Hypothesis: If we toss the coin(s) for many times, then we will have more chances to reach the prediction that we expect based on the principle of probability. Results:
As for part 1: probability of the occurrence of a single event, the deviation of heads and tails of 20 tosses is zero, which means that the possibility of heads and tails is ten to ten, which means equally chances. The deviation of heads and tails of 30 tosses is 4, which means that the occurrence of head is 19 and the occurrence of tail is 11. The deviation of heads and tails of 50 tosses is 3, which means that the occurrence of head is 28 and the occurrence of tail is 22. Compare the second and third observation, we can find that the deviation decrease one. It is corresponding to the hypothesis. The more times we use to toss the coin, the more opportunities we will get to reach the prediction based on the principle of probability. As for part 2: probability of independent events occurring simultaneously, the observation of Heads-Heads is 11, which is 27.5% of the total experiment. And the deviation is 1. The observation of Heads-Tails or Tail-Heads is 16, which is 40% of the total. And the deviation is 4. The observation of Tails- Tails is 13, which is 32.5% of the total number. And the deviation is 3. Discussion:

In this lab, we’d learn about the likelihood that a particular event will occur is called probability. Every event happens independently. The probability of a coin flip has two possible outcomes: the coin may lands heads up or tails up. The probabilities of either outcome are equal. Therefore, the probability of a single coin flip will come up heads is one chance in two. This is 1/2, or 50 percent. In the first part, I toss a single coin for 20, 30, and 50 times. For the 20 tosses part, the observation is 10 heads-up and 10 tails-up, which is corresponding to the principle of probability: the chances of each situation are equal. For the 30...

...Decision Analysis
Course Outline, Quarter I, 2006
Class Materials Topic
Hardcopy in Packet Other*
Introduction
1 Freemark Abbey Winery Structuring Decisions
Framework for Analyzing Risk
2 The North Star Concert North Star.xls Best Guess, Worst Case, Best Case; and Continuous Uncertainties
3 Engine Services, Inc.
Quick Start Guide to Crystal Ball
Analyzing Uncertainty, Probability Distributions, and Simulation Learning Module: Crystal Ball Litigate Demo
Engine Services.xls Language of Probability Distributions and Monte Carlo Simulation
4 Taurus Telecommunications Corporation: A New Prepaid Phone Card Learning Module: Tornado Sensitivity
Taurus Telecommunications.xls Sensitivity Analysis and Key Drivers
Time Value of Money
5 Dhahran Roads (A)
Evaluating Multiperiod Performance Multiperiod Pro Forma and NPV
6 Roadway Construction Company NPV, IRR, and Project Assumptions
Data and Distributions
7 Appshop, Inc. Simulating NPV
8 Lorex Pharmaceuticals
Introduction to Analytical Probability Distributions Lorex Exhibit 2.xls Distributions
9 Sprigg Lane (A) Sprigg2.xls Probability Distributions and Spreadsheet Modeling; Risk
10 The Waldorf Property
Chapter 11 of QBA: Text and Cases
Waldorf.xls Cumulative Distribution Functions, Adjustment for Risk
11 Amore Frozen Foods (A) Macaroni and Cheese Fill Targets
Sampling Amore.xls Sample Uncertainty
Regression
12 Hightower Department Stores:...

...Computer Lab Rules & Regulations
Electronic workstations may only be used by current Swinburne University students and staffs. Swinburne identity card must be presented on request. Swinburne computing facilities should only be used for educational, research and administrative purposes of Swinburne. All other uses are strictly prohibited.
The following rules and terms apply to all computers on campus.
Terms and Conditions
1. All users must abide by the license requirements of any software or resources being used on the computer workstations.
2. The following activities are strictly prohibited and are deemed as a criminal offence :
o Unless otherwise permitted by the Copyright Act, unauthorized copying, downloading or public performance of works or audiovisual material. This includes MP3, OGG, WMV, AVI, RM, MOV, MPEG files etc.
o Removal and/or of computer workstations including its peripherals such as keyboard, mouse and monitor.
o Causing damage to the computer workstation including its peripherals.
o Altering of software settings and/or intentional introduction of malicious software in order to circumvent network security.
o Installation of pirated and/or unauthorised software on the workstations.
1 Unauthorized and illegal copying of any licensed software.
3. Students must also abide by other rules and regulations shown on booking sheets, computer screens, and notices near the...

...customer and stakeholder satisfaction, protect the brand and reputation, achieve continual improvement, promote innovation, remove barriers to trade, bring clarity to the marketplace. By using a proven management system, this will able to continually renew mission, strategies, operations and service levels. (http://www.bsigroup.com/en/Assesment-and-Certification-services/management-system/at-a-glance/What-are-Management-Systems/).
SPEED COMPUTER COLLEGE is a computer school belongs to tertiary level, duly recognized both by the Commission and Higher Education (CHED) and TESDA. Back in 2010 our institution enrollment succeeded to a percent of one hundred and fifty. Our computer facilities intends only into a very limited units so that our Lab supervisor easily maintain and create schedules for students who has a computer subjects.
By this time, Speed Computer College again increased by staggering a percent of two hundred enrollees.
One of the main problems of Speed Computer College is that the laboratories do not have enough assistant. All of the assistants are students, which makes it difficult to find staffing for early and very late hours. And another is no budget to hire assistants who are not students. Computers also break down, which creates a limited number of functioning machines. Fixing machines with hardware failures consume atleast 24 hours. In the event of software difficulties, assistants are not trained to fix many of them. In addition to...

...Learning Programmes Division
Second Semester 2010-2011
Course Handout
Course Number
Course Title
: AAOC ZC111
: Probability and Statistics
Course E-mail address : aaoczc111@dlpd.bits-pilani.ac.in
Course Description
Probability spaces; conditional probability and independence; random variables and probability
distributions; marginal and conditional distributions; independent random variables, mathematical
exceptions, mean and variance, Binomial Poisson and normal distribution; sum of independent random
variables; law of large numbers; central limit theorem; sampling distributions; tests for mean using normal
and student’s distributions; tests of hypotheses; correlation and linear regression.
Scope and Objectives
At the end of the course, the student should be able to understand probabilistic & deterministic models and
statistical inference and apply these concepts to solve a variety of problems.
Prescribed Text Book
T1
Johnson Richard A. & C.B. Gupta, Miller & Freund’s Probability and Statistics for Engineers, PHI,
7th Ed., 2005.
Reference Books
R1.
Paul L. Meyer, Introductory Probability and Statistical Appl., Second Edition. Addison-Wesley, 1970.
R2. M.S. Radhakrishnan, Probability & Statistics, DLPD Notes Note: Softcopy of this Supplementary notes will
be available for download from BITS DLP website.
R3. Mendenhall Beaver Beaver, Introduction to...

...Campus Computer Labs: The Issue At Hand
As a student at Prince Georges Community College I like to take full advantage of all the resources available to me since at the end of the day I am paying for them. This sometimes includes staying a few hours on campus and doing my work instead of waiting until I get home to do it. One major problem that I encounter on a daily basis on the PGCC campus is the campus computer labs. I can never walk into computerlab without leaving out due to it being over crowded, little or no staff being around to help, or computers not working. As a PGCC student I believe this is a huge issue that really needs to be taken more seriously.
On a daily basis I encounter a number of issues on the PGCC campus but the one that affects me the most is the problem with the computer labs. When I know I have to complete an assignment on campus I get an instant headache. Not because I have to do the work but because I know the hassle I have to go through to find a computer lab that isn’t overcrowded and actually has computers that are working. Ashlee Davis, a current student here at PGCC also agrees that the computer labs here on the campus are a big issue. She goes on to say that, “When it comes time to use the campus computer labs she always find herself going to one lab and leaving or either waiting close to 15 minutes for an available...

...of observations, which gives each observation equal weight, the mean of a random variable weights each outcome xi according to its probability, pi. The mean also of a random variable provides the long-run average of the variable, or the expected average outcome over many observations.The common symbol for the mean (also known as the expected value of X) is , formally defined by
Variance - The variance of a discrete random variable X measures the spread, or variability, of the distribution, and is defined by
The standard deviation is the square root of the variance.
Expectation - The expected value (or mean) of X, where X is a discrete random variable, is a weighted average of the possible values that X can take, each value being weighted according to the probability of that event occurring. The expected value of X is usually written as E(X) or m.
E(X) = S x P(X = x)
So the expected value is the sum of: [(each of the possible outcomes) × (the probability of the outcome occurring)].In more concrete terms, the expectation is what you would expect the outcome of an experiment to be on average.
2. Define the following;
a) Binomial Distribution - is the discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields success with probability p. Therewith the probability of an event is defined by its binomial...

...COMPUTER LABS MONITORING SYSTEM
CASE STUDY: NATIONAL UNIVERSITY OF RWANDA ICT CENTER/Helpdesk Unit
Guider: Mr. Ratnakar Kitnana
Done by: NTEGEREJIMANA Fidèle
RegNo: 10101528
Done at HUYE, February 26th 2013
CHAPTER 1 GENERAL INTRODUCTION
1. 0. Introduction
The computer is the principal tool used in today’s technology. It is being widely used all over the world and has a major impact in our daily life. People use computers in many ways such as keep track of products; it helps teachers keep track of lessons and grades. They help students do research and learn. Computers let you be connected to networks. They let you hook up to a worldwide network called the Internet.
A computer laboratory also name computer lab is a cluster of computers that usually are networked and available for...

...CHAPTER 3: PROBABILITY DISTRIBUTION
3.1
RANDOM VARIABLES AND PROBABILITY DISTRIBUTION
Random variables is a quantity resulting from an experiment that, by chance, can assume different values. Examples of random variables are the number of defective light bulbs produced during the week and the heights of the students is a class. Two types of random variables are discrete random variables and continuous random variable.
3.2
DISCRETE RANDOM VARIABLE
A random variable is called a discrete random variable if its set of posibble outcomes is countable. Probability distribution is a listing of all the outcomes of an experiment and the probability associated with each outcome. For example, the probability distribution of rolling a die once is as below: Outcome, x Probability, P(x) 1 1 6 2 1 6 3 1 6 4 1 6 5 1 6 6 1 6
The probability distribution for P(x) for a discrete random variable must satisfy two properties: 1. The values for the probabilities must be from 0 to 1; 0 ≤ ( ) ≤ 1 2. The sum for P(x) must be equal to 1; ∑ ( ) = 1
QMT200
3.2.1 FINDING MEAN AND VARIANCE Mean of X is also referred to as its “expected value”.
= ( ) Where: = ∑[ ( )]
( )=
= (
) − [ ( )]
(
)=
[
( )] = ( )
Example 1 An experiment consists of tossing two coins simultaneously. Write down the sample space. If X is the number of tails observed,...