked List
Unit 6: Sorting (8 Hrs.)
 Introduction and Types of sorting: Internal and External sort
 Comparison Sorting Algorithms: Bubble, Selection and Insertion Sort, Shell Sort
 Divide and Conquer Sorting: Merge, Quick and Heap Sort
 Efficiency of Sorting Algorithms
Unit 7: Searching and Hashing (6 Hrs.)
 Introduction to Searching, Search Algorithms: Sequential Search, Binary Search
 Efficiency of Search Algorithms
 Hashing : Hash Function and Hash Tables, Collision Resolution Techniques
Unit 8: Trees and Graphs (8 Hrs.)
 Concept and Definitions, Basic Operations in Binary Tree, Tree Height, Level and Depth
 Binary Search Tree, Insertion, Deletion, Traversals, Search in BST
 AVL tree and Balancing algorithm, Applications of Trees
 Definition and Representation of Graphs, Graph Traversal, Minimum Spanning Trees: Kruskal and Prims Algorithm
 Shortest Path Algorithms: Dijksrtra Algorithm
Laboratory Works:
The laboratory work consists of implementing the algorithms and data structures studied in the course. Student should implement at least following concepts;
 Dynamic memory allocation and deallocation strategies
 Stack operations and Queue operations
 Array and Linked List implementation of List
 Linked List implementation of Stack and Queues
 Sorting, Searching and Hashing algorithms
 Binary Search Trees and AVL Trees
 Graph Representation, Spanning Tree, and Shortest Path Algorithms
Text Books:
1.Y Langsam , MJ Augenstein and A.M , Tanenbaum Data Structures using C and C++ , Prentice Hall India, Second Edition 2015
Reference Books:
 Leen Ammeral, Programmes and Data Structures in C, Wiley Professional Computting
 G.W Rowe, Introduction to Data Structure and Algroithms with C and C++ , prentice Hall India
 R.L Kruse, B.P. Leung, C.L. Tondo, Data Structure and Program Design in C PrenticeHall India
Course Title 2: Numerical Method
Full Marks: 60 + 20 + 20
Course No.: CSC207
Pass Marks: 24 + 8 + 8
Nature of the Course: Theory + Lab
Credit Hrs: 3
Semester: III
Course Description: This course contains the concepts of numerical method techniques for solving linear and nonlinear equations, interpolation and regression, differentiation and integration, and partial differential equations.
Course Objectives: The main objective of the course is to provide the knowledge of numerical method techniques for mathematical modeling.
Course Content:
Unit 1: Solution of Nonlinear Equations (8 Hrs.)
1.1Errors in Numerical Calculations, Sources of Errors, Propagation of Errors, Review of Taylor’s Theorem
1.2Solving Nonlinear Equations by Trial and Error method, HalfInterval method and Convergence, Newton’s method and Convergence, Secant method and Convergence, Fixed point iteration and its convergence, Newton’s method for calculating multiple roots, Horner’s method
Unit 2: Interpolation and Regression (8 Hrs.)
2.1Interpolation vs Extrapolation, Lagrange’s Interpolation, Newton’s Interpolation using divided differences, forward differences and backward differences, Cubic spline interpolation
2.2Introduction of Regression, Regression vs Interpolation, Least squares method, Linear Regression, Nonlinear Regression by fitting Exponential and Polynomial
Unit 3: Numerical Differentiation and Integration (8 Hrs.)
3.1Differentiating Continuous Functions (TwoPoint and ThreePoint Formula), Differentiating Tabulated Functions by using Newton’s Differences, Maxima and minima of Tabulated Functions
3.2NewtonCote’s Quadrature Formulas, Trapezoidal rule, MultiSegment Trapezoidal rule, Simpson’s 1/3 rule, MultiSegment Simpson’s 1/3 rule, Simpson’s 3/8 rule, MultiSegment Simpson’s 3/8 rule, Gaussian integration algorithm, Romberg integration
Unit 4: Solving System of Linear Equations (8 Hrs.)
4.1Review of the existence of solutions and properties of matrices, Gaussian elimination method, pivoting, GaussJordan method, Inverse of matrix using GaussJordan method
4.2Matrix factorization and Solving System of Linear Equations by using Dolittle and Cholesky’s algorithm
4.3Iterative Solutions of System of Linear Equations, Jacobi Iteration Method, GaussSeidal Method
4.4Eigen values and eigen vectors problems, Solving eigen value problems using power method.
Unit 5: Solution of Ordinary Differential Equations (8 Hrs.)
5.1Review of differential equations, Initial value problem, Taylor series method, Picard’s method, Euler’s method and its accuracy, Heun’s method, RungeKutta methods
5.2Solving System of ordinary differential equations, Solution of the higher order equations, Boundary value problems, Shooting method and its algorithm
Unit 6: Solution of Partial Differential Equations (5 Hrs.)
6.1Review of partial differential equations, Classification of partial differential equation, Deriving difference equations, Laplacian equation and Poisson’s equation, engineering examples
Laboratory Works:
The laboratory exercise should consist program development and testing of nonlinear equations, system of linear equations, interpolation, numerical integration and differentation, linear algebraic equations, ordinary and partial differential equations.Numerical solutions using C or Matlab.
Text Books:
 W. Chency and D. Kincaid, “Numerical Mathematics and Computing“, 7^{th}Edition, Brooks/Cole Publishing Co, 2012
 C.F. Gerald and P.O. Wheatley, “Applied Numerical Analysis“, 9^{th}Edition, Addison Wesley Publishing Company, New York, 2011
Reference Books:
 E. Balagurusamy, “Numerical Methods”, Tata McGrawHill Publishing Company Ltd., New Delhi, 1999
 W.H. Press, B.P. Flannery et al., “Numerical Recipes: Art of Scientific Computing“, 3^{rd} Edition, Cambridge Press, 2007.
 J. M. Mathews and K. Fink, “Numerical Methods using MATLAB “, 4^{rd} Edition, Prentice Hall Publication, 2004
Course Title 3: Computer Architecture
Full Marks: 60 + 20 + 20
Course No: CSC208
Pass Marks: 24 + 8 + 8
Nature of the Course: Theory + Lab
Credit Hrs: 3
Semester: III
Course Description: This course includes concepts of instruction set architecture, organization or microarchitecture, and system architecture. The instruction set architecture includes programmer’s abstraction of computer. The microarchitecture consist internal representation of computers at register and functional unit level. The system architecture includes organization of computers at the cache and bus level.
.
Course Objectives:
 Discuss representation of data and algorithms used to perform operations on data
 Demonstrate different operations in terms of Microoperations
 Explain architecture of basic computer and microprogrammed control unit
 Understand and memory and I/O organization of a typical computer system
 Demonstrate the benefits of pipelined systems Course Contents: Unit 1: Data Representation (4 Hrs.)
 Data Representation: Binary Representation, BCD, Alphanumeric Representation, Complements, Fixed Point representation, Representing Negative Numbers, Floating Point Representation, Arithmetic with Complements, Overflow, Detecting Overflow
 Other Binary Codes: Gray Code, self Complementing Code, Weighted Code, Excess3 Code, EBCDIC
 Error Detection Codes: Parity Bit, Odd Parity, Even parity, Parity Generator & Checker
Unit 2: Register Transfer and Microoperations (5 Hrs.)
 Microoperation, Register Transfer Language, Register Transfer, Control Function
 Arithmetic Microoperations: Binary Adder, Binary Addersubtractor, Binary Incrementer, Arithmetic Circuit
 Logic Microoperations, Hardware Implementation, Applications of Logic Microoperations.
 Shift Microoperations: Logical Shift, Circular shift, Arithmetic Shift, Hardware Implementation of Shifter.
Unit 3: Basic Computer Organization and Design (8 Hrs.)
 Instruction Code, Operation Code, Stored Program Concept
 Registers and memory of Basic Computer, Common Bus System for Basic Computer.
 Instruction Format, Instruction Set Completeness, Control Unit of Basic Computer, Control Timing Signals
 Instruction Cycle of Basic computer, Determining Type of Instruction, Memory Reference Instructions, InputOutput Instructions, Program Interrupt & Interrupt Cycle.
 Description and Flowchart of Basic Computer
Unit 4: Microprogrammed Control(4 Hrs.)
 Control Word, Microprogram, Control Memory, Control Address Register, Sequencer
 Address Sequencing, Conditional Branch, Mapping of Instructions, Subroutines, Microinstruction Format, Symbolic Microinstructions
 Design of Control Unit
Unit 5: Central Processing Unit (4 Hrs.)
 Major Components of CPU, CPU Organization
 Instruction Formats, Addressing Modes, Data Transfer and manipulation, Program Control, Subroutine Call and Return, Types of Interrupt
 RISC vs CISC, Pros and Cons of RISC and CISC, Overlapped Register Windows
Unit 6: Pipelining (6 Hrs.)
 Parallel Processing, Multiple Functional Units, Flynn’s Classification
 Pipelining: Concept and Demonstration with Example, Speedup Equation, Floating Point addition and Subtraction with Pipelining
 Instruction Level Pipelining: Instruction Cycle, Three & FourSegment Instruction Pipeline, Pipeline Conflicts and Solutions
 Vector Processing, Applications, Vector Operations, Matrix Multiplication
Unit 7: Computer Arithmetic (6 Hrs.)
 Addition and Subtraction with Signed Magnitude Data, Addition and Subtraction with Signed 2’s Complement Data
 Multiplication of Signed Magnitude Data, Booth Multiplication, Division of Signed magnitude Data, Divide Overflow
Unit 8: Input Output Organization (4 Hrs.)
 InputOutput Interface: I/O Bus and Interface Modules, I/O vs. Memory Bus, Isolated vs. MemoryMapped I/O
 Asynchronous Data Transfer: Strobe, Handshaking
 Modes of Transfer: Programmed I/O, InterruptInitiated I/O, Direct memory Access
 Priority Interrupt: Polling, DaisyChaining, Parallel Priority Interrupt
 Direct Memory Access, InputOutput Processor, DMA vs. IOP
Unit 9: Memory Organization (4 Hrs.)
9.1Memory Hierarchy, Main Memory, RAM and ROM Chips, Memory address Map, Memory Connection to CPU, Auxiliary Memory (magnetic Disk, Magnetic Tape)
9.1Associative Memory: Hardware Organization, Match Logic, Read Operation, Write Operation
9.1Cache Memory: Locality of Reference, Hit & Miss Ratio, Mapping, Write Policies
Laboratory Works:
The laboratory work includes implementing and simulating the algorithms, studied in the course, by using high level languages like C or VHDL. The laboratory works should include at least following concepts;
 Simulate features like overflow, data representation by using VHDL
 Simulate design of different units by using VHDL
 Simulate pipelining by using VHDL
Implement algorithms for computer arithmetic using a highlevel languages like C or C++
Text Books:
1.M. Morris Mano, “Computer System Architecture”, PrenticeHall of India, Pvt. Ltd., Third edition, 2007
References Books:
 William Stallings, “Computer Organization and Architecture”, PrenticeHall of India, Pvt. Ltd., Seventh edition, 2005.
 Vincent P. Heuring and Harry F. Jordan, “Computer System Design and Architecture”, PrenticeHall of India, Pvt. Ltd., Second edition, 2003.
Course Title 4: Computer Graphics
Full Marks: 60 + 20 + 20
Course no: CSC209
Pass Marks: 24 + 8 + 8
Nature of the Course: Theory + Lab
Credit Hrs: 3
Semester: III
Course Description: The course covers concepts of graphics hardware, software, and applications, data structures for representing 2D and 3D geometric objects, drawing algorithms for graphical objects, techniques for representing and manipulating geometric objects, illumination and lighting models, and concept of virtual reality.
Course Objectives: The objective of this course is to understand the theoretical foundation as well as the practical applications of 2D and 3D graphics.
Course Contents:
Unit 1: Introduction of Computer Graphics (3 Hrs.)
1.1A Brief Overview of Computer Graphics, Areas of Applications.
1.2Graphics Hardware: Display Technology, Architecture of RasterScan
Displays, Vector Displays, Display Processors, Hard copy device. Input Devices.
1.3Graphics Software: Software standards, Need of machine independent graphics language.
Unit 2: Scan Conversion Algorithm (6 Hrs.)
2.1Scan Converting a Point and a straight Line: DDA Line Algorithm, Bresenham’s Line Algorithm
2.2Scan Converting Circle and Ellipse :Mid Point Circle and Ellipse Algorithm
2.3Area Filling: Scan Line Polygon fill Algorithm, Insideoutside Test, Scan line fill of Curved Boundary area, Boundaryfill and Floodfill algorithm
Unit 3: TwoDimensional Geometric Transformations (5 Hrs.)
3.1 TwoDimensional translation, Rotation, Scaling, Reflection and Shearing
3.2 Homogeneous Coordinate and 2D Composite Transformations. Transformation between Coordinate Systems.
3.3Two Dimensional Viewing: Viewing pipeline, Window to viewport coordinate transformation
3.4Clipping: Point, Lines(Cohen Sutherland line clipping, LiangBarsky Line Clipping) , Polygon Clipping(Sutherland Hodgeman polygon clipping)
Unit 4: ThreeDimensional Geometric Transformation (5 Hrs.)
4.1ThreeDimensional translation, Rotation, Scaling, Reflection and Shearing
4.2ThreeDimensional Composite Transformations
4.3ThreeDimensional Viewing: Viewing pipeline, world to screen viewing transformation, Projection concepts(Orthographic, parallel, perspective projections)
Unit 5: 3D Objects Representation (7 Hrs.)
5.1Representing Surfaces: Boundary and Space partitioning
5.1.1Polygon Surface: Polygon tables , Surface normal and Spatial orientation of surfaces, Plane equations, Polygon meshes
5.1.2Wireframe Representation
5.1.3Blobby Objects
5.2Representing Curves: Parametric Cubic Curves, Spline Representation, Cubic spline interpolation, Hermite Curves, Bezier and Bspline Curve and surface
5.3Quadric Surface: Sphere and Ellipsoid
Unit 6: Solid Modeling (4 Hrs.)
6.1Sweep ,Boundary and SpatialPartitioning Representation
6.2Binary Space Partition Trees (BSP)
6.3Octree Representation
Unit 7: Visible Surface Detections (5 Hrs.)
7.1Image Space and Object Space Techniques
7.2Back Face Detection, Depth Buffer (Zbuffer), ABuffer and ScanLine Algorithms.
7.3Depth Sorting Method (Painter’s Algorithm)
7.4BSP tree Method, Octree and Ray Tracing
Unit 8: Illumination Models and Surface Rendering Techniques (5 Hrs.)
8.1Basic Illumination Models: Ambient light, Diffuse reflection, Specular reflection and Phong model
8.2Intensity attenuation and Color consideration ,Transparency, Shadows
8.3Polygon Rendering Methods : Constant intensity shading, Gouraud shading , Phong Shading and Fast Phong Shading
Unit 9: Introduction to Virtual Reality (2 Hrs.)
9.1Concept of Virtual reality
9.2Virtual Reality Components of VR System, Types of VR System, 3D Position Trackers, Navigation and Manipulation Interfaces
9.3Application of VR
Unit 10: Introduction to OpenGL (3 Hrs.)
1.1Introduction, Callback functions, Color commands, Drawings pixels, lines, polygons using OpenGL, Viewing and Lighting
Laboratory Works:
The laboratory course consists of implementing following algorithms using high level languages and OpenGL.
 DDA Line Algorithm
 Bresenham’s line drawing algorithm
 Mid Point Circle Algorithm
 Mid Point Ellipse Algorithm
 Basic transformation on 2D including Translation, Rotation and Scaling
 Simple 3D Object with basic transformations including Translation, Rotation and Scaling
 Clipping
 Hidden surface removal
 Basic Drawing Techniques in OpenGL
Text Books:
1.Donald Hearne and M. Pauline Baker, “Computer Graphics, C Versions.” Prentice Hall
Reference Books:
 J.D. Foley, S.K. Feiner and J.F. Hughes, “Computer Graphics – Principles and Practises” (Second Edition in C)
 R.K. Maurya, “Computer Graphics with Virtual Reality”, Wiley India
 F.S. Hill, Stephen M.Kelley, “Computer Graphics using Open GL” Prentice Hall
Course Title: Statistics II
Full Marks: 60 + 20 + 20
Course No: STA210
Pass Marks: 24 + 8 + 8
Nature of Course: Theory + Lab
Credit Hrs: 3
Semester: III
Course Description: The course consists of concepts of sampling, testing hypothesis, parametric and non parametric tests, correlation and regression, experimental designs and stochastic processes.
Course Objectives: The main objective of the course is to acquire the theoretical as well as practical knowledge of estimation, testing of hypothesis, application of parametric and nonparametric statistical tests, design of experiments, multiple regression analysis, and basic concept of stochastic process with special focus to data/problems related with computer science and information technology
Course Contents:
Unit 1: Sampling Distribution and Estimation (6 Hrs.)
Sampling distribution; sampling distribution of mean and proportion; Central Limit Theorem; Concept of inferential Statistics;Estimation;Methods of estimation; Properties of good estimator; Determination of sample size; Relationship of sample size with desired level of error
Problems and illustrative examples related to computer Science and IT
Unit 2: Testing of hypothesis (8 Hrs.)
Types of statistical hypotheses; Power of the test, concept of pvalue and use of p value in decision making, steps used in testing of hypothesis, one sample tests for mean of normal population (for known and unknown variance), test for single proportion, test for difference between two means and two proportions, paired sample ttest; Linkage between confidence interval and testing of hypothesis
Problems and illustrative examples related to computer Science and IT
Unit 3: Non parametric test (8 Hrs.)
Parametric vs. nonparametric test; Needs of applying nonparametric tests; Onesample test: Run test, Binomial test, Kolmogorov–Smirnov test; Two independent sample test: Median test,
KolmogorovSmirnov test, Wilcoxon Mann Whitney test, Chisquare test; Pairedsample test:
Wilcoxon signed rank test; Cochran’s Q test; Friedman two way analysis of variance test; Kruskal Wallis test
Problems and illustrative examples related to computer Science and IT
Unit 4: Multiple correlation and regression (6 Hrs.)
Multiple and partial correlation; Introduction of multiple linear regression; Hypothesis testing of multiple regression; Test of significance of regression; Test of individual regression coefficient; Model adequacy tests
Problems and illustrative examples related to computer Science and IT
Unit 5: Design of experiment (10 Hrs.)
Experimental design; Basic principles of experimental designs; Completely Randomized Design (CRD); Randomized Block Design (RBD); ANOVA table, Efficiency of RBD relative to CRD, Estimations of missing value (one observation only), Advantages and disadvantages; Latin Square Design (LSD): Statistical analysis of m × m LSD for one observation per experimental unit, ANOVA table, Estimation of missing value in LSD (one observation only), Efficiency of LSD relative to RBD, Advantage and disadvantages.
Problems and illustrative examples related to computer Science and IT
Unit 6: Stochastic Process (7 Hrs.)
Definition and classification;Markov Process: Markov chain, Matrix approach, Steady State distribution; Counting process: Binomial process, Poisson process; Simulation of stochastic process; Queuing system: Main component of queuing system, Little’s law; Bernoulli single server queuing process: system with limited capacity; M/M/1 system: Evaluating the system performance.
Laboratory Works:
The laboratory work includes implementing concepts of statistics using statistical software tools such as SPSS, STATA etc.
S. No.  Practical problems  No. of practical problems 
1  Sampling distribution, random number generation, and computation of sample size  1 
2  Methods of estimation (including interval estimation)  1 
3  Parametric tests (covering most of the tests)  3 
4  Nonparametric test(covering most of the tests)  3 
5  Partial correlation  1 
6  Multiple regression  1 
7  Design of Experiments  3 
Stochastic process  2  
Total number of practical problems  15 
Text Books:
 Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, & Keying Ye(2012).
Probability & Statistics for Engineers & Scientists. 9^{th} Ed., Printice Hall
 Michael Baron (2013). Probability and Statistics for Computer Scientists. 2^{nd} Ed., CRC Press, Taylor & Francis Group, A Chapman & Hall Book
Reference Books:
 Douglas C. Montgomery & George C. Runger (2003). Applied Statistics and Probability for Engineers. 3^{rd} Ed., John Willey and Sons, Inc.
 Sidney Siegel, & N. John Castellan, Jr. Nonparametric Statistics for the Behavioral Sciences, 2^{nd} Ed., McGraw Hill International Editions.

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