Видео

Hi, my name is Clara Fernández. I am currently doing my a master's degree in Egyptology, and I have a special interest in Karnak and its developments over time. I tried to download the VSim Digital Karnak prototype, but I can't seem to find it. I was wondering if you could provide the file. Thank you so much!

been doing this for a while as coder. It is simple since everything is comp science. HOW DO I START EARNING, HOW DO I START A CONSULTANCY

pound the life outta me bro

Great video!!!

Excellent Video

Excellent. Very helpful

Fantastic walk through!

Fantastic! Thanks for sharing! :)

Hello! Thank You very much! Very helpful video! Can I get codings used in this course? It would be very helpful for me. Thanks in advance!

Thank you for the wonderful tutorial. Yoh made it easy to follow along and work through the lab. I may have missed something, but I found that within the style.css code I needed to add a sidebar statement listed below. Without this, the sidebar would draw under the footer on the webpage. This may also just be because of an update to how css is read, but hopefully this helps anyone having similar issues. .sidebar { grid-area: sidebar; padding:10px; background-color: rgb(175, 175, 175); }

There some inaccuracies in the dummy variable discussion. Categorical variables have levels - not categories . To be more precise these are factor variables. This distinction is made later in the lecture but should have made from the outset to avoid confusion.

Nice and clear overview.

Thanks.

can i get a link to the do file. cant see it

'We must mitigate the climate problem...' You're what we call an educated idiot. There is a CO2 dearth at this time. NOT a 'climate change' induced by human activity. SMH

At 57:39 you say "make sure you are okay with setting the scale of that item". Lets say all items are measured on the same scale (likert 0 to 5). How do we assess whether we are okay with setting a particular item as the reference item? Spontaneously Im thinking that in the case of a likert scale where all items are on the same scale, we would atleast want to ensure that the item that is used as the reference item is an item where each answer option (0, 1, 2, 3, 4, 5) has been used "equally" often? For example, using an item where 4 was not chosen by a single respondent - seems like a bad item to use as a reference item? Is this at all true?

In 2:03:19, he said "I am just showing you the real world", I died.

Thanks for the informative video. For time-varying Cox model (2:32:50), do we still need to check PH assumption for the time-varying covariate? Thanks!

Can you please suggest how to perform survival analysis for paired data for longitudinal studies.

Great explanation. Has anyone ever thought of using these ideas for a language model? It could have continuous learning built in, due to the Bayesian Approach.

Excellent. Do you have a video for latent variable means comparison please?

Thanks for your very educative talks. I found very helpful.

General comment Prof. , @9.07, the real part id the natural frequency and wi is the growth rate of the eigenvalue problem. Thats the general convention.

Excellent teaching, thank you very much

Provide simulation with macine learning matlab etc

Definitely, the best R tutorial I have ever come across on RUclips! Good stuff!

Thank you for such a detailed introduction to SEM. I have a question - at 46:18 the model is called a "saturated model" because the df = 0. However I have been reading that a "saturated model" occurs when there are the same number of parameters as there are data points. In this case, the model has 5 parameters and 500 data points. Is it still a saturated model then?

very insightful, I would wish to know how to use these commands after doing missing imputation with MICE

I have learned so much, gracias!

how do we get the value of the latent ? not the variance but the value

Thanks a lot! Greetings from University of Adelaide.

Fabulous Lecture

YOUR THE BEST, YOU JUST SIMPLIFIED EVERYTHING THANKYOU SO MUCH PROFESSOR

great video! Thanks. What do you think of using the estimation method DWLS instead of ML for ordinal items (such as those in the video "strongly disagree to strongly agree")? I have just read a paper (Reimann et al. 2024) where they used DWLS in a 2-factor CFA and got a great RMSEA (0.01). Their rationale was that the responses are ordinal and not continuous. Interestingly, I could run the same data set with ML and got an RMSEA = 0.13. Obviously a big difference. In papers, authors often do not even mention their estimation method.

Wow, this is outstanding! Will it be possible to do similar in R program

Wow..what a tutorial Thank you so much❤❤

For a complete novice to Mplus this was a great introductory tutorial. Thank you.

I am in an intermediate statistics and research course for my doctoral program. I began reading Hayes and found myself dissociating with glazed stares. This workshop has provided an informative path to at least begin to consume the material with some understanding. Your assistance with downloading the PROCESS macro was also very helpful. I hope to find moderation and conditional process analysis workshops from this source as well. Thank you very much!!

Matlab? Mathematica? Maple? Python? R? Thanks.

Once I save the folium html, after two days, the interactive visualization stops to display. What could be the problem?

Thank you so much. Very helpful!

Great video thank you!

Thanks for the great tutorial. At 1:51:35, is the wt.loss also by the time of beginning the study (treatment), like the age variable? Thanks.

My rmse is more than 0.8

My cfi is 1 and rmse NA

Thank you very much for an excellent lecture on CFA. Just a small comment/correction on the very final exercise: the Test statistic for the User Model, is 554.191. In your solution, it is 562.790. and the Degree of Freedom is 20, not 21. By putting these numbers in the formula, we get the correct CFI, which is 0.871 (rounded).

The 'li' syntax didn't work for me. It returned errors

Still can't believe this is free to watch for everyone. Thank you so much.

So much rambling in this presentation. The content gets lost in it.

Great video! Great delivery and insights!