Module descriptor dr. mehdi salih abdulqader

1. Ministry of Higher Education & Scientific Research
Directorate of Private Studies
GASHA private technical institute
‫زانستى‬ ‫ذينةوةى‬َ‫ي‬‫تو‬ ‫باآلء‬ ‫ندنى‬َ‫ي‬‫خو‬ ‫وةزارةتى‬
‫ندنى‬َ‫ي‬‫خو‬ ‫وةبةرايةتى‬َ‫ي‬ ِ‫بةر‬‫تايبةت‬
‫تايبةت‬ ‫طةشةى‬ ‫تةكنيكى‬ ‫ثةيمانطةى‬
Quality Assurance ‫جؤرى‬ ‫نيايى‬َ‫ل‬‫د‬
Module Descriptor
2018-2019
Department Accounting
Module Title Principle of statistics
Code - Module Level First year
Credit Hours 3 Effective From 1/11/2018
Theoretical
Hours/Week
1 Practical
hours/Week
2
Instructor’s Name Dr. Mehdi Salih Abdulqader
Academic Title Lecturer Degree - Specialty Ph.D.
Math. Stat.
Email mehdi-salih@hotmail.com Phone Number 07706622212
Assistant’s Name _
Academic Title Degree - Specialty
Email Phone Number
Module Instances
Location Module Leader Date of Approval Signature
Pre-requisites None Co-requisites None
Learning and teaching strategies:
Aims:
This course introduces students to the basic concepts and logic of statistical reasoning and gives the
students introductory-level practical ability to choose, generate, and properly interpret appropriate
descriptive and inferential methods. In addition, the course helps students gain an appreciation for
the diverse applications of statistics and its relevance to their lives and fields of study. The course
assume that the students have some prior knowledge in statistics and its only prerequisite is basic
algebra.
apply and interpret correlation and modeling techniques for bivariate data in context of simple
linear models
so,
1. Statistical literacy is being able to read and use basic statistical language and graphical

2. Ministry of Higher Education & Scientific Research
Directorate of Private Studies
GASHA private technical institute
‫زانستى‬ ‫ذينةوةى‬َ‫ي‬‫تو‬ ‫باآلء‬ ‫ندنى‬َ‫ي‬‫خو‬ ‫وةزارةتى‬
‫ندنى‬َ‫ي‬‫خو‬ ‫وةبةرايةتى‬َ‫ي‬ ِ‫بةر‬‫تايبةت‬
‫تايبةت‬ ‫طةشةى‬ ‫تةكنيكى‬ ‫ثةيمانطةى‬
Quality Assurance ‫جؤرى‬ ‫نيايى‬َ‫ل‬‫د‬
representations to understand statistical information in the media and in daily life.
2. Statistical reasoning is being able to reason about and connect different statistical concepts
and ideas, such as knowing how and why outliers affect statistical measures of center and
variability.
3. Statistical thinking is the type of thinking used by statisticians when they encounter a statistical
problem. This involves thinking about the nature and quality of the data and, where the data
came from, choosing appropriate analyses and models, and interpreting the results in the
context of the problem and given the constraints of the data.
Learning Outcomes:
After successfully completion of the course students should be able to demonstrate:
1. Define statistics and how they lead to better business decisions
2. Analyze different types of data, apply appropriate statistical techniques to organize, summarize
and display data in a meaningful way.
3. Compute various measures of center, variation and location of data
4. Apply some basic probability rules
5. Understand the concepts of probability distributions and random variables then apply these
concepts to real life situations and evaluate the conclusions
6. Use appropriate probability distribution to estimate and perform a hypothesis testing about
parameters of one population
7. estimate and perform hypothesis testing of a mean of a population
8. Perform and interpret a linear correlation and regression to analyze the relationships in bivariate
data
Contents:
A. The meaning statistics and why statistics some concepts and terms of statistics
1. Variables and type of data
2. Types of statistics
3. Data and types of data
4. Level of measures
5. Data collection and sampling techniques
B. Frequency Distributions and Graphs
1. Introduction
2. Organizing Data
3. Histograms, and Ogives
4. Other Types of Graphs
C. Data Description
1. Measures of central Tendency (Mean, Median and Mode)
2. Measures of Variation ( Range, Variance and Standard Deviation)
3. Measure of Position (Quartiles)
4. Exploratory Data Analysis

3. Ministry of Higher Education & Scientific Research
Directorate of Private Studies
GASHA private technical institute
‫زانستى‬ ‫ذينةوةى‬َ‫ي‬‫تو‬ ‫باآلء‬ ‫ندنى‬َ‫ي‬‫خو‬ ‫وةزارةتى‬
‫ندنى‬َ‫ي‬‫خو‬ ‫وةبةرايةتى‬َ‫ي‬ ِ‫بةر‬‫تايبةت‬
‫تايبةت‬ ‫طةشةى‬ ‫تةكنيكى‬ ‫ثةيمانطةى‬
Quality Assurance ‫جؤرى‬ ‫نيايى‬َ‫ل‬‫د‬
D. Correlation and Regression
1. Scatter Plots
2. Correlation analysis
3. The significance of the correlation Coefficient
E. Linear Regression
F. Probability and Counting Rules
Sample Space and Probability
Rules of Probability
G. Discrete and continuous Probability distributions
H. Normal Distribution and its applications
Assessments:
The weight (%) of each assessment must be predetermined and the total = 100
 Exams two exam one in each semester (30%) and Final (50%)
 Quizzes Minimum 2/semester or 4/year (5%)
 Assignments (X) (5%)
 Presentations (5%)
 Attendance (5%)
Feedbacks Specify the methods to be used with the distribution of marks by
percentage (%): exams (mid-term), assignment, quizzes, presentation,
participation, attendance, lab assignments, and field trips.
1) Two exam one in each semester each (15%)
2) Quizzes at least four quizzes (5%)
3) Assignments through the academic year (5%)
4) Students will be asked for presentation some material (5%)
5) Attendance (5%)
6) Final Exam (50%)
Reading list Divide into required and recommended reading to include :
Author, Year, Title, Place of Publication, Publisher
 Elementary Statistics – A Step by Step Approach, By Allan G.
Bluman. McGraw-Hill Publishing
 Fundamentals of Statistics, By: Michael Sullivan III. Published by
Pearson – Prentice Hall
 Basic Statistics for Business & Economics, by McClave, Benson, &
Sincich, published by Prentice Hill.
 Basic Statistics for Business & Economics, by D. A. Lind, W. G.
Marchal, S.A. Wathen, ninth edition, published by Mc-Graw Hill.
Websites

4. Ministry of Higher Education & Scientific Research
Directorate of Private Studies
GASHA private technical institute
‫زانستى‬ ‫ذينةوةى‬َ‫ي‬‫تو‬ ‫باآلء‬ ‫ندنى‬َ‫ي‬‫خو‬ ‫وةزارةتى‬
‫ندنى‬َ‫ي‬‫خو‬ ‫وةبةرايةتى‬َ‫ي‬ ِ‫بةر‬‫تايبةت‬
‫تايبةت‬ ‫طةشةى‬ ‫تةكنيكى‬ ‫ثةيمانطةى‬
Quality Assurance ‫جؤرى‬ ‫نيايى‬َ‫ل‬‫د‬
Course time table:
Week 1: Theoretical: Orientation
Practical: Introduction and knowing data
Week 2: Theoretical: The meaning statistics and why statistics some concepts and
terms of statistics
Practical: Application of the above concepts
Week 3: Theoretical: Variables and type of data, Types of statistics , Data and types of
data
Practical: collecting data
Week 4: Theoretical: Level of measures, Data collection and sampling techniques
Practical:
Week 5: Theoretical: Frequency Distributions and Graphs, Introduction, Organizing
Data
Practical: Construction of frequency table
Week 6: Theoretical: Histograms, and Ogives, and some other types of graphs
Practical: Construction of frequency table
Week 7: Theoretical: Data Description, measures of central tendency (Mean, Median
and Mode)
Practical: Construction of frequency table
Week 8: Theoretical: Data Description, measures of central tendency (Mean, Median
and Mode)
Practical: Graphical representation of numerical data
Week 9: Theoretical: Measures of Variation ( Range, Variance and Standard Deviation)
Practical: Measures of central tendency
Week 10: Theoretical: Measures of Variation ( Range, Variance and Standard Deviation)
Practical: Measures of central tendency
Week 11: Theoretical: Measure of Position (Quartiles)
Practical: Measures of central tendency
Week 12: Theoretical: Measure of Position (Quartiles)
Practical: measures of position
Week 13: Theoretical: Exploratory Data Analysis
Practical: exploratory data
Week 14: Theoretical: Correlation and Regression, Scatter Plots
Practical: concepts of correlation between of two variables
Week 15: Theoretical: Correlation analysis
Practical: Scatter diagram
Week 16: Theoretical: The significance of the correlation Coefficient
Practical: Examples and Problems
Week 17: Theoretical: Linear Regression

5. Ministry of Higher Education & Scientific Research
Directorate of Private Studies
GASHA private technical institute
‫زانستى‬ ‫ذينةوةى‬َ‫ي‬‫تو‬ ‫باآلء‬ ‫ندنى‬َ‫ي‬‫خو‬ ‫وةزارةتى‬
‫ندنى‬َ‫ي‬‫خو‬ ‫وةبةرايةتى‬َ‫ي‬ ِ‫بةر‬‫تايبةت‬
‫تايبةت‬ ‫طةشةى‬ ‫تةكنيكى‬ ‫ثةيمانطةى‬
Quality Assurance ‫جؤرى‬ ‫نيايى‬َ‫ل‬‫د‬
Practical: concepts of regression, fitting of lines of regression
Week 18: Theoretical: Coefficient of Determination and Standard Error of the Estimate
Practical: Linear regression review, Model assessment, Some practical issues
Week 19: Theoretical: Probability, Sample Space and Probability, Rules of Probability
Practical: Examples and problems
Week 20: Theoretical: Rules of Probability
Practical: Examples and problems
Week 21: Theoretical: Rules of Probability
Practical: Examples and problems
Week 22: Theoretical: Discrete Random Variables, and Probability Distribution of
Discrete r. v., Mean and Variance of Discrete r. v.
Practical: Random experiment, trial, sample point and sample space, events,
operations of events, concepts of equally likely, mutually exclusive and exhaustive
events.
Week 23: Theoretical: Discrete Probability distributions, Binomial distribution
Practical: Random variables discrete, probability mass function (pmf)
Week 24: Theoretical: Continuous Random Variables
1. Continuous r. v.
2. Probability density functions
Practical: Random variables continuous, probability density function (pdf)
Week 25: Theoretical: Continuous Random Variables
3. Mean and Variance of continuous r. v.
Practical: Random variables continuous, probability density function (pdf)
Week 26: Theoretical: Normal Distribution
Practical: use of normal distribution table
Week 27: Theoretical: Applications of Normal Distribution
Practical: Applications of Normal Distribution
Week 28: Theoretical: Applications of Normal Distribution
Practical: some problems
Week 29: Theoretical: Revision and preparing for the final exam
Practical: Practical based on analysis of data collected by students
Week 30: Theoretical: Revision and preparing for the final exam
Practical: Practical based on analysis of data collected by students