(Part 2) Reddit mentions: The best biostatistics books

Schrödinger's book has been reccomended to me as has [On Growth and Form by D'Arcy Wentworth Thompson] (http://www.amazon.com/Growth-Form-Complete-Revised-Biology/dp/0486671356).

The original was in like the early 1900s but updated versions should be fine. On Growth and Form is more for those wondering about mathematics in biology though.

I'm not too clear on what angle you want, but often you'll find that Bio texts are woefully out of date in many areas if you are looking at something in particular.

The Cell is also a good book (and free as an electronic resource at many universities).

For Medical Statistics my suggestions are:

• Medical Statistics by Martin Bland. A very thorough book explaining all the common statistical tests and concepts you will be using in any medical research. Fluid writing and not a chore to read, it even has a small chapter regarding Bayesian Statistics. You probably should complement it with a book about R to learn how to do the statistics you learn here with a program :)
  
  
  
• Biostatistics for Medical and Biomedical Practioners Another good book which overlaps with the other one except for replacing Bayesian chapters with a more in depth treatment of non-common statistical procedures such as Poisson Regression and the such.
  
  I think you will be well served with one or another choice, if you need a book suggestion on a specific topic of Statistics just say so as well :)

This was a textbook for my undergraduate biostats class that has carried me through several papers in medical school. It's very short and easy to read and has hardly any math (hence the title- Intuitive biostatistics: A nonmathematical guide to statistical thinking). I highly recommend it.

https://www.amazon.com/Intuitive-Biostatistics-Nonmathematical-Statistical-Thinking/dp/0199730067/ref=pd_sbs_14_t_1?_encoding=UTF8&psc=1&refRID=F2BN9TD974KS7E9N709J

This is the book I taught myself from. I do my own homebrew biostats for my projects using JMP and it isn't too difficult, at least for the simple things.

To me, biostats is really something worth learning the bare bones of and retaining forever. Few doctors understand it enough to argue their point and you can nuke pretty much any evidence-based medicine talk from orbit if you can tell the difference between nominal and continuous data and know what a p value actually represents.

I'd suggest R for stats software. The only problem is you don't purchase it, so if you're determined to spend money it won't suit that requirement.

https://www.amazon.com/Biostatistics-Introduction-Statistics-Through-Biological/dp/146141301X

(I haven't used this book; it looks like lecture notes and data sets to go with it are here)

There's a free stats with biological examples book on CRAN too

https://cran.r-project.org/doc/contrib/Seefeld_StatsRBio.pdf

(I also don't know about its quality, I haven't used it. It's about ten years old by the look).

Depends what sort of survival analysis you're doing. I mostly do competing risks, and I like Multivariate Survival Analysis and Competing Risks by Martin Crowder. It's surprisingly funny!

https://www.amazon.com/Principles-Biostatistics-CD-ROM-Marcello-Pagano/dp/0534229026 This one's nice and might be a good match for you

Dicing with Death by Stephen Senn is a nice book about medical statistics. He doesn't assume statistical knowledge so he explains all of the underlying concepts. I found it very reabable and the anecdotes and historical stories interesting.

Thank you for these references. Spiegelhalter is one of my favorite authors on these subjects, his expositions always seem straightforward. Actually, his book on clinical trials is on my shelf.

you can check the following books

1. Bayesian Methodology: An overview with the help of R software: Tool for data science professionals
   
   https://www.amazon.com/dp/B07QCHTR54
   
   
2. Essentials of Bio-Statistics: An overview with the help of Software
   
   https://www.amazon.com/dp/B07GRBXX7D

I really liked this book, for one. It covers point pattern analysis, which I suspect will interest you.

Well if it is stats you are looking for then the standard in my department is Gotelli's A primer of ecological statistics. For more general biological stats look at Whitlock & Schluter and Quinn & Keough. Also don't forget the classic Biostatistical Analysis.

Not what I'm saying, perhaps I misunderstood your question? Making sure the results say there's no difference isn't the same as testing whether the difference is small enough, AKA testing for equivalence.

I have no direct experience with this type of design but there seems to be a whole book dedicated to the issue so it might not be such a simple matter.

Zar's text might be helpful. https://www.amazon.com/Biostatistical-Analysis-5th-Jerrold-Zar/dp/0131008463/

IMO repeated measures and longitudinal data are extremely underappreciated topics for data scientists, which you encounter if you work with any kind of data where you record data on "subjects" or "users" over time. The most accessible and best resource I have seen on this topic is Applied Longitudinal Data Analysis by Singer and Willett, which is written for a graduate-level social science audience and has a light touch on the math but goes heavy on building intuition for random effects models and survival analysis. There is a cache of examples in R for it here. There are more mathematical treatments of this from a biostatistics perspective, such as Analysis of Longitudinal Data, but I would start with Singer and Willett.

This is probably your best bet. Still, realize that even for veteran researchers, ideas rarely lead to successful experiments.

I'm going to be blunt: your research idea has probably already been investigated or has a major flow. Note that major flaws are often just weaknesses inherent to large studies (particularly involving nutrition). When a medical researcher comes up with a hypothesis, most of the time they can't actually test it due to these limitations. Usually the problem is that in order to find a significant effect, you need a much bigger study than you can reasonably afford.

Along with doing the reading mentioned by UncertainHeisenberg, I would recommend reading this book. It takes you through the entire process of planning and designing a study and is written in plain English.

Figure out what kind of study you would need and do the sample size calculations presented in the book.

https://www.amazon.com/Growth-Form-Complete-Revised/dp/0486671356 Maybe?

Fabulous Fibonacci Numbers?

Geometry of Art and Life

The Curves of Life

(ps "throes" unless the crockery is suffering...)

I use R almost exclusively for my spatial analysis. Sometimes I use command line gdal stuff. Here is the book I used to get started: http://www.amazon.com/Applied-Spatial-Data-Analysis-Use/dp/1461476178

There are PDFs online.

Have you tried the packages spdep, spatstat, gstat? In the class I took on this subject, we used these packages along with maptools and GISTools to avoid Arc entirely. This book was our reference:
https://www.amazon.com/Introduction-Spatial-Analysis-Mapping/dp/1446272958

If I'm not mistaken, the package spdep was developed by the authors of these books:
https://www.amazon.com/Applied-Spatial-Data-Analysis-Use/dp/1461476178
https://www.amazon.com/Spatial-Statistics-Geostatistics-Applications-Information/dp/1446201740

Were you instructed to use geoRglm?