Linear Regression With Python. (Record no. 16078)
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| fixed length control field | 03332nam a2200265uu 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250710182908.0 |
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| fixed length control field | 250616s||||||||||||||||o||||||||||| |d |
| 024 80 - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 9781837026425 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | PACKT |
| Transcribing agency | PACKT |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | en |
| 044 ## - COUNTRY OF PUBLISHING/PRODUCING ENTITY CODE | |
| MARC country code | GB |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | James V Stone |
| Relator term | author. |
| 245 00 - TITLE STATEMENT | |
| Title | Linear Regression With Python. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | GB: |
| Name of publisher, distributor, etc. | Packt, |
| Date of publication, distribution, etc. | 2024-11-25. |
| 263 ## - PROJECTED PUBLICATION DATE | |
| Projected publication date | 2024-11-25 |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | GB: |
| Name of producer, publisher, distributor, manufacturer | Packt, |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 140. |
| 377 ## - ASSOCIATED LANGUAGE | |
| Language code | en |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | <p><b>Master linear regression concepts with Python through hands-on examples and in-depth explanations of statistical methods.</b></p><h4>Key Features</h4><ul><li>A comprehensive guide to regression analysis blending theory, statistics, and Python examples</li><li>Advanced regression topics like Bayesian and multivariate methods explained with clarity</li><li>Real-world examples and Python code walkthroughs for practical understanding of concepts</li></ul><h4>Book Description</h4>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.<h4>What you will learn</h4><ul><li>Understand the fundamentals of linear regression</li><li>Calculate the best-fitting line using data</li><li>Analyze statistical significance in regression</li><li>Implement Python code for regression models</li><li>Evaluate the goodness of fit in models</li><li>Explore multivariate and weighted regression</li></ul><h4>Who this book is for</h4>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. |
| 538 ## - SYSTEM DETAILS NOTE | |
| System details note | Data in extended ASCII character set. |
| 538 ## - SYSTEM DETAILS NOTE | |
| System details note | Mode of access: Internet. |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | PACKT |
| 773 0# - HOST ITEM ENTRY | |
| Title | Linear Regression With Python |
| Place, publisher, and date of publication | GB,Packt,2024-11-25 |
| Physical description | 140 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://learning.packt.com/product/482405">https://learning.packt.com/product/482405</a> |
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