000			03296cam a2200277zu 4500
001			88962213
003			FRCYB88962213
005			20250429181748.0
006			m o d
007			cr un
008			250429s2024 fr | o|||||0|0|||eng d
020			_a9781837026432
035			_aFRCYB88962213
040			_aFR-PaCSA _ben _c _erda
100	1		_aStone, James V
245	0	1	_aLinear Regression With Python _bA Tutorial Introduction to the Mathematics of Regression Analysis _c['Stone, James V']
264		1	_bPackt Publishing _c2024
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aStone, James V
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88962213 _qtext/html _a
520			_aMaster linear regression concepts with Python through hands-on examples and in-depth explanations of statistical methods.Key FeaturesA comprehensive guide to regression analysis blending theory, statistics, and Python examplesAdvanced regression topics like Bayesian and multivariate methods explained with clarityReal-world examples and Python code walkthroughs for practical understanding of conceptsBook DescriptionThis 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 learnUnderstand the fundamentals of linear regressionCalculate the best-fitting line using dataAnalyze statistical significance in regressionImplement Python code for regression modelsEvaluate the goodness of fit in modelsExplore multivariate and weighted regressionWho this book is forThis 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.
999			_c1325409 _d1325409