Class Diagrams
Exercises
University
In a university there are different classrooms, offices
and departments. A department has a name and it
contains many offices. A person working at the
university has a unique ID and can be a professor or
an employee.
TMC1414 Introduction to Programming
ASSIGNMENT 1
GROUP 11
Johari Abdullah
johari.abdullah@gmail.com
1 G ENERAL I NFORMATION
Release Date:
15th September 2014.
Mode:
Individual Assignment: You are expected to do your own work on this assignment. You may (a
COURSE PLAN
TMC1414 INTRODUCTION TO PROGRAMMING
FACULTY OF COMPUTER SCIENCE & INFORMATION TECHNOLOGY, UNIVERSITI MALAYSIA SARAWAK
SEMESTER 1 SESSION 2014/2015
GROUP 11 (Johari Abdullah)
Week
1
2
3
4
5
6
Day
Monday
Tuesday
Thursday
Monday
Tuesday
Date
8-Se
Lecture 12 Superclasses, Subclasses, the word super Example: A point (x, y), x = an integer, y = an integer can be considered as a class. A circle with center (x,y) and radius r (x,y,r), x = an integer, y = an integer, r = the radius can be considered as
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groups need to understand where they converge and where the potential for
conflict exists.
Open source development is useful for many applications running on diverse
technologies, including handheld devices an
Input / Output in Java
Input/Output in Java can be done using the keyboard and screen, using files, or some combination of these methods.
Input typed at the keyboard and output displayed on the screen are often referred to as console input/output.
Interac
The UML Class Diagram
Is a static diagram (describes system
structure)
Combines a number of model elements:
Classes
Attributes
Operations (methods)
Associations
Aggregations
Compositions
Generalisations
A UML Class
Name
Attributes
Operations
Properties
http:/math.hws.edu/javanotes/c2/exercises.html
Exercise 2.1:
Write a program that will print your initials to standard output in letters that are nine
lines tall. Each big letter should be made up of a bunch of *'s. For example, if your
initials were "DJE
Modelling with Problem Frames: Explanations
and Context in Ambient Intelligent Systems
Anders Kofod-Petersen1 and Jrg Cassens2
o
1
2
Department of Computer and Information Science,
Norwegian University of Science and Technology,
7491 Trondheim, Norway
and
TMC2413 Object Oriented Software Development
Tutorial 3
Unified Modelling Language
Case study for Question 1 and 2:
A ticket vending machine is a vending machine that sells tickets to commuters. This kind of
machine is a combination of both hardware and s
Object Oriented UML Modeling for ATM Systems
Rajni Pamnani, Pramila Chawan, Satish Salunkhe
Department of computer technology,
VJTI University, Mumbai
Abstract
The Object-Oriented Modeling assists the programmer to
address the complexity of a problem doma
Software Engineering Fundamentals - Tutorial
Class and object diagrams
Q1
Draw a class diagram for the following problems. Include any appropriate attributes, and name the associations.
a)
A hotel has an address and multiple rooms that can be rented.
Draw
Discussion Problems and Solutions 3
1
Section 2.1
Exercise 1: Find the power set of each of these sets, where a and b are distinct
elements.
(a) cfw_a, (b) cfw_a, b, (c) cfw_, cfw_
Solution:
(a) cfw_, cfw_a
(b) cfw_, cfw_a, cfw_b, cfw_a, b
(c) cfw_, cfw_,
Artificial Intelligence I
Matthew Huntbach, Dept of Computer Science, Queen Mary and Westfield College, London,
UK E1 4NS. Email: mmh@dcs.qmw.ac.uk. Notes may be used with the permission of the author.
Notes on Semantic Nets and Frames
Semantic Nets
Seman
Data Flow Diagram Tutorial
Objectives
After completion of study of this unit you should be able to:
Describe the use of data flow diagrams
Produce a data flow diagram from a given case study
including different levels
Distinguish between the different
Section 2.3
Measures of Central Tendency
Larson/Farber 4th ed.
Section 2.3 Objectives
Determine the mean, median, and mode of a
population and of a sample
Determine the weighted mean of a data set and the
mean of a frequency distribution
Describe the s
Section 6.3
Confidence Intervals for Population
Proportions
1
Section 6.3 Objectives
Find a point estimate for the population proportion
Construct a confidence interval for a population
proportion
Determine the minimum sample size required when
estimating
Section 6.2
Confidence Intervals for the Mean (Small
Samples)
1
Section 6.2 Objectives
Interpret the t-distribution and use a t-distribution table
Construct confidence intervals when n <30, the
population is normally distributed, and is unknown
2
The t-Di
Chapter 6
Confidence Intervals
1
Chapter Outline
6.1 Confidence Intervals for the Mean (Large
Samples)
6.2 Confidence Intervals for the Mean (Small
Samples)
6.3 Confidence Intervals for Population Proportions
6.4 Confidence Intervals for Variance and Stan
Section 5.4
Sampling Distributions and the Central Limit
Theorem
1
Section 5.4 Objectives
Find sampling distributions and verify their properties
Interpret the Central Limit Theorem
Apply the Central Limit Theorem to find the
probability of a sample mean
Section 5.5
Normal Approximations to Binomial
Distributions
1
Section 5.5 Objectives
Determine when the normal distribution can
approximate the binomial distribution
Find the correction for continuity
Use the normal distribution to approximate binomial
pr
Section 5.3
Normal Distributions: Finding Values
1
Section 5.3 Objectives
Find a z-score given the area under the normal curve
Transform a z-score to an x-value
Find a specific data value of a normal distribution
given the probability
2
Finding values Giv
Section 5.2
Normal Distributions: Finding Probabilities
1
Section 5.2 Objectives
Find probabilities for normally distributed variables
2
Probability and Normal Distributions
If a random variable x is normally distributed, you can
find the probability that
Chapter 5
Normal Probability Distributions
1
Chapter Outline
5.1 Introduction to Normal Distributions and the
Standard Normal Distribution
5.2 Normal Distributions: Finding Probabilities
5.3 Normal Distributions: Finding Values
5.4 Sampling Distributions
Section 4.2
Binomial Distributions
1
Section 4.2 Objectives
Determine if a probability experiment is a binomial
experiment
Find binomial probabilities using the binomial
probability formula
Find binomial probabilities using technology and a
binomial table
Section 3.4
A dditional Topics in Probability and Counting
1
Section 3.4 Objectives
Determine the number of ways a group of objects can
be arranged in order
Determine the number of ways to choose several
objects from a group without regard to order
Use th