SKU: 31205225888

PORSCHE BOXSTER 2008 19" FACTORY ORIGINAL WHEEL RIM REAR

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Description

PORSCHE BOXSTER 2008 19" FACTORY ORIGINAL WHEEL RIM REARPORSCHE 19" RIM 67330 99736215805 Item Description PORSCHE BOXSTER 2008 19 INCH ALLOY RIM WHEEL FACTORY OEM REAR 67330 99736215805 Manufacturer Part Number: 99736215805 Hollander Number: 67330 Condition: Remanufactured (aka reconditioned) to Original Factory Condition Finish: GLOSS BLACK WITH MACHINED LIP Size: 19" x 9. 5" Bolts: 5x130mm Offset: N A Position: REAR NOTE: The buyer is responsible for fitment; *Center Cap(s), Valve Stem(s), Valve Stem

PORSCHE 19" RIM 67330 99736215805
Item Description

 PORSCHE BOXSTER 2008 19 INCH ALLOY RIM WHEEL FACTORY OEM REAR 67330 99736215805

Manufacturer Part Number: 99736215805
Hollander Number: 67330
Condition: Remanufactured (aka reconditioned) to Original Factory Condition
Finish: GLOSS BLACK WITH MACHINED LIP
Size: 19" x 9.5"
Bolts: 5x130mm
Offset: N/A
Position: REAR


NOTE: The buyer is responsible for fitment; 
*Center Cap(s), Valve Stem(s), Valve Stem Sensor(s),
 TMPS, Tire(s), Lug Nut(s) as well as Lug Nut Covers are NOT Included.

Vehicle Fitment
  • 2008 PORSCHE BOXSTER 19" FACTORY OEM WHEEL RIM
  • 5 SPOKE FACTORY ORIGINAL WHEEL RIM
Quality Management

Product quality is our top concern, so at i1parts solely with the highest quality remanufacturers, therefore each wheel undergoes a rigorous process of remanufacturing and variousinspections based on internationally recognized standards to make sure its structure is 100% sound, straight and true,using state of the art technologyand methods by the highest quality remanufacturers, many of which are ISO 9001 andSAE J2530 certified, so our customers can find replacement wheels thattruly are just like new.
All of our remanufactures use computerized systems to match thefactory color. To further improve the satisfaction of our customers wethen inspect every wheel prior to listing making sure the color is asclose to factory as possible.

Payment
Price is important factor to our customers, usually our prices arecertainly competitive, but sometimes our quality control model does not always permitus to have the lowest prices. Therefore we have created a Damaged Wheel Buy Back (Recycling)program to decrease the overall cost for our customers while alsooffering an environmentally safe way of disposing of their old wheels. Only OEM rims are qualified for  Damaged Wheel Buy Back (Recycling) program.   

We accept payment in the form of PayPal (preferred method).Payment must be made via eBay. Items will not ship untilpayment is received. We are required to collect 6% sales tax to allorders shipped to PA state residents. This will be added to your orderupon checkout. International orders can only be made via PayPal. Please contact us via Ebay for more information.

Shipping Information

All wheels or products are shipped within the contiguous 48states using FedEx Ground or UPS Ground services. We ship within 24 to 72 hours upon confirming your payment. If rush shipping isneeded, please contact us for a quote. We can add Next Day, 2nd Day,etc. to accommodate your needs. All items are shipped in reinforcedcardboard boxes and packaged to ensure protection.

Shipments to buyers in Alaska, Hawaii, Guam, Puerto Rico, the U.S.Virgin Islands or outside the United States - Please contact us for ashipping quote. Outside the U.S., buyers may be subject to local taxes,and brokerage fees. Please be aware of this before bidding orpurchasing. These fees are the responsibility of the buyer.



Return Policy

Returns are accepted within 14 (fourteen) days of receipt and the returned items must not be installed, used, mounted or altered in anyway. Customers may return the purchased items for any reason that makes customer unsatisfied. Please be NOTED that there is a 25% restocking fee and the customer is responsible for return shipping unless the item is found to be damaged or defective. All items must be returned in the same condition in which they were received.

Feedback

We are committed to your satisfaction. We will automatically leavepositive feedback for buyers within 24 hours of receiving payment.Feedback is an important asset on eBay for buyers and sellers alike, soif you are satisfied by your experience with The i1parts we wouldgreatly appreciate it if you could take a moment to leave us positivefeedback with 5 star ratings. If you are not completely satisfied pleasecontact us to give us the opportunity to improve your experience.Please know that your positive feedback and 5 star rating on eBay areappreciated and vital to the growth of The i1parts. Thank you!!!

Shipping Notes
  • Free Standard Shipping on $100+ Orders to the USA.
  • Except Preorder products are shipped in 48 hours.
  • Delivery to the USA:
  1. Standard Shipping : 3-10 business days
  • If time is of the essence, please consider selecting expedited delivery for faster service.
Exchange/Return Notes
  • We offer a 30-day return/exchange service after receiving.
  • Final sale items are not eligible for returns or exchanges.
  • To process your return/exchange, please contact us at [email protected]
  • Please click here for more details>>> Return & Exchange Policy
SKU: 31205225888

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Walter Echo-Hawk, author of THE SEA OF GRASS.
Dallas, US
★★★★★ 5
Native American history at its best!
Format: Hardcover
Kent Blansett's engrossing story about the life & times of the famed Mohawk activist Richard Oakes is Native American history at its best. I appreciated the well-written context provided about the birth, growth and impact of the Red Power Movement and the pivotal role that social justice activism played in the rise of modern Indian nations in the United States today. This scholarly work helps us understand modern Native America and is a "must-read" for every Native American Studies student and scholar, as well as readers interested in important American social justice movements.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on April 1, 2019
P
Verified Purchase
Par
Whiting, US
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents. The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!! The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!! I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge. Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents. Personally I was disappointed by lack of any example on time series. Must have for ML practitioner as a reference and guide.
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Reviewed in the United States on December 20, 2024
R
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Richard Hackathorn
Belleville, US
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book. For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch. For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min). From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures. I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications. Several textbook editions later, what is different about this new edition? First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills. Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like. Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
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Reviewed in the United States on February 26, 2022
A
Verified Purchase
Amazon Customer
Birmingham, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 10, 2025
K
Verified Purchase
Kindle Customer
Carnegie, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 3, 2026

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