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Philadelphia University
Faculty of Science
Department of Basic Science and Mathematics
First semester, 2016/2017
Course Syllabus
Course Title: Elementary Probability and Statistics
Course Level: First year
Course code: 0250231
Course prerequisite (s) : none
Lecture Time: 08:10-09:00 (S-T-T)
Credit hours: 3
12:10-1:00 (S-T-T)
Academic Staff
Specifics
Name
Rank
Office Number
and Location
Office Hours
E-mail Address
9 - 10
Ameina Al- Taani
lecturer
1016, Science
faculty building
(Sun-Tue-Thu)
10 – 11
[email protected]
(Mon-Wed)
Course module description:
This is an introductory course in statistics. The course is planned so that students learn the basic concepts
needed in probability theory and statistics. It familiarizes students with statistical terms such as population,
sample, sample size, random variable, mean, variance, and much more. The course covers materials such as
collecting data, graphical methods, descriptive statistics, regression and correlation and probability basics.
Course module objectives:
This module aims to:

Collect data

Present data using various graphical methods

Calculate and interpret numerical summaries

Use and apply laws of probability and learn how these laws are used in statistical inference

Use the concepts of sampling distributions and learn how it applies in making statistical
inferences be based on sample of data

Be familiar with some important discrete and continuous distributions

Make appropriate use of statistical inference
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Textbook
Title: Introduction to Statistics
Author: Jaffar S. Almousawi
Publisher: Dar Albarraka for Publishing
Additional Books
 Richard A. Johnson, Statistics: Principles and Methods, 6th Edition, John Wiley and Sons,Inc. 2010
Teaching methods: Lectures and problem solving.
Duration: 15 weeks, 45 hours in total.
Lectures: 45 hours, 3 per week .
Learning outcomes:
 Knowledge and understanding
--The student will have the knowledge and understanding of how to apply statistical concepts into real
world problems.
-- The course serves as a prerequisite to other statistics courses such as probability theory and mathematical
statistics.


Cognitive skills (thinking and analysis).
The student will be taught how to think statistically. In other words, the course assists the student in
the understanding and application of many statistical methods and how to analyze real world data.
Communication skills (personal and academic)
-- Be able to work effectively alone or as a member of small group working in some task.
-- Encourage the student to be self-starters and to finish the problems properly.
-- Improve performance of students through the interaction with each other in solving
different problems.
Assessment instruments
Short reports and/ or presentations, and/ or Short research projects
Quizzes.
Home works.
Final examination: 40 marks.
Allocation of Marks
Assessment Instruments
Mark
First examination
20%
Second examination
20%
Final examination: 40 marks
40%
Reports, research projects, Quizzes, Home
20%
works, Projects
Total
100%
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Documentation and academic honesty
Documentation style (with illustrative examples)
Students should note that the material covered in the course is all found in the text
book. If a student would like to document any material written on the blackboard,
they must be aware of making mistakes.
Protection by copyright
When a student document any material related to this course or to any other course, he/she
Must refer to the reference
Avoiding plagiarism.
Students must abide by the highest standards of academic integrity. Any form of academic
dishonesty will result in a "zero" for that particular assignment or a"zero" for the course, at
the instructors discretion. Any student who plagiarizes, cheats on exams, or otherwise behaves
in a dishonest way may be reported to the university administration for further disciplinary action
as specified in the University Regulations Manual.
Course/module academic calendar
week
(1)
Basic and support material to be covered

Introduction Statistics what is it?
Introduction and Data Collection. Types of Data and Their Sources. Some
Important Definitions Population, Sample, Parameter, statistic, Descriptive
statistics, And Inferential Statistics
(2, 3)

Data and data organizing:
Presenting Data in Tables and Charts, Organizing Numerical Data, The
Ordered Array and Stem-Leaf Display, Tabulating and Graphing Univariate
Numerical Data, Frequency Distributions: Tables, Histograms
(4, 5, 6)
(7)
First
Exam
(8, 9,
10)
(11)
Second
exam
(12, 13)

Summarizing data numerically:
Numerical Descriptive Measures, Measures of Central Tendency,
Quartiles, Measures of Variation, Shape
 Simple Linear Correlation and Regression
Converting Simple Linear Correlation and Regression, The Scatterplot, The
Least-Squares Equation, Slope of a Line, Intercept

Probability concepts and Distributions:
Basic Probability, Sample spaces and events, Simple Probability, Joint
Probability, Conditional Probability, Statistical independence, Counting Rules

Discrete Probability Distributions:
Some Important Discrete Probability Distributions. The Probability of a
Discrete Random Variable, Binomial Distribution

The Normal Probability Distribution
The Normal Distribution, The Standardized Normal Distribution
(14)

Sampling Distributions
Sampling Distributions, Sampling Distribution of the Mean, The Central Limit
Theorem
(15)
Final
Exam

Review for the all chapters
Homework,
Reports and their
due dates
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Expected workload:
On average students need to spend 3 hours of study and preparation for each 50-minute lecture.
Attendance policy:
Attendance is expected of every student. Being absent is not an excuse for not knowing about any
important information that may have been given in class. Under the University’s regulations, a student
whose absence record exceeds 15% of total class hours will automatically fail the course. Students who
in any way disrupt the class will be expelled from the classroom and will not be allowed to return until
the problem has been resolved.