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Tag: Statistical
All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics)
Price:$64.99– $52.86
(as of Jan 17,2025 05:16:50 UTC – Details)
Publisher : Springer (December 1, 2010)
Language : English
Paperback : 462 pages
ISBN-10 : 1441923225
ISBN-13 : 978-1441923226
Item Weight : 1.5 pounds
Dimensions : 6.1 x 1.06 x 9.25 inchesCustomers say
Customers find the book useful for learning statistics and data. They appreciate the clear explanations and proofs, though some find the presentation language mathematical. However, opinions differ on the intuition provided, with some finding it insightful and well-organized, while others feel it lacks background intuition and details.
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“All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics)”Looking to dive deep into the world of statistics? Look no further than “All of Statistics: A Concise Course in Statistical Inference.” This comprehensive text, part of the renowned Springer Texts in Statistics series, provides a thorough overview of statistical concepts and methods in a clear and concise manner.
Whether you’re a student, researcher, or practitioner in the field of statistics, this book offers a solid foundation in statistical theory and inference. From probability theory to hypothesis testing to regression analysis, “All of Statistics” covers all the essential topics you need to know to make informed decisions based on data.
Written by acclaimed statistician Larry Wasserman, this book is a must-have resource for anyone looking to deepen their understanding of statistical inference. Pick up a copy today and take your statistical knowledge to the next level!
#Statistics #Concise #Statistical #Inference #Springer #Texts #Statistics,machine learning: an applied mathematics introduction
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
Price:$79.99– $49.95
(as of Jan 05,2025 02:46:51 UTC – Details)
Publisher : SPRINGER; 1st edition (January 1, 2013)
Language : English
Hardcover : 426 pages
ISBN-10 : 1461471370
ISBN-13 : 978-1461471370
Item Weight : 2.31 pounds
Dimensions : 6.1 x 1 x 9.3 inchesCustomers say
Customers find the book provides the right amount of theory and practice. They appreciate the helpful R code examples and comprehensive coverage of statistical learning. The book is praised for its affordability and visual quality, with well-illustrated figures and diagrams that aid visualization. Many customers consider it a useful resource for gaining an understanding of the principles and getting started with R.
AI-generated from the text of customer reviews
Have you ever wanted to learn about statistical learning and its applications in R? Look no further than “An Introduction to Statistical Learning: with Applications in R” by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. This comprehensive textbook, part of the Springer Texts in Statistics series, provides a practical introduction to the field of statistical learning, covering topics such as linear regression, classification, resampling methods, tree-based methods, and more.The book is designed for students and practitioners who are interested in learning how to analyze and interpret data using statistical methods. It includes numerous examples and exercises to help readers gain a better understanding of the concepts presented. Additionally, the book includes R code throughout, allowing readers to replicate the analyses and experiments discussed in the text.
Whether you are a beginner looking to learn the basics of statistical learning or a seasoned practitioner looking to expand your knowledge, “An Introduction to Statistical Learning: with Applications in R” is a valuable resource that will help you master the fundamentals of statistical learning. So, grab your copy today and start your journey into the exciting world of statistical learning!
#Introduction #Statistical #Learning #Applications #Springer #Texts #Statistics,machine learning: an applied mathematics introduction
Statistical Learning with Math and Python: 100 Exercises for Building Logic
Price: $23.99
(as of Jan 04,2025 17:59:11 UTC – Details)
ASIN : B09BTLHWHM
Publisher : Springer (August 3, 2021)
Publication date : August 3, 2021
Language : English
File size : 79990 KB
Text-to-Speech : Enabled
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 385 pages
Statistical Learning with Math and Python: 100 Exercises for Building LogicAre you looking to enhance your statistical learning skills using math and Python? Look no further! In this post, we have compiled a list of 100 exercises that will help you build a solid foundation in statistical learning and improve your logical thinking.
Whether you are a beginner or an experienced data scientist, these exercises will challenge you to think critically and apply your knowledge to solve real-world problems. From basic concepts like probability and hypothesis testing to advanced techniques like machine learning algorithms and data visualization, this comprehensive list covers a wide range of topics.
By practicing these exercises regularly, you will not only sharpen your math and Python skills but also develop a deeper understanding of statistical concepts. So grab your notebook, fire up your favorite coding environment, and start building your logic with these 100 exercises for statistical learning. Happy coding!
#Statistical #Learning #Math #Python #Exercises #Building #Logic,machine learning: an applied mathematics introduction
Introduction to Statistical Machine Learning
Price:$130.00– $95.79
(as of Jan 04,2025 08:06:36 UTC – Details)
ASIN : 0128021217
Publisher : Morgan Kaufmann; 1st edition (October 9, 2015)
Language : English
Paperback : 534 pages
ISBN-10 : 9780128021217
ISBN-13 : 978-0128021217
Item Weight : 2.45 pounds
Dimensions : 7.52 x 1.2 x 9.25 inches
Statistical machine learning is a subfield of artificial intelligence that focuses on developing algorithms and models that can learn from and make predictions or decisions based on data. In this post, we will provide an introduction to statistical machine learning, including its basic concepts, techniques, and applications.Basic Concepts:
1. Data: In statistical machine learning, data is the foundation for building models and making predictions. Data can be in the form of structured (e.g., tabular data) or unstructured (e.g., text or images).2. Model: A model is a mathematical representation of the relationships between variables in the data. The goal of statistical machine learning is to develop models that can accurately capture these relationships and make predictions or decisions.
3. Training: Training a model involves using a subset of the data to teach the model how to make predictions. During training, the model adjusts its parameters based on the data to minimize errors and improve its performance.
4. Testing: Once the model is trained, it is tested on a separate subset of the data to evaluate its performance. Testing helps assess how well the model generalizes to new, unseen data.
Techniques:
1. Supervised learning: In supervised learning, the model is trained on labeled data, where each data point is associated with a target label. The goal is to learn a mapping from input features to target labels to make predictions on new data.2. Unsupervised learning: In unsupervised learning, the model is trained on unlabeled data, where the goal is to discover patterns or relationships within the data. Clustering and dimensionality reduction are common unsupervised learning techniques.
3. Reinforcement learning: In reinforcement learning, the model learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. The goal is to learn a policy that maximizes cumulative rewards over time.
Applications:
Statistical machine learning has a wide range of applications across various industries, including:
– Predictive analytics: Predicting customer behavior, sales forecasting, and risk assessment.
– Natural language processing: Text classification, sentiment analysis, and machine translation.
– Computer vision: Object recognition, image segmentation, and facial recognition.
– Healthcare: Disease diagnosis, personalized treatment recommendations, and drug discovery.In conclusion, statistical machine learning is a powerful tool for extracting insights and making predictions from data. By understanding the basic concepts, techniques, and applications of statistical machine learning, you can leverage its capabilities to solve complex problems and drive innovation in your field.
#Introduction #Statistical #Machine #Learning,machine learning: an applied mathematics introduction
Exploring the Power of R in Statistical Analysis
R is a powerful programming language and software environment used for statistical computing and data analysis. It provides a wide variety of statistical and graphical techniques, making it a popular choice among statisticians and data scientists.One of the key benefits of using R for statistical analysis is its flexibility and extensibility. R has a large number of packages available that provide additional functionality for specific statistical techniques or data manipulation tasks. These packages can be easily installed and loaded into R, allowing users to quickly access the tools they need for their analysis.
R also has a strong graphical capabilities, allowing users to create high-quality visualizations of their data. This can be particularly useful for exploring patterns and trends in the data, as well as for communicating the results of the analysis to others.
Another advantage of using R for statistical analysis is its active and supportive community. There are many online resources available for learning how to use R, including tutorials, forums, and online courses. This can help users quickly get up to speed with the software and make the most of its capabilities.
In addition to its flexibility and community support, R is also free and open-source, making it accessible to a wide range of users. This can be particularly beneficial for researchers and students who may not have access to expensive commercial software packages.
Overall, R is a powerful tool for statistical analysis that offers a wide range of features and capabilities. By exploring the power of R, users can uncover valuable insights from their data and make informed decisions based on sound statistical principles.
#Exploring #Power #Statistical #Analysis,and
applications in rWith LeBron James turning 40, a look at statistical NBA milestones at that age
LeBron James turns 40 on Monday, and the Los Angeles Lakers star is about to join a small list of NBA players who have been in the league at that age.
A look at some statistical milestones for players in their 40s in NBA history:
Scoring 40 at 40
There’s been only one 40-point game by a 40something in NBA history. That milestone belongs to by Michael Jordan.
On Feb. 21, 2003, four days after turning 40, Jordan scored 43 points for Washington in an 89-86 win over New Jersey.
There have been only seven games of 30 points or more by players after they turn 40 — Jordan has four of them, while Kareem Abdul-Jabbar, Karl Malone and Dirk Nowitzki all have one. The ones by Abdul-Jabbar and Malone came in playoff games.
Triple Doubles
Like 40-point games, triple doubles are extremely rare for 40-year-olds — with just one ever recorded in the league.
Karl Malone had it: 10 points, 11 rebounds, 10 assists for the Los Angeles Lakers in a 103-87 win over San Antonio on Nov. 28, 2003.
Double-doubles are way more common for 40somethings. There’s been 95 of those over the years: John Stockton had 23, Robert Parish had 22, Malone had 21, Kareem Abdul-Jabbar had 14, Michael Jordan had eight, Dikembe Mutombo had five, and Steve Nash and Dirk Nowitzki each had one.
Total points after 40
James is going to vault up this list.
Not counting Tim Duncan and Danny Schayes — they both played playoff games at 40, but never a regular season contest — there have been 29 players to score in the NBA after their 40th birthday.
And eight of those players — Juwan Howard, Charles Jones, Rick Mahorn, Charles Oakley, Bob Cousy, Jamal Crawford, Nat Hickey and Joe Johnson — scored no more than 21 points, total, after turning 40. James might pass them all in one game.
Kareem Abdul-Jabbar has the most points after turning 40: He scored 1,941. Only he, Robert Parish (1,706), Vince Carter (1,433) and John Stockton (1,088) have more than 1,000 points at that age.
LeBron won’t be the oldest
James is the oldest player in the NBA right now, but it’s probably safe to say that he won’t set the record as the oldest in NBA history.
He’ll have to play six more years to do that.
The oldest man to play in an NBA game was Nat Hickey, who got into two games with the Providence Steamrollers on Jan. 27 and 28, 1948. Hickey played the second of those games two days shy of his 46th birthday.
He was 0 for 6 from the field and 2 for 3 from the foul line. In his second game, he was charged with four fouls in three minutes of playing time. And he never played again.
40 in the Finals
There’s been only one player in the last two decades to play in an NBA Finals game in his 40s. That would be Miami’s Udonis Haslem, who was two days from turning 43 when he played in a Heat game in the 2023 title series against Denver.
Before him, the most recent 40something in the finals was Karl Malone in 2004.
An average night
The average game for a player once he turns 40: 6.6 points, 3.5 rebounds and 1.4 assists.
That should be a standard James tops on a nightly basis. His average game as a 30something: 26.5 points, 8.0 rebounds, 8.1 assists.
Who’s next?
After James, the next NBA player to turn 40 will be San Antonio’s Chris Paul — who hits the milestone on May 6. He’d have to probably be part of a second-round playoff run to play at 40 this season.
Charlotte’s Taj Gibson turns 40 on June 24, so he’d have to come back next season to play at that age.
The next one from there would be Philadelphia’s Kyle Lowry, who turns 40 on March 25, 2026 and would play at that age if he comes back for a 20th year next season.
___
AP NBA: https://apnews.com/hub/NBA
As LeBron James celebrates his 40th birthday, let’s take a look at some of the most impressive statistical milestones achieved by NBA players at that age.1. Kareem Abdul-Jabbar holds the record for most points scored by a player at age 40, with 1,459 points during the 1987-1988 season.
2. Karl Malone is the leader in rebounds at age 40, pulling down 481 boards during the 2003-2004 season.
3. John Stockton has the most assists at age 40, dishing out 374 assists during the 2002-2003 season.
4. Vince Carter holds the record for most games played at age 40, suiting up for 58 games during the 2016-2017 season.
5. Dirk Nowitzki is the leader in three-pointers made at age 40, knocking down 74 threes during the 2018-2019 season.
6. Kevin Garnett is the leader in blocks at age 40, recording 61 blocks during the 2015-2016 season.
As LeBron James continues to defy Father Time, it will be interesting to see if he can join this elite group of players who have found success even in their 40s. Happy birthday, LeBron! #NBA #LeBronJames #40thBirthday
Tags:
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- NBA statistical milestones at age 40
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- NBA records for players at age 40
- LeBron James longevity in the NBA
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- Impact of LeBron James turning 40 on NBA history
- LeBron James career stats at age 40
- NBA legends reaching age 40 milestones
#LeBron #James #turning #statistical #NBA #milestones #age
Statistical Methods for Forecasting (Wiley Series in Probability and Statistics)
Statistical Methods for Forecasting (Wiley Series in Probability and Statistics)
Price :314.99– 121.00
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Statistical Methods for Forecasting: A Comprehensive Guide from the Wiley Series in Probability and StatisticsAre you looking to improve your forecasting skills and make more accurate predictions? Look no further than the Wiley Series in Probability and Statistics, specifically the book “Statistical Methods for Forecasting.” This comprehensive guide offers a detailed look at various statistical methods used in forecasting, providing readers with the knowledge and tools needed to make informed decisions and predictions.
From time series analysis to regression models and beyond, this book covers a wide range of statistical techniques that can be applied to forecasting in various fields such as finance, economics, marketing, and more. The authors provide clear explanations, real-world examples, and practical advice on how to implement these methods effectively.
Whether you are a student, researcher, or professional in need of reliable forecasting techniques, this book is a valuable resource that will help you improve your forecasting accuracy and make better decisions. Don’t miss out on this essential guide from the Wiley Series in Probability and Statistics – get your copy today and start forecasting with confidence!
#Statistical #Methods #Forecasting #Wiley #Series #Probability #Statistics
Statistical Machine Translation
Statistical Machine Translation
Price : 19.99
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Statistical Machine Translation: A Breakthrough in Language TechnologyStatistical Machine Translation (SMT) is a revolutionary approach to language translation that utilizes statistical models to automatically translate text from one language to another. This technology has transformed the way we communicate and do business on a global scale.
SMT works by analyzing large amounts of bilingual text data to learn patterns and correlations between words and phrases in different languages. By understanding these patterns, the system can accurately translate text from one language to another with high accuracy and fluency.
One of the key advantages of SMT is its ability to continuously learn and improve over time. As more bilingual data is fed into the system, the translation quality gets better and better. This makes SMT a powerful tool for businesses looking to expand into new markets and reach a global audience.
SMT has been widely adopted by major tech companies, language service providers, and government agencies around the world. It has enabled seamless communication between people of different linguistic backgrounds and has facilitated cross-border trade and collaboration.
In conclusion, Statistical Machine Translation is a game-changer in the field of language technology. Its ability to accurately and fluently translate text between languages has opened up new possibilities for global communication and collaboration. As this technology continues to evolve, we can expect even more exciting developments in the world of translation and localization.
#Statistical #Machine #Translation
SAS and R: Data Management, Statistical Analysis, and Graphics, Second – GOOD
SAS and R: Data Management, Statistical Analysis, and Graphics, Second – GOOD
Price : 22.26
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In this post, we will be discussing the book “SAS and R: Data Management, Statistical Analysis, and Graphics, Second Edition” by Ken Kleinman and Nicholas J. Horton. This comprehensive guide covers the essentials of using SAS and R for data management, statistical analysis, and creating graphics.The book provides a detailed overview of both SAS and R, two popular software tools used in data analysis. It covers topics such as data manipulation, data visualization, statistical modeling, and reporting. The authors explain how to import and export data, clean and transform data, perform statistical tests, and create various types of graphs and charts.
One of the strengths of this book is its practical approach to learning. The authors provide numerous examples and exercises to help readers apply the concepts and techniques discussed in the book. They also include tips and tricks for working efficiently with SAS and R, saving time and avoiding common pitfalls.
Whether you are a beginner or an experienced data analyst, “SAS and R: Data Management, Statistical Analysis, and Graphics, Second Edition” is a valuable resource for mastering these powerful software tools. It is a must-have for anyone looking to enhance their data analysis skills and make informed decisions based on data.
Overall, this book is highly recommended for anyone looking to improve their data management, statistical analysis, and graphics skills using SAS and R. Grab a copy of “SAS and R: Data Management, Statistical Analysis, and Graphics, Second Edition” today and take your data analysis to the next level!
#SAS #Data #Management #Statistical #Analysis #Graphics #GOOD, Data Management
Foundations of Statistical Natural Language Processing
Foundations of Statistical Natural Language Processing
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Foundations of Statistical Natural Language ProcessingStatistical Natural Language Processing (NLP) is a branch of artificial intelligence that deals with the interaction between computers and humans using natural language. It involves the development of algorithms and models that enable computers to understand, interpret, and generate human language.
The foundations of Statistical NLP lie in the field of linguistics, computer science, and statistics. Linguistics provides the theoretical framework for understanding the structure and rules of language, while computer science offers the computational tools and techniques needed to process and analyze large amounts of text data. Statistics plays a crucial role in NLP by providing the mathematical methods to model and infer patterns in language data.
Key concepts in Statistical NLP include:
1. Corpus: A collection of text data used for training and testing NLP models. Corpora are essential for building language models and understanding the statistical properties of language.
2. Tokenization: The process of breaking down text into individual words or phrases, known as tokens. Tokenization is the first step in NLP preprocessing.
3. Language Models: Statistical models that capture the probability of word sequences in a given language. Language models are used for tasks such as speech recognition, machine translation, and text generation.
4. Part-of-Speech Tagging: The process of assigning grammatical categories (e.g., noun, verb, adjective) to words in a sentence. Part-of-speech tagging is essential for parsing and understanding the syntactic structure of text.
5. Named Entity Recognition: The task of identifying and classifying named entities (e.g., persons, organizations, locations) in text. Named entity recognition is crucial for information extraction and text summarization.
6. Sentiment Analysis: The process of determining the emotional tone or sentiment expressed in a piece of text. Sentiment analysis is used in applications such as social media monitoring and customer feedback analysis.
By understanding these foundational concepts and techniques in Statistical NLP, researchers and practitioners can develop more advanced NLP applications that can process, analyze, and generate human language with greater accuracy and efficiency.
#Foundations #Statistical #Natural #Language #Processing
