Browsing by Author "GOUAL, Hafida"

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    Explanatory Data Analysis : course and exercises
    (Université Badji Mokhtar Annaba, 2025) GOUAL, Hafida
    Exploratory Data Analysis (EDA) is a crucial component of modern statistical practice, originally articulated by John W. Tukey in 1977. It emphasizes the importance of understanding data through visualization, summary, and pattern recognition prior to formal modeling. EDA serves as a foundational tool across various scientific and industrial domains, facilitating the extraction of meaningful insights from complex datasets. This handbook targets third-year undergraduate students in Applied Mathematics, providing a rigorous mathematical foundation while emphasizing practical interpretation and active learning. It progresses from univariate to multivariate analysis, integrating theoretical concepts with real-world applications. The structured approach encompasses essential topics such as descriptive statistics, dimensionality reduction, and multivariate analysis techniques. Ultimately, this text aims to cultivate an analytical mindset, preparing students for advanced studies and professional endeavors in data analysis, echoing Tukey's belief in the value of revealing the unexpected through EDA.
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    Non-parametric Statistics: course and exercises
    (Université Badji Mokhtar Annaba, 2025) GOUAL, Hafida
    This handout course on nonparametric statistics provides an accessible introduction to fundamental concepts and methods that do not rely on strict assumptions about data distribution. Students will explore the key differences between parametric and nonparametric inference, gaining insights into when to apply these techniques. The course covers essential topics such as ranks, medians, and order statistics, alongside practical applications of statistical tests like the Mann-Whitney and Kruskal-Wallis tests. Participants will learn to construct nonparametric estimators for cumulative distribution functions and probability density functions, using tools like histograms and kernel density estimation. The course also introduces resampling methods, including the Jackknife and Bootstrap, enabling students to estimate standard errors and build confidence intervals. Through hands-on exercises and examples, students will enhance their ability to perform and interpret nonparametric hypothesis tests, making informed decisions about their use in various scenarios. By the end of the course, participants will be equipped with the knowledge and skills to effectively implement nonparametric methods in their statistical analyses.