Wednesday, September 07, 2022

Ruminating on Hypothesis testing

The following two articles by Rebecca Bevans are an excellent introduction to the concept of Hypothesis testing and the types of statistical tests available:

Snippet from the article on the process of hypothesis testing:

Step 1: State your null and alternate hypothesis

Step 2: Collect data

Step 3: Perform a statistical test

Step 4: Decide whether to reject or "fail to reject" your null hypothesis

Free Stats & Finance courses

The following site has an excellent collection of 20 free courses that I would highly recommend for folks who want to learn the basics of finance and fundamentals of maths/stats in finance.

https://corporatefinanceinstitute.com/collections/?cost=free&card=117884

I really liked the following courses and helped me consolidate my understanding:

- Data Science fundamentals - https://learn.corporatefinanceinstitute.com/courses/take/data-science-and-machine-learning/

Continuous, Discreet and Categorical variables

The following websites gives an excellent overview for beginners of the 3 different types of variables that we encounter in feature engineering (or even in basic stats):

https://www.scribbr.com/methodology/types-of-variables/

Snippets from the articles:

A discrete variable only allows a particular set of values, and in-between values are not included. If we are counting a number of things, that is a discrete value. A dice roll has a certain number of outcomes, and nothing else (we can roll a 4 or a 5, but not a 4.6). A continuous variable can be any value in a range. Usually, things that we are measuring are continuous variables, because it can be any value. The length of a car ride might be 2 hours, 2.5 hours, 2.555, and so on.

Categorical variables are descriptive and not numerical. So any way to describe something is a categorical variable. Hair color, gum flavor, dog breed, and cloud type are all categorical variables.

There are 2 types of categorical variables: Nominal categorical variables are not ordered. The order doesn't matter. Eye color is nominal, because there is no higher or lower eye color. There isn't a reason one is first or last.

Ordinal categorical variables do have an order. Education level is an ordinal variable, because they can be put in order. Note that there is not some exact difference between the levels of education, just that they can be put in order.