Hi Josh, Love your content. Has helped me to learn a lot & grow. You are doing an awesome work. Please continue to do so. Wanted to support you but unfortunately your Paypal link seems to be dysfunctional. Please update it.

BAM!!! And thank you for your support!!!!

Wow, thanks!

Thank you so much!!! I'm really glad you like my videos and my music. :)

Thank you very much! :)

Thanks for watching!

Hooray! I'm glad to help! :)

I feel the same!! hah

Solal Sténou Merci!! :)

Thanks so much! :)

Thank you! :)

Hooray! Thank you! :)

Thank you!

Hooray!

so, how did it go today?

Thanks!

Thank you very much!!!

Awesome! Thank you!

Hooray!

Thank you very much! :)

Thank you! :)

Wow! That sounds awesome!!!

@@statquest oh yes! I owe you a lot - you saved me so many hours! Γ

Thank you! 😃

Awesome!!! Now that you have that concept down, a lot of other stuff in statistics should make more sense. (At least I hope!) :)

Hooray!!! That would be awesome. I will dream of the day I can visit you in Toronto. :)

Thank you! :)

Thank you! :)

Thank you! :)

Wow, thanks!

Thank you very much! :)

My pleasure 😊

Thank you! :)

Thanks! :)

Thanks! :)

Thank you very much! :)

Yep, that is correct. That's why I made all those other videos first - the output is jam packed with stuff.

Thank you very much! :)

Thank you! :)

Thanks! :)

Hooray! :)

Hooray! I'm glad you like it! :)

Thank you! :)

Yes! However, it might be a while before I get to it.

Hooray!

Glad you found it.

Thank you! :)

Those are all in the works. I wish I could work 2 or 4 times faster than I can. I've wanted to cover the major probability distributions for over a year, but got sucked down a machine learning path and now feel spread pretty thin. However, these will happen eventually! :)

StatQuest with Josh Starmer could you make a video on how to work 2 to 4 times faster? :-)

As soon as I figure that out, I'll make a video on it! ;)

BAM!!!

same!

Thanks!

Thank you! :)

No yet.

Thanks! :)

Thanks!

Awesome!!! :)

Mine too

Awesome!

Great suggestion!

@@statquest where's the video sir in python sir?

Thanks! :)

Thank you! :)

BAM! :)

@@statquest Triple Booyah BAM from my side!

bam!

Thank you very much!!! :)

Thanks! :)

I'll keep that in mind.

Thank you!

Wow, thank you!

I'll keep that in mind.

I know this is probably 10 months too late, but the book “Introduction to Categorical Data Analysis” by Alan Agresti is a great book. Does a really good job explaining logistic regression and is pretty light on the math.

Good luck! :)

Thanks!

bam!

Hmm...I'm not sure. Your graph should definitely taper off as the predicted probabilities get closer to 1, but how visible this tapering is might depend on how many data points you plot.

Not yet. :( But one day we will.

Thanks!

You may like this video too: Another great video about logistic regression in JMP kzfaq.info/get/bejne/b99-ktybrKeuink.htmlsi=jUwEZUDobBudE8AE

I'll keep that in mind.

The Chi-Square test is on the list. I've looked into the Hosmer-Lemeshow fit... Can you tell me what you think about the limitations? Specifically those mentioned in the wikipiedia article about it? en.wikipedia.org/wiki/Hosmer%E2%80%93Lemeshow_test#Limitations_and_alternatives And iteratively reweighted least squares is also on the list. However, up next are some basic statistics videos and then videos on lasso, ridge, and elastic-net regression.

the Hosmer-Lemeshow statistic was used to avoid problem in Pearson chi-squared statistic which was when observations being grouped by the values of the x variables, the Pearson chi-squared goodness of fit test cannot be readily applied if there are only one or a few observations for each possible value of an x variable, or for each possible combination of values of x variables. (A sample with a sufficiently large size is assumed. If a chi-squared test is conducted on a sample with a smaller size, then the chi-squared test will yield an inaccurate inference). So in the Hosmer-Lemeshow statistic, the observations are grouped by expected probability. But there is very little guidance on selecting the number of subgroups. The number of subgroups,g, is usually calculated using the formula g> P + 1. For example, if you had 12 covariates in your model, then g > 12. How much bigger than 12 g should be is essentially left up to you. Small values for g give the test less opportunity to find mis-specifications. Larger values mean that the number of items in each subgroup may be too small to find differences between observed and expected values. Sometimes changing g by very small amounts (e.g. by 1 or 2) can result in wild changes in p-values. As such, the selection for g is often confusing and arbitrary. Also, it doesn’t take overfitting into account and tends to have low power. For these reasons, the Hosmer-Lemeshow test is no longer recommended. Am I on right? Is it enough cues to no longer used of HL test? I have another question, ( Overfitting is happening when your sample size is too small. If you put enough predictor variables in your regression model, you will nearly always get a model that looks significant. While an overfitted model may fit the idiosyncrasies of your data extremely well, it won’t fit additional test samples or the overall population. The model’s p-values, R-Squared and regression coefficients can all be misleading. Basically, you’re asking too much from a small set of data.) If I have a small sample, is there any problem to use Maximum likelihood to fit model and McFadden's pseudo-R squared? Is there any rule to chose the number of sample for any regression? Sorry for the many of questions, it is my first year in biostatistics. :)

These are all great questions. You are correct about the HL test and you are correct about overfitting. There are, however, lots of tricks you can use to compensate for overfitting (lasso regression, ridge regression, elastic net regression etc.) One way to test to see if you have a model that is "overfit" is to use cross validation. As for a minimum number of samples for logistic regression - people often say "10 samples per level of each discrete variable". It's a general rule of thumb and it doesn't always apply. However, again you can use cross validation to verify if you have enough samples or not. Cross validation is a very practical tool!

Thank you, Mr Josh, for answering me, I need to study more about Cross-validation.

Sorry l have more than one account 🙈🙊

Alpha =1 because I experimented with different values for alpha before realizing that having things 100% opaque (ie the default value) was best. Shape draws the Xs and stroke makes the Xs easier to see.

I'll keep that in mind.

:)

Consider omitting them. However, it really depends. Maybe use cross validation to see if predictions are better with or without them. For details on Cross Validation, see: kzfaq.info/get/bejne/nLmpp9143N2mhqs.html

Thanks! :)

Oh man, forgot, why factor for the ordered data?

If the categorical variables are cast as "factors", then R will create the correct design matrix.

In this example we convert 'hd' into a factor because it represents 4 possible outcomes (which we then reduce to just 2 possible outcomes). We have to do this because the outcomes are coded as numbers, which R original interprets as integers, rather than discrete outcomes.

bam!

Unfortunately I've never done Poisson regression... :(

For logistic regression, it will be easier to understand what the estimated coefficients mean if you multiply the percentage of games won by 100. When you do this, you can use these values as "numeric" and the coefficient will tell you how much the probability of the outcome changes for every 1 percentage change in that variable. For more details on interpreting the coefficients, check out kzfaq.info/get/bejne/rLRllrF_l5Osh3k.html

Thank you very much for your help!!! I appreciate it a lot! I'm glad it's not a complicated solution... :D

In this video, we use a combination of "num" and "factors", so you can use both types.

Your first question is answered in my video on Saturated Models and Deviance: kzfaq.info/get/bejne/b7pgqs98ycvbZn0.html For your second question, the answer is "you are correct!". :)

Hooray! :)

Unfortunately, not off the top of my head. :(

If you want your model to be interpretable - in that you can look at the parameter value and make sense out of them - then removing correlated variables is a good idea and factor transformation and PCA can help with that. On the other hand, if you want to use your model to make the best predictions, then correlated variables are fine and can improve predictions. If you are interested in the details behind Logistic Regression, then check out these other StatQuests: General Overview: kzfaq.info/get/bejne/r6-JfrVl2M3eeWw.html Interpreting Coefficients: kzfaq.info/get/bejne/rLRllrF_l5Osh3k.html Fitting the Model to Data with Maximum Likelihood: kzfaq.info/get/bejne/eMx7lNGdlse3d2Q.html Calculating R-squared and its p-value: kzfaq.info/get/bejne/rt52jNWgnbfZiHU.html

I'm not sure. I've never done multinomial logistic regression.

AIC is on the to-do list. Since asked for it, I'll bump it up a little closer to the top.

Double BAM!!! That's sounds great! Awesome video, all of them are so helpful to understand logistic regression!

@@565-FENRIR Hooray! Thank you! :)

I cover the random forest method in this video: kzfaq.info/get/bejne/bKuIg7yrx8ywc3k.html (the theory is here: kzfaq.info/get/bejne/qbdoapOSubHVmYE.html )