UKC

Having successfully complete two engineering degrees without having to learn any stats my luck has run out and I need to get up to speed quickly.

Can anyone recommend a book (or website etc) pitched at a basic level (of stats) but for those who can already add up without supervision. The stuff I've found so far is either too complicated or too basic.

Thanks in advance.

Dave

In reply to mostly harmless:

Try this,
http://www.statsdirect.com/help/Default.htm#contents.htm%3FTocPath%3D_____1

In reply to mostly harmless:

"Stats" is a bit of a wide field... You might get more useful replies if you give some indication about what you need to do.

If you're looking for a relatively basic, or at least accessible, text book then those introducing stats to biologists might be a good place to start, for example this one http://www.biostathandbook.com (free PDF edition online)

In reply to mostly harmless:
www.st-georges-academy.org/sites/default/files/files/REVISIONGUIDE%2520-%2520stats.pdf

Covers all the basics. Without knowing what you want to do its hard to know what is too easy or hard.

Link not working. Google GCSE statistics and it is the 3rd one.

In reply to mostly harmless:

You might find some of this book useful. It's not primarily a stats book but it's very well written and includes some useful chapters on stats.

http://www.inference.phy.cam.ac.uk/itprnn/book.html

As a Physics graduate who studied no virtually no stats until teaching myself after my degree I think the important thing it to focus on the fundamentals. Don't get too hung up on hypothesis testing and generalised linear models until you have understood the basics of statistical inference - how to infer knowledge of something from incomplete information and how to assess the accuracy of that information. When I was in college I thought stats was just 'making numbers from other numbers', calculating averages and standard deviations just for the hell of it. It was only later when I studied it myself that I realised the fundamental importance of statistical inference to virtually all decision making - human or machine. There are also some very interesting 'fundamental' theorems underlying stats, the Central Limit Theorem being one of the most important.

In reply to mostly harmless:

Like you, I have a physical science background, and decided that I finally ought to get to grips with this thing called stats that my biology/social science/psychology colleagues talk about but seem very afraid of.

Have you thought of doing a MOOC?

I have done several now in the stats/data science field, and find them very helpful as a way of giving focus and discipline to my learning. The emphasis ranges from classical stats to the wider field of statistical learning, data mining ,data science, analytics - whatever you want to call it.

They are all free, including the assessment, or you can pay £30-£50 per course for a verified certificate, if you want one.

There is a whole series of nine courses from Johns Hopkins University called Data Science, on the Coursera platform. I have done the first seven of them, and start the last two in a few days. They are dense, fast-paced and will give a lot to do if you take them two or three at a time as I have done. Only two, Statistical Inference and Regression Modelling focus on what you possibly mean by stats, but the others give you really good (I think) practice in actually solving and presenting problems - so you learn how to use R, Git and GitHub, how to get and clean messy data, how to do exploratory analysis and how to use the various graphics packages in R.

Many find the approach overly mathematical, but you might like that. I do.

Another one I am part way through is The Analytics Edge, offered by MIT on the EdX platform. This also gives a lot practice in R, with a very wide range of examples of the use analytics, for example in the stock market, in health, in sport. The assessments are time consuming but straightforward, or at least they were until the current one, which is a Kaggle Competition.

In September (good timing for you) I am going to start another one, which is highly rated, called Data Analysis and Statistical Inference, from Duke University on the Coursera Platform. It also uses R and has a free text book online that they will use. (All the JHU materials are on Github/Youtube too).

I could in principle do all this myself just by looking at websites, but the courses move you along and make you do assessments, in which most of the effective learning takes place.

In reply to mostly harmless:

I can recommend this text from Peter Diggle and Amanda Chetwynd
http://ukcatalogue.oup.com/product/9780199543199.do
which focusses on the thinking behind using statistics ( inference ) rather than dogmatic numerical recipes.
there is a partial extract here to see if you like it:
http://www.lancaster.ac.uk/staff/diggle/vets/DiggleChetwyndextract.pdf

In reply to mostly harmless:

My favourite textbook is

JH Zar, Biostatistical analysis, Prentice Hall 2010

On the web, I find

http://vassarstats.net/textbook/

a good companion to their online stats calculator.

CB

In reply to mostly harmless:

You could try an a level text book?

In reply to mostly harmless:

R and the IDE R Studio are open-source free software. R was specifically designed for solving statistical problems and is now widely used.

There are some excellent interactive classes in R for learning both R and stats in the package called swirl.

In reply to mbh:

Thanks for that, the main problem I currently have is identifying which distribution to use/fit and how to test that it was correct and correct within a confidence limit.

Thanks everyone for your advice, I'd entirely forgotten about MOOCs and I'll work through some/all of them.


Currently using R Studio, and I'll check out the swirl package.

Thanks again.

Dave