(Part 2) Top products from r/AskStatistics

You need Intuitive Biostatistics. It's written specifically for scientists and medical professionals without a math background to learn how to interpret data in scientific papers. I'm a PhD student in cell bio and it is invaluable. The only thing it might not cover super in-depth is probability, but it pretty much covers the gamut of everything else without delving into the mathematics behind everything. The guy who wrote it also makes the Graphpad Prism software, which a lot of bioscientists use for data analysis.

For the next step, I highly recommend JB Statistics videos. They are the best moderate math level explanations for the common concepts I have yet come across. Especially watch the sampling distribution playlist several times to fully comprehend the CLT.

Some other advice I wish I was told before I started learning statistics: 1) Statistics is the inverse of probability. 2) Statistics is unintuitive and hard to understand. You will have to read some things dozens of times and from different people's wording to fully understand a concept (looking at you, p-values). Best of luck.

"All models are wrong, but some are useful." - George Box

From my experience, it generally appears to be that it's always possible to have more information from which to source; however, barriers such as cost and time often prohibit you from being able to do so.

I think there will always be a better statistical model out there; I don't see anything wrong with updating your model over time as you come across more information or more effective techniques to do the analysis you would want.

I think these two sources should interest you. (1) (2)

Also Nate Silver's Signal and the Noise might be worth a read. It's not a technical manual, but it will give you some things to think about regarding the use of models in a wide variety of fields.

Hope that helps!

> I'm trying to create a forecasting model to predict ...was hoping someone could point me in the right direction with documentation, vignetts, papers that could help me figure this out, or ideas on how you would approach this problem.


Well you are in luck, there is an entire book that answers what you are trying to accomplish, and spells out the steps how to do it.


https://www.amazon.ca/Introductory-Time-Paul-S-P-Cowpertwait/dp/0387886974/ref=sr_1_1?keywords=introductory+time+series+r&qid=1574873740&sr=8-1

It may also be available for free through other means.

The specific chapters you want will probably be two and three (correlation, and forecasting strategies), though the rest of the book will likely be beneficial as well.

> I have some but I am not super well versed in the matter. (with regards to R and programming skills)

If that’s the case, it might be difficult to implement what is in the book. It assumes you know R at probably around a low/mid level. But if you power through you should be able to get some good out of it.

Also be aware that according to some of the reviews. the source code and data does not appear to be online any more. That makes direct copy and pasting the examples difficult, but if you are applying it to your own data set only, that might be less of an issue.

Its a pretty good book (its the one I read on forecasting originally), and it will get you going in the might direction but its not perfect and you might be able to find a more current one with available sample data. Read the reviews.

Get started and let me know how it goes :)

EDIT

All of the data, as well as functions and R code are indeed available online. Just had to look for it:

http://www.maths.adelaide.edu.au/emac2009/

Edit 2:

Here is are a few newer books as well. I skimmed it quickly and it may or may not be better:

http://db.ucsd.edu/static/TimeSeries.pdf

Again, you are looking at autocorrelation and forecasting. This one may lean a bit more on the math side.

/EDIT

I TFed an intro undergrad course that used Alan Agresti's Statistical Methods for the Social Sciences. I didn't read much of it, but the students seemed to like it. He also has another book that's probably also pretty good. The intro course for non-stats students at my graduate school is Applied Statistics for the Behavioral Sciences, which might also be worth a look. If those are too technical or hands-on, then the "for Dummies" book might also be a good choice - it's in very plain language and tries to keep things relevant to real-life examples.

Many of the bigger-picture "whys" become more apparent when you have a solid grounding in probability theory and the theory behind statistical inference, though. Some of them don't have very satisfying answers, either (Q: Why p = 0.05? A: Convention). In my opinion, the more you understand statistics, the more you realize it's less about finding exact answers than it is about quantifying imprecision. That can be hard for a layperson to wrap their head around!

Since you are still in college, why not take a statistics class? Perhaps it can count as an elective for your major. You might also want to consider a statistics minor if you really enjoy it. If these are not options, then how about asking the professor if you can sit in on the lectures?

It sounds like you will be able to grasp programming in R, may I suggest trying out SAS? This book by Ron Cody is a good introduction to statistics with SAS programming examples. It does not emphasize theory though. For theory, I would recommend Casella & Berger, many consider this book to be a foundation for statisticians and is usually taught at a grad level.

Good luck!

There is a bunch of engineering stats books out there. The one we teach out of at my uni is the one by Devore. I think it does a good job of teaching what it does. I know Ross has an engineering stats book out there, and so does Montgomery, and they are both people who have written good books in the past. The one by Ross seems to have some good topics in it from reading the table of contents.


Also, you probably want to pick up a regression book. I like the one by Kutner et al., but it is ungodly pricey. This one has a free pdf. I don't like a lot about it, but the first few chapters of every regression book are pretty much the same.

If you want to go deep into statistical theory, there is Casella and Berger as well.


For programs, I know MATLAB has a stats package that should be sufficient for the time being. If you want to go further in stats, you might want to consider R because it will have vastly more stats functions.

I know this is a boring suggestion, but nothing beats the old, venerable Schaum's Outlines for their combination of problems, solutions, and inexpensiveness. If you are just starting, perhaps start with this one, and once you get some exposure to the basics you'll have a better idea of what you might want to pursue next- perhaps the next step would be analysis using a computer instead of by hand.

Lots of us have free YouTube videos on the basics that you can reference if/when you need them as you go. Try me or Kahn Academy, there are many others. Let me know if this idea doesn't fit with what you had in mind, and I can try to point you in a different direction.

I'm a big fan of The Drunkard's Walk. Also, the author Leonard Mlodinow (PhD in physics from Berkeley) has a number of other really good books on different scientific fields.

"Numbers Rule Your World" by Kaiser Fung offers a great explanation of probability and statistics with lots of real world examples and applications. Numbers Rule Your World: The Hidden Influence of Probabilities and Statistics on Everything You Do https://www.amazon.com/dp/0071626530/

Incorporating expert opinion into a Bayesian model is usually done through prior distributions instead of an additional feature. (As an aside, doing so is considered subjective Bayesian inference versus objective Bayesian inference).

As a quick overview, Bayesians usually make inference on the posterior distribution - a combination of the prior distribution (in your case, expert opinion), and the likelihood. As a really basic example, consider a setting where you have data on MI outcomes (no covariates at this point) - a series of 1's and 0's. A frequentist would likely take the mean of the data. As a Bayesian, you would consider this binomial likelihood and likely combine it with a beta prior. The default (non informative) prior would be to use a beta(1, 1) distribution. However, if in a prior dataset, you had observed four patients, three with an MI and one without, you could use a prior of beta(1+3, 1+1). See here for more details on beta-binomial.

In the above example, it's easy to incorporate prior information because we used a conjugate prior. While probably not exactly what you are doing for your dissertation, here's an overview of a conjugate prior with a linear regression from wikipedia. There are many more resources online for this that you can find by searching for something along the lines of "bayesian linear regression subjective conjugate prior". For a more detailed (introductory) overview of bayesian statistics, check out this book.

To be honest, as much as I'm a Bayesian, I think that creating an automatic model that incorporates expert opinion will be really difficult. Usually, subjective priors are chosen carefully, and there not always as interpretable as the beta-binomial posterior presented above. I think this goal is possible, but it would require a lot of though about how the prior is automatically constructed from a data set of surgeon's predictions. If you have any followup questions/would like more resources, let me know!

Edit: I guess I never really addressed the issue of predictive models. However, the difficult part will be constructing the prior automatically. If you can do this, predicting outcomes will be a simple change to make, especially in the case of linear model.

Here you go. Free shipping, too.

I like Paul Allison's mini-book on Missing Data http://www.amazon.com/Missing-Quantitative-Applications-Social-Sciences/dp/0761916725. Additionally, for both theoretical and practical considerations, I also like John Graham's Missing Data: Analysis and Design https://methodology.psu.edu/pubs/books/missing. If you are using R, you may want to look into using MICE (multiple imputation using chained equations).

There is a good book about teaching statistics by Andrew Gelman and Deborah Nolan: Teaching statistics: A Bag of Tricks.

Get this book: Guerrilla Analytics: A Practical Approach to Working with Data

I haven't read it, but there's a book written about what p-values are: https://www.amazon.com/p-value-Stories-Actually-Understand-Statistics/dp/0321629302

I've heard this one's a classic, but I haven't read it, so I can't comment personally:

https://smile.amazon.com/Grammar-Graphics-Statistics-Computing/dp/0387245448?sa-no-redirect=1

Although I am not a statistician myself and given your background, some of my recommendations would be:

• All of Statistics for a concise treatment of well....all of statistics :p
  
  
• [Introduction to Statistical Learning/Elements of Statistical Learning] (https://www.amazon.com/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370/ref=sr_1_1?s=books&ie=UTF8&qid=1493070885&sr=1-1&keywords=introduction+to+statistical+learning) avaliable for free in the internet if you are interested in applying statistical methods with Machine Learning
  
  
• Introduction to Probability for a intro to probability theory which underlies most of statistics.
  
  
• One of either [Python Data Science Handbook] (https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057/ref=sr_1_1?s=books&ie=UTF8&qid=1493071026&sr=1-1&keywords=python+data+science+handbook) or [R for Data Science] (https://www.amazon.com/Data-Science-Transform-Visualize-Model/dp/1491910399/ref=sr_1_1?s=books&ie=UTF8&qid=1493071056&sr=1-1&keywords=r+for+data+science) for building programming chops in your language of choice Python or R.
  
  This should probably be enough for now but if you need more recommendations just say so :)
  
  Regarding the time series question, it's not my area of expertise but since time series analysis ends up employing many statistical methods, I think it can be considered an area of statistics (Statisticians around here correct me if I am wrong :P)

You can do a significance test. But 1 in 20 randomization checks will be statistically significant. So if your test is significant, you don't know if randomization failed, or if you were unlucky.

And if a randomization check is not significant, that doesn't tell you much. It just tells you that you failed to detect failures of randomization.

So don't try. Design robust randomization procedures that you believe in (using telephone randomization, opaque envelopes, whatever). If you don't have complete faith in your randomization, you don't believe it, no matter what.

Link (behind a paywall): http://www.bmj.com/content/319/7203/185.1.full I think the clinical trials book by Torgerson and Torgerson covers this: https://www.amazon.com/Designing-Randomised-Trials-Education-Sciences/dp/0230537359 (But so should any decent book on clinical trials).