Where Do I Start?
A quick guide on how to use this wiki.
Who Is This Wiki For?
Everyone, really. However, specifically I am designing this wiki with two audiences in mind. One, the casual student and the short-on-time practitioner who needs a quick answer to something. Such guides will mostly be located in what is currently the “blog” posts section and the High School and AP Statistics section. Two, the scientist and the self learner who really want to understand the subject. Such guides will be located in the main section of the website.
I am hoping this min-max approach will allow this wiki to be accessible, practical, and extremely knowledgeable.
Where Do I Go To Find What I Want?
High School and AP Statistics Students
Looking For A Quick Answer?
For those of you looking for a quick answer, reference, or definition, you can just type what you want into the search bar above. Or you can use Google's search engine, which may end up being better than this website's search engine.
Looking To Learn Statistics From The Ground Up?
For those of you interested in learning statistics, machine learning, and artificial intelligence from the ground up, you can follow the road map below. If you feel like you already know some of the topics, feel free to start where you think it makes sense to. There is no need to view the high school and AP statistics section as I will cover the same topics but with greater depth, different language, and different focus.
Road Map To Teach Yourself Statistics
The below is the recommended order to go to teach yourself statistics.
You do not need to know a lot of calculus to understand statistics. But you do need to understand concepts such as derivatives and integrals. Integrals are an essential part of understanding probability distributions and density functions, which is a topic covered in the foundation portion of statistics. Understanding derivatives will help you understand optimization, which is how most models are derived.
(2) Probability And Statistics
While the entire website is about statistics, this section focuses on the important theorems and concepts that are the foundations for understanding statistical models and inference. This includes topics like central limit theorem, combinations and permutations, sampling distributions, etc...
There are many distributions and so at this stage, you should become familiar with some of the more common ones such as normal, binomial, bivariate normal, t-distribution, etc... Many of the models or statistical inference techniques make some assumption about the distribution of your data.
(4) Linear/Matrix Algebra
Although not necessarily required to understand at this stage nor need to understand most of the topics on this website, as we get deeper into the subject, you may find understanding this useful. This is especially true if you really want a good understanding of how the algorithms works and building your own machine learning algorithms.
(5) Simple Linear Regression
Here we get to our first and most common statistical inference technique.
(5) Simple ANOVA
(6) Multiple Linear Regression
(8) Logistic Regression
At this point, you will have enough of an understanding that you can go off on your own and learn specific topics of interest and importance.