Course : Introduction to probability and descriptive statistics
Level : 1st years Bechelor
Instructor : Dr. Ahmed Bouchenak (Associate Professor -B-)
Email : a.bouchenak@univ-mascara.dz
Teaching unit : Methodological
Credits : 3
Coe?cient : 2
Hourly volumes (per week) : Course (1h30) TD (1h30)
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Prerequisites : Recommended prior knowledge : Basic mathematics.
Course goals : The objective of this course is to :
- Introduce the fundamental notions of probability and statistical series with one variable.
- Know the basic principles of statistics.
- Analyze data (scienti?c tools allowing to summarize a whole data in order to highlight the
information).
- Know the di?erent types of character qualitative, quantitative (discrete and continuous).
- Learn to calculate Position and dispersion parameters.
- Represent graphically the three types of characters.
- Learn probability calculus.
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Course topics :
Chapter 1 : Basic notions and statistical vocabulary.
1- Vocabulary .
- Statistical test, Population, Statistical unit (individual).
- statistical variable (character), Modalities (terms).
2- Types of statistical variables (characters).
- Qualitative character.
- Quantitative character (Discrete, Continuous).
Chapter 2 : Study of a Qualitative and Discrete Quantitative Statistical Variable.
1- Qualitative character, Graphical representation (Bar diagram and Pie diagram).
2- Discreet quantitative character, Repetition, Cumulative repetition, Frequency, Cumulative
frequency.
3- Distribution function , Graphical representation (Bar chart, cumulative curve).
4- Position parameters (central tendency characteristic) :
Mode, Median, Quartiles, Arithmetic mean, Geometric mean, Harmonic mean, Quadratic mean.
5- Dispersion parameters (variability characteristic) :
Range, Variance, Standard deviation, Coe?cient of variation, Average absolute deviation.
Chapter 3 : Study of a Continuous Quantitative Statistical Variable
1- Continuous quantitative character, Class of values, Number of classes.
2- Repetition and Frequency of a class, Graphical representation (Histogram).
3- Position parameters (central tendency characteristic) :
Mode, Median, Quartiles, Arithmetic mean, Geometric mean, Harmonic mean, Quadratic mean.
4- Dispersion parameters (variability characteristic) :
Range, Variance, Standard deviation, Coe?cient of variation, Average absolute deviation.
Chapter 4 : Probability Calculus
1- Combinatorial analysis : (Fundamental principle of combinatorial analysis, Arrangements,
Permutations, Combinations).
2- Probable space : (Random experiment, Elementary and compound events, Realization of an
event, Incompatible event, Complete event system, Algebra of events, Probable space, Concept
of probability).
3- Probabilistic space : (De?nitions, consequence of the de?nition, conditional probability, in-
dependent events, independent experiences).
4- Construction of a probability.
5- Conditional probabilities, independence and compound probabilities (Conditional probabili-
ties, Independence, Mutual independence, Compound probabilities, Bayes formula).
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Evaluation methods :
Nature of control Weighting in %
EXAM 60 %
TD 40 %
TOTAl 100 %
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References :
[1] G. Calot, Cours de statistique descriptive, Dunod, Paris, 1973.
[2] P. Bailly, Exercices corrigés de statistique descriptive, OPU Alger, 1993.
[3] H. Hamdani, Statistique descriptive avec initiation aux méthodes d'analyse de l'information
économique : exercices et corriges, OPU Alger, 2006.
[4] K. Redjdal, Probabilités, OPU Alger, 2004.
[5] Descriptive Statistics and Probability Theory, Robert A. Barks Hutchinson, 1972.
[6] Statistics : Descriptive statistics and probability, Elliot A. Tanis Harcourt Brace Jovanovich,
1987.
[7] Probability and Statistics : The Science of Uncertainty Michael J. Evans and Je?rey S.
Rosenthal University of Toronto.
- Teacher: Ahmed bouchenak