ITST211 : Introduction to Statistics


Department of Information Systems

Academic Program

Bachelor in Information Systems








Probability: concept of a random experiment and sample space; addition and multiplication laws of probability; conditional probability and independence, Bay's theorem and its application. Random Variables and their probability: Conditional Probability; Binomial , Poisson, Hyperogeomtric, Normal , Gamma , Exponential and uniform random variables and their properties. Basic statistical concepts: Statistical data, measures of central tendency; dispersion skewness and kurtosis.Regression and Correlation: simple, linear regression; regression coefficient and correlation coefficient. Fitting of linear and curve linear regressions, Multiple linear regression and multiple.Test of Significance: Basic concepts; use of normal test and t-test for hypothesis testing for a mean and the differences of two means. Use of X2 distribution for testing independence and goodness of fit

Intended learning outcomes

By the end of the course, the student should be able to:
  • Learn about used terms and synonyms in the field of statistics and the types of statistical data collected from the study.
  • Determine the types of statistical data that were collected from the study and the methods of summarizing, displaying and organizing them in simple or compound frequency tables in order to be able to analyze them and reach the required results for the problem in question.
  • Explain the measures of central tendency and their use in representing a large group of data and study the relationship between these measures in addition to knowing other measures such as quartiles, deciles and percentiles. Distinguishing between the types of statistical data and the characteristics of each of them and studying the relationships between the phenomena under study, is also emphasised.
  • Show the homogeneity and dispersion of the statistical data by knowing the most important measures of dispersion and the relationship between these data through identifying the measures of regression, correlation, skewness and kurtosis.
  • Show how to draw and represent statistical data after presenting it in appropriate frequency tables.
  • Distinguish between the types of statistical data, the characteristics of each of them, and their areas of use, while providing the student with the skill of knowing the relationships between the phenomena under study.
  • Analyze statistical data in order to solve some practical problems such as poverty, unemployment, spread of ignorance and disease
  • Prove some theories related to measures of central tendency, dispersion, correlation, and regression.
  • Compare the different statistical measures to conclude the relationship between these measures.
  • Discusse and interpret The graphic representation the statistical data after presenting them in appropriate frequency tables.
  • use statistical data collected from previous studies.
  • Provide solutions to new and different problems related to previously studied phenomena
  • Use different statistical measures to represent the data and find out the relationship between them
  • Apply and use statistical laws according to the study in question
  • Draw and represe

Teaching and learning methods

  • Practical and theoretical lectures
  • Discussion and dialogue
  • Brainstorm
  • Working papers, case study
  • Presentations
  • Videos and e-learning
  • Using software and computer applications such as (MATLAB, Geogebra, Geometer)

Methods of assessments

  • Midterm Exam = 30
  • Assignments = 10
  • Final Exam = 60

Course contents

  • Definition, basic concepts, types and sources of statistical data, and types of samples
  • Presentation of data using simple and tabulated frequency tables (with periods)
  • Types of frequency tables, relative and percentage frequency tables
  • Cumulative Frequency (upward – downward)
  • Graphing using pie charts, simple bar graphs, and multiple bar graphs
  • Representation using bar graphs, histogram, the polygon, and the iterative curve.
  • the concept of the summation
  • measures of central tendency
  • The effect of linear transformations on measures of central tendency
  • The relationship between measures of central tendency and some other measures
  • measures of dispersion
  • The effect of linear transformations on measures of dispersion
  • measures of skewness and kurtosis
  • Correlation and regression

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Introduction to Programming (ITGS122)
General English2 (ITEL122)
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Introduction to Statistics (ITST211)
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