Linear Regression With Python : A Tutorial Introduction to the Mathematics of Regression Analysis
Master linear regression concepts with Python through hands-on examples and in-depth explanations of statistical methods. Key Features: A comprehensive guide to regression analysis blending theory, statistics, and Python examples ; Advanced regression topics like Bayesian and multivariate methods ex...
Enregistré dans:
| Auteur principal: | |
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| Format: | Livre numérique |
| Langue: | Anglais |
| Publié: |
Birmingham :
Packt Publishing
2024.
Paris : Cyberlibris |
| Accès en ligne: | Accès Université d'Orléans et IFPM |
| Note: |
Couverture. https://static2.cyberlibris.com/books_upload/136pix/9781837026425.jpg Cyberlibris (ScholarVox) corpus Informatique |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Linear Regression With Python, A Tutorial Introduction to the Mathematics of Regression Analysis, James V Stone, Birmingham, Packt Publishing, 2024, 1 vol. (140 p.), 978-18-3702-643-2 |
| Résumé: | Master linear regression concepts with Python through hands-on examples and in-depth explanations of statistical methods. Key Features: A comprehensive guide to regression analysis blending theory, statistics, and Python examples ; Advanced regression topics like Bayesian and multivariate methods explained with clarity ; Real-world examples and Python code walkthroughs for practical understanding of concepts. Book Description: This book offers a detailed yet approachable introduction to linear regression, blending mathematical theory with Python-based practical applications. Beginning with fundamentals, it explains the best-fitting line, regression and causation, and statistical measures like variance, correlation, and the coefficient of determination. Clear examples and Python code ensure readers can connect theory to implementation. As the journey continues, readers explore statistical significance through concepts like t-tests, z-tests, and p-values, understanding how to assess slopes, intercepts, and overall model fit. Advanced chapters cover multivariate regression, introducing matrix formulations, the best-fitting plane, and methods to handle multiple variables. Topics such as Bayesian regression, nonlinear models, and weighted regression are explored in depth, with step-by-step coding guides for hands-on practice. The final sections tie together these techniques with maximum likelihood estimation and practical summaries. Appendices provide resources such as matrix tutorials, key equations, and mathematical symbols. Designed for both beginners and professionals, this book ensures a structured learning experience. Basic mathematical knowledge or foundation is recommended. What you will learn: Understand the fundamentals of linear regression ; Calculate the best-fitting line using data ; Analyze statistical significance in regression ; Implement Python code for regression models ; Evaluate the goodness of fit in models ; Explore multivariate and weighted regression. Who this book is for: This book is ideal for students, data scientists, and professionals interested in learning linear regression. It caters to both beginners seeking a solid foundation and experienced analysts looking to refine their skills. Basic mathematical knowledge or foundation is recommended; prior programming experience in Python will be beneficial. The hands-on examples and coding exercises make it suitable for anyone eager to apply regression concepts in real-world scenarios |
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| Description: | Couverture. https://static2.cyberlibris.com/books_upload/136pix/9781837026425.jpg Cyberlibris (ScholarVox) corpus Informatique |
| ISBN: | 9781837026425 |
| Accès: | L'accès en ligne est réservé aux établissements ou bibliothèques ayant souscrit l'abonnement. Cyberlibris |