Logistic Regression Basics Explained: Probabilities, Odds, Odds-Ratios and Log-Odds-Ratios (4K)

Enjoy! 🥳

Welcome to my VLOG! My name is Yury Zablotski & I love to use R for Data Science = "yuzaR Data Science" ;)

This channel is dedicated to data analytics, data science, statistics, machine learning and computational science! Join me as I dive into the world of data analysis, programming & coding. Whether you're interested in business analytics, data mining, data visualization, or pursuing an online degree in data analytics, I've got you covered. If you are curious about Google Data Studio, data centers & certified data analyst & data scientist programs, you'll find the necessary knowledge right here. You'll greatly increase your odds to get online master's in data science & data analytics degrees. Boost your knowledge & skills in data science and analytics with my engaging content. Subscribe to stay up-to-date with the latest & most useful data science programming tools. Let's embark on this data-driven journey together!

[amosarubi]

Thanks. Your videos and perspective are unique.

[zhakota]

Thanks a lot for the guidance! The most understandable explanations of statistics.

[tanviirahmed]

Sweet problem while watching your videow is: I try to link it with all other videos you've uploaded and I get lost 😆

Solution: I again watch that specific video of yours where I think I got lost and get back on track.

the best thing about your videos is every second is informative. Thank you very much.

[Inexorablehorror]

Many thanks. Could you please also do something about probit regression (and as compared to logit regression), as well as ordinal probit regression? Many thanks for your effort!

[BoloYang-xe]

How I understood what odds are, odds ratios. Using your example: the survival rate of females on a ship is 70 to 30, males 20 to 80. If we convert it into probability, then for females it is 0.8, and for males 0.2. How to calculate the odds based on this probability: chance (to survive) = probability (of survival) / (1 - probability (of survival)) = 0.7 / (1-0.7) = 2.33. It turns out that females have a chance of survival that is 2.33 times greater than the chance of dying. According to this formula, men have a chance to survive = 0.25, that is, the chance to survive is 4 times less than the chance to die. Or if you can interpret the odds this way - for every 233 surviving females, there are 100 dead. For every 25 surviving males, there are 100 dead. Odds ratio, as I understand it, calculates the odds ratio for two events, let’s assume event A - female survival rate is 70% and event B - male survival rate is 20%. Formula Odds ratio = Odds females (2.33) / Odds males (0.25) = 9.32. OR = 9.32 means that the chances of a female surviving a given event are 9.32 times greater than the chances of a male surviving the same event.

[Endrumpf]

Prof. Please increase the frequency of videos.

All forms of Probit Model,
Impact evaluation model like Switching regression, DID, and so on...