Section outline

  • عام

    طي الكل توسيع الكل

    This  introductory course in statistics covering fundamental concepts of descriptive statistics, numerical data representation, and combinatorial analysis. It explains how to collect, organize, summarize, analyze, and interpret data. The course includes statistical variables (qualitative and quantitative), frequency tables, graphical representations (bar charts, histograms, distribution functions), measures of central tendency (mean, median, mode), dispersion measures (variance, standard deviation), and basic combinatorics and probability concepts. Practical exercises with solutions are provided to reinforce understanding.

  • Contact Sheet

    Module Title: UEM211 – Introduction to Probability and Descriptive Statistics

    Instructor Name: Abdelkarim Kelleche
    Email: a.kelleche@univ-dbkm.dz

    Teaching Volume (VHH): 42 hours

    • Lecture: 1 hour 30 minutes

    • Tutorial (TD): 1 hour 30 minutes

    Coefficient: 2
    Credits: 3

    Personal Work Hours (VH of Personal Work): 02 hours

    Assessment Methods:

    • Continuous assessment: 40%

    • Final examination: 60%

    Support Methods:

    • Supervised tutorials and problem-solving sessions

    • Practical exercises

    • Office hours for individual consultation

    • Additional academic support upon request

  • General Objectives

    At the end of this course, the learner will be able to:

    • Define fundamental statistical concepts such as population, individual, variable, modality, frequency, and probability.

    • Distinguish between qualitative and quantitative variables (discrete and continuous).

    • Classify statistical data according to their type.

    • Construct statistical tables including absolute, relative, and cumulative frequencies.

    • Represent data graphically using bar charts, pie charts, histograms, frequency polygons, and distribution functions.

    • Calculate measures of central tendency (mean, median, mode, quartiles).

    • Compute measures of dispersion (range, variance, standard deviation, coefficient of variation).

    • Interpret statistical indicators in practical contexts.

    • Apply combinatorial techniques (arrangements, permutations, combinations) to counting problems.

    • Solve basic probability problems using axiomatic definitions and Bayes’ theorem.

    • Analyze statistical data to support decision-making.

  • Prerequisites

    Before enrolling in this course, the learner should be able to:

    • Solid background in basic mathematics (real numbers, fractions, percentages).

    • Knowledge of elementary algebra (equations, inequalities, powers, square roots).

    • Familiarity with simple mathematical functions and graphical representations.

    • Basic logical reasoning skills.

    • High school level mathematics.

  • Global Plan

    • Preliminaries

      • Introduction to Statistics

      • Basic Vocabulary (Population, Individual, Variable, Modalities)

      • Types of Variables (Qualitative and Quantitative)

      • Statistical Tables and Graphical Representations

    • Numerical Representation of Data

      • Frequencies and Cumulative Frequencies

      • Relative Frequencies

      • Graphical Representation of Discrete and Continuous Variables

      • Measures of Central Tendency (Mean, Median, Mode, Quartiles)

      • Measures of Dispersion (Range, Variance, Standard Deviation)

      • Distribution Function

    • Combinatorial Analysis

      • Fundamental Counting Principles

      • Arrangements (with and without repetition)

      • Permutations (with and without repetition)

      • Combinations

      • Applications to Probability

    • Probability Theory

      • Random Experiment and Events

      • Operations on Events

      • Axiomatic Definition of Probability

      • Bayes’ Theorem

  • Chapter 01