Lecture notes on probability and statistics

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dc.contributor.authorKHEIREDDINE BOUGUERRA
dc.date.accessioned2025-05-26T07:33:06Z
dc.date.available2025-05-26T07:33:06Z
dc.date.issued2025-04-29
dc.description.abstractThis document serves as a valuable resource for engineering students seeking to gain a deep understanding of fundamental concepts in probability and statistics. It covers statistical series for both one and two variables, enabling rigorous data analysis and interpretation. The first part focuses on one-variable statistical series, emphasizing central tendency indicators (mean, median, mode) and dispersion measures (variance, standard deviation). The second part addresses two-variable statistical series, introducing key concepts such as covariance and correlation, which are essential for establishing relationships between different quantities. Furthermore, the document explores probability theory in a finite framework, providing students with the necessary tools to quantify uncertainty and model random phenomena. Concepts such as conditional probabilities and event independence are also developed. Finally, special attention is given to random variables, whether discrete or continuous, as well as their associated probability distributions, including the binomial, Poisson, and normal distributions. Through this pedagogical approach, engineering students acquire the foundational knowledge needed to model and analyze complex phenomena while developing a strong mathematical intuition.
dc.identifier.other2025/103
dc.identifier.urihttps://repository.univ-msila.dz/handle/123456789/46243
dc.language.isoen
dc.publisherMOHAMED BOUDIAF UNIVERSITY OF M'SIIA
dc.relation.ispartofseries2025
dc.subjectOne-variable statistical series
dc.subjectmean
dc.subjectmedian
dc.subjectmode
dc.subjectvariance
dc.subjectstandard deviation
dc.subjecttwo-variable statistical series
dc.subjectcovariance and correlation
dc.subjectprobability theory
dc.subjectrandom variables
dc.subjectprobability distributions
dc.titleLecture notes on probability and statistics
dc.typeLearning Object

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