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You shouldn't have substituted the std error value of 0.262 for the coefficient of x1, but rather the eval coefficient of 0.2687 because that is what our independent variable is. Let me know if there is something wrong with what I have said.
It was very helpful to watch, just one comment: Usually y is the dependent variable, so your variable names in the beginning are the wrong way around. In this case it does not matter because it was only one independent variable, but technically speaking you tried to estimate the effect of evaluation results on beauty there The same later on (total_bill got explained by tip), which is why I imagine it didnt work with binary variables.
Thanks for this intro to linear regression. I think "sex" and "smoker" didn't work because of their "categorical" nature as opposed to other "numerical" predictors.
@@derekbanas from real life examples - it does not matter what model are you choosing if you are an expert you can combine enough compexity and at least moderate results. Or if you are black boxing you do not care what model is. There is no such rule that catrgorical variables will not be predictive. In general education, wealth, family status, age, gender, Credit score, location are preferable
this is gold, i dont think anyone makes as simple tutorials as you
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Derek, the vast amount of knowledge you have is impressive!
Thank you :) It is just what I do for a living. Anyone could learn this stuff if I did
Dear Derek, thanks so much for this video.
I'm happy you liked it :) I'll do my best to start covering machine learning with TensorFlow next week
Thank you Derek!
You're awesome !!
Is there any non-linear regression tutorial?
easy to follow along and good content as well
You shouldn't have substituted the std error value of 0.262 for the coefficient of x1, but rather the eval coefficient of 0.2687 because that is what our independent variable is. Let me know if there is something wrong with what I have said.
I'm also interested in a reply to this. I immediately caught this too but I want to make sure I'm not misinterpreting something.
It was very helpful to watch, just one comment: Usually y is the dependent variable, so your variable names in the beginning are the wrong way around. In this case it does not matter because it was only one independent variable, but technically speaking you tried to estimate the effect of evaluation results on beauty there The same later on (total_bill got explained by tip), which is why I imagine it didnt work with binary variables.
Thanks Derek , but I really wait for calculus and linear algebra in one video
I'll see what I can do. Linear algebra is very doable. Calculus would be a very long video
Thanks for this intro to linear regression. I think "sex" and "smoker" didn't work because of their "categorical" nature as opposed to other "numerical" predictors.
Yes that is most definitely true. I could have gotten them to work, but I thought it didn't matter that much
@@derekbanas from real life examples - it does not matter what model are you choosing if you are an expert you can combine enough compexity and at least moderate results. Or if you are black boxing you do not care what model is.
There is no such rule that catrgorical variables will not be predictive. In general education, wealth, family status, age, gender, Credit score, location are preferable
Inventory analysis please
Greetings, Derek Banas
Thank you for checking out my video :)
can you use this for multivariate linear regression?
can you please make tutorial a bout scapy in python
un grand merci (:
C'est un plaisir pour moi d'aider
can someone tell me why I am adding a column of 1 to X?
Sooo... "Expressions" or "Regressions" now?
Regressions, sorry if I said expressions at some point?
@@derekbanas Psshht, the title of the video says "Expressions" 🤫
That's funny :D I'll fix it
Exclusiveshiv is me
Thank you so much for stopping by my livestream :) Normally I don't have technical difficulties. I greatly appreciate it!