Logistic Regression [Simply explained]

What is a Logistic Regression? How is it calculated? And most importantly, how are the logistic regression results interpreted? In a logistic regression, the dependent variable is a dichotomous variable. Dichotomous variables are variables with only two values. For example: Whether a person buys or does not buy a particular product. Logistic regression is very often used in machine learning.
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00:00 What is a Regression
00:45 Difference between Linear Regression and Logistic Regression
01:24 Example Logistic Regression
02:23 Why do we need Logistic Regression?
03:31 Logistic Function and the Logistic Regression equation
05:01 How to interpret the results of a Logistic Regression?
07:58 Logistic Regression: Results Table
08:21 Logistic Regression: Classification Table
09:19 Logistic Regression: and Chi Square Test
10:22 Logistic Regression: Model Summary
11:24 Logistic Regression: Coefficient B, Standard error, p-Value and odds Ratio
13:56 ROC Curve (receiver operating characteristic curve)
#statistics

[@datatab]

If you like you can download our free Logistic Regression Playbook: datatab.net/tutorial/statistics-playbook 🙂

[@tomyanggriawan7534]

Thankyou

[@MrAbah105]

simple explanation, thanks

[@fabiolagr4142]

After a long hiatus from statistic, your videos have helped to put me up to speed. This one in particular, was excellent! Thank you

[@jwoluenpao]

Thank you. That was the first time for me to understand what logistic regression was. It really helps.

[@parulbhaiya5158]

This is the best and the easiest method of explanation so far I've seen for the particular topic. Thankyou!!!

[@J2C1983]

This is an excellent explanation of logistic regression! Very easy to follow! Thanks!

[@datatab]

Glad it was helpful!

[@md.kutubulalamubayed6205]

I love the way you deliver your lecture ... its superb. many many thanks

[@DailyMeditation365]

Amazing video that really helped me understand logistic regression better. Thank you!

[@DanielRamBeats]

This is a beautiful example, thank you. You guys should do one on neural networks

[@muhammedhadedy4570]

Amazing tutorial and amazing statistical software. Thanks so much for your great videos. Please, keep up the great work.

[@datatab]

Thanks, will do! Many thanks for your nice Feedback!!! Regards Hannah

[@fabio.s.barbosa]

thanks a lot for your explanation. Easy to follow and very instructive.

[@datatab]

Thanks : )

[@muhammadfaisyalalauddinmuk6646]

That was sooo clear. Thank you for the video

[@datatab]

Glad it was helpful!

[@shamanahuji]

very nice video. very well explained. Thank you

[@muchazmuchaz2885]

Best video ever. Thank you

[@dees900]

Great class. thank u

[@risausa4796]

Thanks for this video!

[@robertapereira7896]

Great video, congrats! Do you perhaps have a video on probit analysis (including 0 and 100% values in the dataset)? Thank you very much.

[@ThomasHaberkorn]

Yet again a great video, thanks 👍

[@datatab]

Thank you Thomas and thank you for your feedback!!!! : )

[@zunaidqureshi8521]

Thanks for this wonderful explain!

[@datatab]

Glad it was helpful!

[@Vladimir-Marin]

You made it so easy, thank you ☺

[@familians]

You could lime this video too: Another great video about logistic regression in JMP kzbin.info/www/bejne/b6qxkKygfKZ9kKcsi=jUwEZUDobBudE8AE

[@ahmadneri5602]

I want to give you 1000like because it was a best video and explain. It was very helpful I learned it all of them

[@datatab]

Many thanks : )

[@ajitpandiyan471]

You guys made it simple and clear 😁

[@datatab]

Thanks!

[@rawiahnaoum4382]

You explain it very well. It is like 1+1 =2 -- it is really easy to understand

[@datatab]

Many thanks : )

[@Featherlicht]

Lovely explanation ❤

[@allemagallied4775]

13:49 is a mistake I think. odds ratio of 1.04 does not mean that the probability increases by 1.04 times. An odds ratio of 1.04 means that for a one-unit increase in the independent variable, the odds of the event happening (in the context of a binary outcome) are 1.04 times higher. It does not directly relate to a specific change in the probability of the event occurring. To understand the impact on probabilities, you would need to convert the odds ratio back to probabilities using the logistic function. The mistake is in confusing odds and probabilities.

[@user-zz4xf5mq9p]

Yup, this is correct. Good catch!

[@bettyk107]

you are right !!!

[@atharvigupta4250]

very good and clear explanation, thank you

[@datatab]

Glad it was helpful!

[@abcdefghijkl5412]

Too many thanks. It's very well explained and understood. May you help and also make a detailed video on ordinal logistic regression and multinomial logistic regression, explaining the different equations used under each step by step like you have done under binary logistic regression? Thanks

[@datatab]

Hi many thanks for your feedback! Yes it is on our to do list but it will certainly still take a while!! Regards Hannah

[@alonsomoscoso3255]

Thank you for this!

[@datatab]

Many thanks : )

[@sattyajitdatta9109]

Excellent!

[@PremBankerVT]

i love this video. The concept is explained in a very easy manner

[@datatab]

Many thanks!

[@pushkal8800]

I have one doubt , is logistics regression = sigmoid(linear regression) or are there any other differences. Other than that amazing video, never found this much clarity.

[@datatab]

@@pushkal8800 Yes, logistic regression is essentially a combination of linear regression followed by the application of a sigmoid function. However, while this captures the essence of how logistic regression models the relationship between the independent variables and the dependent variable, there are a few more nuances that distinguish logistic regression from simply applying a sigmoid function to linear regression.

[@isurusenevirathne]

well explained

[@rupeshkamble9105]

Thanks the video

[@rizkyfadhillah4295]

If we use a binary logistic regression test, should the independent variable be made into two categories? Can't we analyze it if the independent variable has more than two categories? Especially if the two independent variables are in ordinal data form and the dependent variable is in nominal data form.

[@yusufxanderortega5944]

how do we determine the coefficient parameters in model?

[@johannasteinbock1848]

Min 8:12: What does "correctly assigned" mean in this context?

[@user-wu1ci1cl4n]

no vedio on the curve ,no link?

[@markleeismylee1511]

Ma'am, you are my hero😍

[@antonellaorologiaio5383]

How can I add control variables in DataTab?

[@user-ph9si6ii2t]

Hi! good job thank you for that. But I have a one question. how you conculcated of coefficients B? can you give same example for that...?

[@affendiali607]

you may wanna watch the video on linear regression. plenty of videos on youtube have explained it well since is it just b is just a gradient/slope of the line in the linear equation. the formula is also quite straight forward too.

[@dr.battulapradeep1183]

No video on ROC curve. If available provide link.

[@datatab]

Sorry! Now it is there: kzbin.info/www/bejne/h3O5q414qM6ZaJI

[@oceanview3165]

She sounds so much like my physics professor from Ukraine! Excellent explanation!

[@zudimunir7798]

good video

[@alokebachhar7419]

Just loved you..

[@Raya7766]

Thank you but you didn't mention what r square means in logistic regression. You just said that it's different.

[@abdremo]

well explained, thank you, btw you can pronounce dichotomous as die-cut-o-mus

[@datatab]

Hi, many many thanks for your feedback! Thant helped me a lot : )

[@jignashasoni2129]

Wrong. In logistic regression, the response variable is an attribute variable that can be binary, nominal, or ordinary. you were only talking about the binary response variable.

[@aybeeaa8662]

Damn man this is so confusing 😢

[@familians]

Hi!! Try this one… you may like: Another great video about logistic regression in JMP kzbin.info/www/bejne/b6qxkKygfKZ9kKcsi=jUwEZUDobBudE8AE