Statistics book reddit

Statistics book reddit. It is a place to ask, share, and learn about any A Level subject, exam, or plan. They're also very good and classical books to learn. Statistical Rethinking by McElreath is a good introductory book for Bayesian statistics and probability. A students guide to Bayesian statistics - Ben Lambert. Doesn’t explain WHY things are ideamotor. " Discussion. engelthefallen. As the title says, I wonder if anyone has experience with good matrix algebra books with an emphasis on applications in statistics for self-study? I have looked over linear algebra books for a broader science audience (e. al. Perhaps look on Library Genesis for these, as well as any books on "Mathematical Statistics". I suggest getting at least two used textbooks. To actually learn the stats, you can use the following two books in the following order: Modern introduction to statistics: https://openintro-ims. If you need a "bible", then that is an admission you don't have a clue and need to get one. If you want a bit formal treatment then Introduction to Probability Book by Dimitri Bertsekas and John Tsitsiklis is good. Gelman et. Prove Theorem 10. It's small, short and cheap. However, I would still use other resources in addition to Statquest if you can. Bala0406. 2nd Edition. If you're actually looking for applications of computer science to statistics, it's hard to go wrong with reading about Markov Chain Monte Carlo. The book not only teaches you the theory behind statistical methods, but also demonstrates their The 20 best statistics books recommended by Will Kurt, Kirk Borne, Stanley Lazic, PsycCRITIQUES, Andrew Gelman and others. Hairy_Hareng. Intro to Data Science: The Introduction to Data Science class will survey the foundational topics in data science, namely: Data Manipulation, Data Analysis with Statistics and Machine Learning, Data Communication with Information Visualization, & Data at Scale -- Working with Big Data. ) and goes through distribution theory, convergence theorems, all the way to I would recommend Casella and Berger, mainly because I think it's required reading by anyone doing statistics. The book is quite interesting but also quite technical. r/alevel is a subreddit for A Level students and aspirants. Maybe something like Rosenbaum's Design of Observational Studies or the Imbens book; I'd strongly recommend the former. Then look at online lectures. - Introduction to Mathematical Statistics by Hogg et al. It helped me immensely throughout my course in University but alas, it's not enough now. The book is titled “Probability and Statistics for Engineering and the Sciences. This used to be in textbooks*, but that sub (along with textbookrequest, which shadowbans/censors free links etc, has been taken over by a greedy bookseller who has removed this message, and only wants to make money. Introduction to Probability models by Sheldon Ross. Don't ask…. PM to report issues and my human will review. Ugh, not really a fan of "casual books". The problems in the book are typically tougher than the actual exam which is usually a good thing when studying. . Please do not message asking to be added to the subreddit. • 1 yr. From the description: Most textbooks on regression focus on theory and the simplest of examples. Popper. Ask a question about statistics (other than homework). We are susceptible to all sorts of odd thinking tendencies that are chronicled in "Thinking fast and Slow". is a definitive approach to applied statistics using Bayesian methods. For the basic fundamentals, and something that isn’t overly mathematical, I would suggest Jay L. Books: Applied Spatial Analysis with R. I'm looking for a good book to teach myself the concepts of statistics, mainly for Data Science and ML, but I'm having a hard time choosing between 'Think Stats' and 'Practical Statistics for Data Scientists' (both O'Reilly books). Add a Comment. Each chapter also has a section devoted to assumption-checking and pitfalls (i. I found the following books recommended but was not sure what the benefits or differences of each of them were of which is the best to read for my purposes, any help is appreciated : - All of Statistics by Wasserman. Not sure if they also cover the basics of inference in 6. If you're looking for a good primer on applied statistics, I'd say grab any of the "Use R!" One of my favorite books is "Using Multivariate Statistics" by Tabachnick & Fidell, 2007. Thank you! Yes, you can lie with statistics, as in a marketing message. We used "Fundamental Statistics for the Behavioral Sciences" by David C. Dubner and Steven Levitt, Well if you're interested mostly in how statistics is applied in CS to solve problems I would recommend either of those ML books I linked. For my two statistical learning courses, they share the same recommended books: Hastie, Tibshirani, Friedman (2009) "Elements of Statistical Learning: Data Mining, Inference, and Prediction. Today I'm reading Intro to Statistal Learning and Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow. Practical SQL. StatQuest is a YouTube channel and is probably the best place to start. Quantitative Social Science: An Introduction by Imai. ISBN- 978-1-337-90106-2. 19 Prove part (b) of Theorem 9. Data Analysis with R: Exploratory data analysis is an Fields book is perfect for a first introduction to statistics before diving into a mathematical statistics book to learn the math. Real statistical problems, however, are complex and subtle. 91K subscribers in the AskStatistics community. _ I have extensively used: (1) All of Statistics, Larry Wasserman (2) Casella and Berger (3) Mathematical Statistics and Data Analysis, Rice. If anyone has unused code (of any edexcel endorsed book) or if code is used then if you are fine with providing Pearson Active Learn account details, I could make pdf of that book. They give you a deep sense of how to approach a dataset and decide what tools to use to analyze it. , Patton MQ. We are 4 weeks into the school year and I am just utterly lost. This book teaches the mindset better than anything else I've ever read. really interesting video yes. "The Art of Statistics" by David Spiegelhalter. I'm just looking for a book that I can read that will actually help me understand wtf is going on in class. also am not afraid of maths or heavy maths usage, i kinda likes when they try to show intuition of thought via mathematically. AS (Year 1) books are called "Student Book 1" A2 (Year 2) books are called "Student Book 2" (Physics books of Miles Hudson latest) All books in this folder: https Naked statistics: stripping the dread from the data, All of statistics a concise courses in statistical inference, Think stats, Modern Mathematical Statistics with Applications, Statistical Rehinking are some of the best Statistics books. For sports, you'd need to pick a specific sport to read sport specific books. But first wanted to learn probability and statistics and get a good grip on the same. Qualitative Research and Evaluation Methods. So that would be my suggestion. It's the same course for the most part. Now, this time there are several good online courses and books are available for learning probability and statistics. The red-covered book is near useless, unless you're a junior (undergraduate) or don't want to actually understand the material. Rule 5 No Bigotry: Including but not limited to: Racism, Transphobia (including xenogender hate and transmedicalism), Enbyphobia, Homophobia, Islamophobia, Antisemitism, and Gender Exclusion. Latest editions also unlock a series of video lectures by the author. I have no idea if it is any good for its purpose, though. Then you can get more into statistics: Savage, The Foundations of Statistics. Or even Peter's Elements of Causal Inference. ISBN 979-8558877953. , be sure to look for X, Y, and Z) 10. Wasserman's All of Statistics has fewer technical details but gives a rapid panorama. I hope I can find something akin to that about Field has a nice introduction to statistics where you also learn how to apply the knowledge with SPSS: "discovering statistics using SPSS". Forecasting: Principles and Practice is one of my favorite resources on the topic. ”. The goal is not to have read it, nor to have finished the book, but to understand the topic better - and slowing down and doing the work will help with that. But the problem is I didn't get the intuition, it was just picking code from the internet or implement a model in tf or pytorch. But that’s a false dichotomy: models are often used for exploration, and with a little care you can use visualisation for confirmation. The first book is a great beginning into to Bayesian stats with no prob or stats prerequisites. 00 from amazon. ago. Hi everyone, I am trying to enter the field of data science. We used this textbook for the course: Devore, JL and Berk, KN (2012) Modern Mathematical Statistics with Applications. Serious Stats (by Baguley) is an excellent overview of statistical methods, but it assumes you've put in some time previously. If you're going toward data science, Python is really your best bet. However, it's a very basic book so it may or may not be what you're looking for. Good books on statistics. Finding a project you very much want to complete is the Python for Data Analysis is definitely the best book to start if you want to use Python. 2. Springer. " James, Witten, Hastie, Tibshirani, "An Introduction to Statistical Learning: with Applications in R". Rigorous proof (by the standards of mathematicians) is neither required nor even desirable. You can discuss and share content here; We are a community that enjoys helping each other, so feel free to ask questions as well. Hastie and Tibshirani also have a free online course through Stanford that covers the book. The art of statistics: How to learn from data by Speigelhalter Statistics by Freedman and Pisani Introduction to probability, statistics, and random processes by pishro-nik Practical statistics for data scientists: 50 essential concepts using R and python Statistical inference by casella statistics in plain english by Urdan $39. As a physics student, I find the book completely impenetrable. • 7 mo. This is not a book about the theory of regression. • 3 yr. Can I have some opinions or reviews 2. Go slowly - it's not a novel, and spending time on details is what helps you understand better. It provides a deep level of mathematics but also has a lot of practical resources such as SAS/SPSS syntax and output. 2. The key difference is how often do you look at each observation: if you look only once, it’s confirmation; if you look more than once, it’s exploration. E-M algorithm), and a wide variety of other topics. Tim Harford has done a lot of work to try and Introduction to Statistical Learning is one of the better free resources covering a lot of the algorithms used in data science problems. The book is called "Introduction to Mathematical Statistics. Sort by: Top. app Not quite just about statistics, but I really like The Logic of Scientific Discovery, by K. Machine Learning: An overview with the help of R. also, learn R. If you're going to be doing more experiments and The very fact that you are looking for a "bible" means you don't have the independent knowledge to tell correct statistics from nonsense. Let me know if I got it wrong All of Statistics: A Concise Course in Statistical Inference. ( u/beaverteeth92 ) Assessing the accuracy of the maximum likelihood estimator ( u/Jimmy_Goose ) Items with more mixed reviews and/or non-mandatory reads: Nonparametrics: Wasserman "All of Is statsquest a great resource to learn statistics for ml and data science. It gives a nice introduction to a good portion of the topics associated with forecasting time series in a fairly accessible manner. Any "theorems" in statistics require such narrow and precisely defined hypotheses that they only apply in very restrictive situations (for instance, the Neyman-Pearson theory of hypothesis testing). For a course like this, my uni requires a full-year course in calculus-based probability and statistics. Great replacment for Statistics 101: The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics by Kristin H. She uses Data Analysisfor a bunch of hilarious tasks. BTW there's very little overlap between BDA and Gelman+Hill. 18. Once you've got a handle on the basics ( z, t, and F tests as well as correlations and linear regression), you're ready to take this one on. That often ends up being Bayesian but any model fit with maximum likelihood also falls under the scope of his book. He defines probabalistic as any model that can be reformulated in terms of a probability distribution and its parameters. Howell. There are examples and sample code, but it is all in R. Deep Learning with R - for an introduction to deep learning. After that, it depends what you need to know next. 14 need to be filled in, and the second part of the theorem needs to be proved. The 2nd edition is available for free online, and the 3rd edition can be bought on Amazon . I love OpenIntro Statistics. Maybe you could skip it and go straight to Gelman's Bayesian Data Analysis if you're really up for it. Well explained, tons of exercises (and well varied exercises). While there is no harm in buying cheap textbooks, all options should be given, esp The SPSS base v25 license is $1250 per year. " I imagine a book called "Introduction to Mathematical Physics" [Edit: Or, even better, "Introduction to Mathematical Physics and Engineering", since I think it's fair to say Engineering:Physics::Data Analysis:Theoretical Statistics] would deal more with integrals and derivatives than tensor fields and novel algebras, would have Bayesian Data Analysis by Andrew Gelman et al. also, learn Statistics with Julia. This book and the associated lectures are excellent. g. His book however still is a great source for learning statistics even if you do not use the SPSS chapters. Since both courses list them, I will probably lean If you're gonna use SPSS a lot, Rebecca Warner's books! From bivariate to multivariate. _ The presentation style is a great combination of intuition, history, and proofs (up to a page or two) of key concepts. Very readable, starts slow and builds up with examples. Question. I've listed the books you have mentioned below in case if anyone needs it. " It has the tone you seek but it is more about base R. Share. The second is a great into to Bayesian networks (despite the name there’s nothing Bayesian about them really) and causal analysis. One book I really enjoyed when learning statistics at uni was Grimmett and Stirzaker’s “probability and random processes”. An Introduction to R for Spatial Analysis. A lot of undergrad statistics classes use Andy Field's Discovering Statistics Using R, which is a very good introductory and non-technical textbook. Good luck! Mathletics by Wayne L Winston. The Signal and the Noise by Nate Silver might work for you. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python (2nd Edition) these two are in python and gratis: Think Stats. But to really do statistics you need not only the mathematics but the art of modelling. 3. It's just ok, but it's not particularly rigorous, and it really focuses on R Commander. Focus on learning foundational statistics really well and you’ll be able to use any technology. But I personally read this book "The Art of Statistics: Learning from Data" by David Spiegelhalter, this book helps me a lot. •. Reply. OpenIntro Statistics. 1. 041). Shonkwiler and Mendivil's Explorations in Monte Carlo Methods is the book I aim all my math, CS, and physics students at for this material. Then, yes "R for Data Science" is great to get some bearing. I was recommended an amazing book by Joseph K Blitzstein and Jessica Hwang "Introduction to probability". By knowing the basics of the material it is easier to focus just on the math instead of having to l arm both the math and the general principles. The Art of Statistics, David Spiegelhalter. Should be required reading for every professional data scientist, but seems like most people haven't even heard of this book. You can also find resources, memes, and friends on r/alevel. It's getting long in the tooth but I enjoyed "The Art of R Programming. TrueBirch. I agree with u/oFabo, ISL is a good introduction to machine learning Fields book is perfect for a first introduction to statistics before diving into a mathematical statistics book to learn the math. So if you decide to branch out at some point to learn more about statistics you're gonna find reliable academic literature making use of Python hard to come by. 9. Yes, it has nice explanations and is a great place to start :) It is helpful for understanding some of the concepts and breaks concepts down tremendously. There are no statistics "bibles". Others have recommended a lot of resources already, so I'll just suggest this one: Seeing Theory. This is the official subreddit of the Minecraft modpack RLCraft. netlify. Don't solicit academic misconduct. The MIT course is great. Practical Statistics works better as a quick and friendly reference book to remember statistical concepts you have already been exposed to and now want to implement in either R or Python. It’s short and so far its explanation of combinatorics is much too brief. bakwasmatkaro. However, most statistics is to find an answer, a true answer. , Lay and Strang), but believe a more specialized book would be beneficial as I aim to improve my statistics training. If anyone can suggest a better book (or better books) that don't cost a hundred dollars, I'd really appreciate it. They have the free pdf, but the color textbook is only $20. _ /r/Statistics is going dark from June 12-14th as an act of protest against Reddit's treatment of 3rd party app developers. If you want to go into machine learning, Introduction to Statistical Learning with Applications in R, and Elements of Statistical Learning seem to be the gold standard. They are all health/med focused, but can easily be adapted to whichever domain you are in. Devore. Also the author gives real world examples that you could relate , so it's easy to remember series of books. It encapsulates a ton of knowledge that most people only learn through years of project-based ecperience. "How to Lie with Statistics" is a good book, but it does a lot to shake the confidence in statistics in general without really promoting the importance of them. Reply reply. He has books for SPSS, R, SAS and such as well. In any case, you can't read an intermediate Statistics book without having math chops, so just be ready. Kevin Murphy's book is great but not necessarily Bayesian in all contexts. I highly recommend FeedConstruct for anything related to odds feed and sports data providing. It's easy to lie with statistics, but it's even easier to lie if everyone thinks that statistics are useless. 70 - A Modern Introduction to Probability and Statistics: Understanding Why and How (Springer Texts in Statistics) I am a bot here to save you a click and provide helpful information on the Amazon link posted above. Our book is “A First Course in Probability” 10th edition, by Sheldon Ross. (1) was the greatest help for me: it covers many topics, gives intuitive as well as thorough explanations and has good exercises. Spatial Epidemiology Notes - Applications and Vignettes in R. Will Kurt - Bayesian Statistics The Fun Way , This one contains real world examples and how stats and prob is used to solve these problems. I have a great background in vector calc, PDE's, LA etc etc and I usually don't struggle in courses. Guerrilla Analytics: A Practical Approach to Working with Data. Starts with very basic probability- stuff you would have probably covered pre-uni/1st year of uni (conditional probability, independence etc. I wouldn't recommend using Discovering Statistics Using R; I'm assuming that's what others are referring to. I haven't read (except for the first two), but they have been recommended to me in the past: - "The Signal and the Noise: Why So Many Predictions Fail--but Some Don't" by Nate Silverman, - "Freakonomics" by Stephen J. Though this is slowly changing, it's something to keep in mind. I needed help with identifying the best resources for a rookie like me to learn about probability and statistics (classical books, online courses, sites, etc). And that means you are in trouble. 12. I am not affiliated with Amazon. ISBN 978-1790122622. Think Bayes. This book will give a good perception into statistics with real world examples. Introduction to Statistical Learning (ISL) and Elements of Statistical Learning (ESL). “Smart Baseball” by Keith Law. Spatial Statistics & Geostatistics. Probably because they brought out a second edition recently, so they could be updating the course. I also recommend using more than one resource as supplements when studying like Khan Academy, which can help nail down some concepts better than the review book. Data Analysis: A Model Comparison Approach is also a More basic than Casella and Berger: books like Mathematical Statistics with Applications, Wackerly, Mendenhall and Scheaffer (there's a number of other texts at a similar level that are quite good; your university library probably has 5 or 6 reasonable options). . until now,I have approached machine learning from a programmer's perspective and did some projects. It covers probability and distributions and such. General: Wasserman "All of statistics" ( u/rosenjcb ) Papers: A conversation with George Box. Thanks! the economics-for-business stats course where I am uses Anderson, Sweeney, Williams, Camm, Cochran, Fry, and Ohlmann. - Wasserman, All of Statistics; Casella and Berger is similar: 9. MOD. R in Action is pretty good. 20 Some of the details of the proof of Theorem 9. Source: I've taught a graduate course in time series out of Brockwell and Davis, and it's a solid book. Bishop Pattern Recognition and Machine Learning. Larry Wasserman, All of Nonparametric Statistics. No fluff and very clear presentation. Good places to start for that are Gelman and Hill and Friedman, Hastie and Tibshirani. They are just perfect for understanding. All of the examples are data you might find in experimental psychology (albeit simplified), and the code and data files are available first use. _This community will not grant access requests during the protest. another channel jbstatistics is also pretty good. Practical Statistics for Data Scientists this one is good, reminds me I should probably brush up on my stats, especially Bayesian. Joy of X was great and is very similar. Python Data Science Handbook. 10. 3rd ed. ( u/psychometry ) Statistical modeling: the two cultures. This was recommended to me by ours stats guy at work. Seconding ISL, R for Data Science, and Python Data Science Handbook - great resources. Priestley is still my go-to for theoretical discussion, but you pay for that with much less clarity. But no. I'm fond of The Signal and the Noise by Nate Silver. George Box, Statistics for Experimenters. Jarman. Bonus for qualitative methods, mixed methods research or survey sampling specifically The Book of Why by Judea Pearl might be worth a look. +1 for stats quest. One being an introductory stats/probability book (Sullivan III is one I know offhand) and the other being a probability theory textbook (the Bertsimas Tsitsikas Intro to Probability is a well-known text). Wackerly, Mendenhall, & Sheaffer, Mathematical Statistics with Applications. The standard text on formal probability theory is Billingsley, which might be a reasonable start since probability theory is the foundation of statistics. “The book” by Tom Tango. The 20 best statistics books recommended by Will Kurt, Kirk Borne, Stanley Lazic, PsycCRITIQUES, Andrew Gelman and others. My program used Andy Field's "Discovering Statistics Using SPSS" and I found it to be a very useful, informative and somewhat entertaining book to use for learning and studying. save_the_panda_bears. It lets you explore probability and statistics concepts interactively and visually, which will help with developing intuition. O'Reilly's think stats VS practical statistics for Data Scientists. Larry Wasserman, All of Statistics. Once you follow every step, make sure you know why it was done. The first for general statistical theory and application, the latter for developing a deeper intuition and understanding of linear regression which is the workhorse for most social science. UPDATED SITES AND SOURCES FOR FREE BOOKS. Recommendations for probability and statistics course. For an introduction to R, I recommend 2 books: "The Book of R" by Tilman Davies. Hi, i wanted to look for a book to learn bayesian statistics which also try to put effort in how to think in term of bayesian statistics and how its different from Frequentist ones. Neither of these books has much in the way of real data examples. I am helping design a 2 part stats course for psychology graduate students. - Mathematical Statistics with Applications by Wackerly et al. Hi all! I am currently in a course called Intro to Mathematical Statistics 1. Any books that focus on understanding probability distributions, hypothesis testing, sampling and methods of conducting experiments? There is an amazing book by Will Kurt - Bayesian Statistics The Fun Way - This covers all the topics you have mentioned. For example running a frequency distribution on “Yo Momma" Jokes”on the net or uses probability to jokingly track Big Foot. I want to change that and now I want to understand research I still use the class material today! Below are some of the books we used. I agree with u/oFabo, ISL is a good introduction to machine learning The funniest and most engaging book I have read was this : Will Kurt - Bayesian Statistics the Fun Way. 's Bayesian Data Analysis focuses more on An Introduction to Statistical Learning - for an introduction to statistics and machine learning. e. Upvote if this was helpful. It was my first book in the area and still one of the bests. PM to opt-out. Book was hilarious. Education. It is in general about the scientific method, but it also has a part on probability and the idea of Falsifiability is strongly related with hypothesis testing. From there I'd get something like "R Graphics Cookbook" to keep you engaged. A lot of theoretical results are presented without proof in this book, but mostly so they can be discussed for applications' sake. I also got the book ("Introduction to probability" by Bertsekas and Tsitsiklis) for this course and I second what another user said: the book is very good. It's really for the interested reader rather than the textbook market. MLE is just finding the maxima of a function. A while back I asked this subreddit about good books on probability theory. /r/Statistics is going dark from June 12-14th as an act of protest against Reddit's treatment of 3rd party app developers. Mostly Harmless Econometrics by Angrist and Pischke. Both will cover basic probability distributions, linear models, more general models, classification, algorithms (e. If you are a student there is a discounted version, but once you are out of school plan on paying that much or more if you want to use it at work. Please make sure to read our subreddit rules. Statistics for Business and Economics 14ed, Cengage (2019). It focuses on getting the intuition right, then introduces the math, unlike many other textbooks. While you can lie (or mislead) with statistics, the truth is there if you are willing to look for it. I do not think this is a good book for explaining the subject matter. If you want a book intended for math students Probability and Random IMO, this book is much better than Casella & Berger. Most of my personal R code for spatial analysis is largely uncommented but I will share some resources I base a lot it off of. I primarily used Barron’s for review. The teacher will loosely follow "Introduction to Probability and Statistics for Engineers and Scientists" by Sheldon Ross; it is a very expensive book and the reviews from other students and from Amazon are mostly negative. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Behavioral Data analysis with R and Python - Florent Buisson. Deep Learning Models explored with help of Python Programming. Don Dillman has a great book. The most important concept is conditional probability and conditional expectation. bp im cg ok wv ya qq ji hj zk