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Earlier quoted context omitted.
I've found the book indeed - although it seems to be the first edition. It's here: https://civil.colorado.edu/~balajir/CVEN6833/bayes-resources...
With all due respect, how hard did you look? This is the third result from a query I ran: https://github.com/Booleans/statistical-rethinking/blob/mast... Main repo w/ some notebooks: https://github.com/Booleans/statistical-rethinking
As much as I appreciate and love all pedagogical endeavours in the field, especially in the form of open texts, I really, really, really dislike this overall approach to teaching introductory statistics. I'm hoping to see, over time, a shift away from ad-hoc null hypothesis testing in favour of linear models (yes, in introductory courses, from the start-- see link below) and Bayesian-by-default approaches. https://li…
> I'm hoping to see, over time, a shift away from ad-hoc null hypothesis testing in favour of linear models (yes, in introductory courses, from the start-- see link below) and Bayesian-by-default approaches. Is there anything where I can start today, as a guinea pig? My statistics education is basically zero.
It goes over Bayes' Theorem early on, which I assume is Bayesian-by-default. I didn't realize this isn't universal.
I watched it because my professor seemed to be teaching from the same textbook, so it followed the same general course structure. The textbook we used was "Probability and Statistics for Engineering and the Sciences — 9th edition". You can find a PDF of the eighth edition just by googling the name.
You could probably follow along just by taking notes during the video lectures, but if you want to give yourself homework, the textbook provides a lot of practice problems.
Anyone looking to apply and compare frequentist and bayesian methods within a unified GUI (which is essentially an elegant wrapper to R and selected/custom statistical packages), should check out JASP developed by the University of Amsterdam [0]. It's free to use, and the graphs + captions generated during each step are publication quality right out of the box. Using it truly feels like a 'fresh way' to do statistics…
How does this compare to other stat libraries?
R Studio for the uncompromised R language integration.
Jamovi for the clean interface and simple to use analyses and workflows.
Stenci.la for the multiple language notebook paradigm.
KNIME for the node based interface and workflow productionisation.
PSPP for the 'same but different' SPSS interface and feature set.
Quarto for the 'analysis as publication' approach, reactive notebooks and custom integrations.
Myself I often opt for BigQuery + Javascript stat libraries, which is free insofar you remain within the sandbox mode.
The epub is apparently too big to send to a kindle, but I can't see the option to download it, only the pdf. Any ideas?
What is a good book on statistics that one can use for self-learning?
Another good book at a more advanced level is this one: "Regression and Other Stories" by Andrew Gelman and Jennifer Hill http://www.stat.columbia.edu/~gelman/arm/>.
Another good book at an even more advanced level is this one: "Data Analysis Using Regression and Multilevel/Hierarchical Models" by Andrew Gelman and Jennifer Hill http://www.stat.columbia.edu/~gelman/arm/>.
These books are all very polished productions. What make makes them special is that they emphasis teaching the statistical way of thinking.
Earlier quoted context omitted.
There is also Brillant that has a very polished interactive course: https://brilliant.org/courses/statistics/
These things are great if they add value for you, but I would be very skeptical of any non-mathematical approach to statistics. I think statistics is only made clear by mathematics, much the same as Physics. And one cannot grasp statistics without being able to understand the maths. I think that still the best way to understand statistics is to start with the mathematical theory and to grind 1000+ textbook problems.
Later I re-learned it in an applied way, and things made much more sense to me.
(That's fine if you liked learning it your way, but that's no reason to be skeptical about other approaches.)
Statistics education is undergoing a bit of a revolution, driven by the accessibility of computers. For example, hypothesis testing is introduced by randomization[1], using a randomized permutation test[2]. I find this really easy to understand, compared to how I learned statistics using a more traditional approach. The traditional approach taught be a cookbook of hypothesis tests to use: use the t-test in this situa…
Statistics education is undergoing a bit of a revolution, driven by the accessibility of computers. For example, hypothesis testing is introduced by randomization[1], using a randomized permutation test[2]. I find this really easy to understand, compared to how I learned statistics using a more traditional approach. The traditional approach taught be a cookbook of hypothesis tests to use: use the t-test in this situa…
Hey i recognized you from the Scala doodle project! that and the other stuff from Underscore was a really cool way to learn Scala & FP, thank you :)
Statistics education is undergoing a bit of a revolution, driven by the accessibility of computers. For example, hypothesis testing is introduced by randomization[1], using a randomized permutation test[2]. I find this really easy to understand, compared to how I learned statistics using a more traditional approach. The traditional approach taught be a cookbook of hypothesis tests to use: use the t-test in this situa…
As much as I appreciate and love all pedagogical endeavours in the field, especially in the form of open texts, I really, really, really dislike this overall approach to teaching introductory statistics. I'm hoping to see, over time, a shift away from ad-hoc null hypothesis testing in favour of linear models (yes, in introductory courses, from the start-- see link below) and Bayesian-by-default approaches. https://li…
I am partway through McElreath's "Statistical Rethinking" and I fully agree with this.
[0] https://bookdown.org/ajkurz/Statistical_Rethinking_recoded/