Reddit mentions of Applied Linear Statistical Models

Applied Linear Statistical Models by Kutner is a far better reference for statistical modeling compared to ISLR/ESLR or any kind of "machine learning" text, but it sounds as though you did a stat masters since you're asking about stat modeling instead of the new buzzwords. The latter options are certainly more narrow.

https://www.amazon.com/Applied-Linear-Statistical-Models-Michael/dp/007310874X
Considered a cornerstone, of sorts.

The book that you want the person to look up is Applied Linear Statistical Models. It is a great reference book and gets into the nitty gritty calculations for figuring out the appropriate degrees of freedom in some pretty ugly experimental designs.

>If you Google KNNL it'll know what you're looking for).

Mine certainly didn't, I got two pages of Karnataka Neeravari Nigam Limited and associated projects.

If anyone else is wondering, I'm assuming this is the book, I eventually found it on a CSU syllabus: https://www.amazon.ca/Applied-Linear-Statistical-Models-Student/dp/007310874X

Not to be confused with: https://www.openhub.net/p/knnl

It requires study so you might not have any sudden moments of clarity, but this is pretty much the Bible of regression.

http://www.amazon.com/Applied-Linear-Statistical-Models-Michael/dp/007310874X


Highly recommended.

I agree with all of the above. Also, here's the Linear Models tome we used: http://www.amazon.com/Applied-Linear-Statistical-Models-Michael/dp/007310874X

By the way, do you know if things like linear/nonlinear regression, ANOVA and multivariate statistics is useful for me? Like stuff from https://www.amazon.com/Applied-Linear-Statistical-Models-Michael/dp/007310874X/ref=dp_ob_title_bk or https://www.amazon.com/Bayesian-Analysis-Chapman-Statistical-Science/dp/1439840954/ref=pd_sim_14_8?_encoding=UTF8&pd_rd_i=1439840954&pd_rd_r=c9c0f3c5-f332-11e8-9f0a-0f336f5f387a&pd_rd_w=xHkKH&pd_rd_wg=7lXm5&pf_rd_i=desktop-dp-sims&pf_rd_m=ATVPDKIKX0DER&pf_rd_p=18bb0b78-4200-49b9-ac91-f141d61a1780&pf_rd_r=PFCZ1JM04FMAVAHG6VNP&pf_rd_s=desktop-dp-sims&pf_rd_t=40701&psc=1&refRID=PFCZ1JM04FMAVAHG6VNP

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You can tell how little I know since I'm kinda shooting topics at the wall hoping something sticks

It's very clear for a book on mathematical statistics. It also considers the Bayesian (and even Empirical Bayesian) approach. I'm sometimes shocked at what it covers and how well it covers it in so few pages. For example, there's a nice section on the EM algorithm, which most books in the same class don't cover (unless they're huge).

Edit: I should mention... if you're a scientist looking for how statistics works this is the book for you. If you want to learn a ton about regression/ANOVA, time-series, covariance structures, blah, blah, blah, this book is not for you. A great introduction (for all scientists) that covers this stuff quickly and effectively (as well as MLE, optimization, and R) is Ecological Models and Data with R.

Edit 2: If you want applied linear models, Applied Linear Statistical Models is good, but doesn't use R. Luckily formula objects and delayed evaluation give R some beautiful expressivity here.

Thanks. The program is Data Science and prereqs are Calc, Lin Alg and basic stats.

I started my review using https://www.amazon.com/Applied-Linear-Statistical-Models-Michael/dp/007310874X but the book assumes you have basic stats. I took these courses 5+ years ago so I only vaguely remember the material.

Good example with hetero/homoskedasticity. I want to make sure I understand things like random variables and different types of distributions.