![Mark Himmelstein](/img/default-banner.jpg)
- Видео 76
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Mark Himmelstein
Добавлен 9 авг 2020
Short videos explaining statistical concepts for psychology graduate students.
Видео
Lab 13 Other Estimation Methods for EFA
Просмотров 283 года назад
Lab 13 Other Estimation Methods for EFA
Lab 13 Odds and Ends Heywood Cases, Oblique Rotation, and CFA
Просмотров 2163 года назад
Lab 13 Odds and Ends Heywood Cases, Oblique Rotation, and CFA
Lab 12 Quadratic Discriminant Analysis
Просмотров 1,2 тыс.3 года назад
Lab 12 Quadratic Discriminant Analysis
Lab 12 Discriminating Between More than Two Groups
Просмотров 403 года назад
Lab 12 Discriminating Between More than Two Groups
Lab 11 Evaluating Discriminant Functions and Classification Rules
Просмотров 403 года назад
Lab 11 Evaluating Discriminant Functions and Classification Rules
Lab 11 Types of Errors, Base Rates, and Costs
Просмотров 373 года назад
Lab 11 Types of Errors, Base Rates, and Costs
Lab 11 Discrimination and Classification
Просмотров 8203 года назад
Lab 11 Discrimination and Classification
Lab 10 Making Use of Canonical Correlation Analysis
Просмотров 4033 года назад
Lab 10 Making Use of Canonical Correlation Analysis
Lab 10 Canonical Correlation Analysis
Просмотров 6083 года назад
Lab 10 Canonical Correlation Analysis
Lab 9 Inferences in Multiple Regression
Просмотров 483 года назад
Lab 9 Inferences in Multiple Regression
Lab 9 Multivariate Multiple Regression
Просмотров 4,8 тыс.3 года назад
Lab 9 Multivariate Multiple Regression
Lab 9 Multiple Regression in Matrix Notation
Просмотров 873 года назад
Lab 9 Multiple Regression in Matrix Notation
Lab 8 Complex Repeated Measures Designs
Просмотров 183 года назад
Lab 8 Complex Repeated Measures Designs
Lab 7 Multivariate Contrasts for Several Groups
Просмотров 383 года назад
Lab 7 Multivariate Contrasts for Several Groups
Lab 6 Inferences About Two Mean Vectors
Просмотров 6453 года назад
Lab 6 Inferences About Two Mean Vectors
Lab 5 Paired Data and Repeated Measures
Просмотров 313 года назад
Lab 5 Paired Data and Repeated Measures
Lab 4 The Multivariate Normal Distribution
Просмотров 513 года назад
Lab 4 The Multivariate Normal Distribution
Kiana Keys
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.
Where is the dataset ....can you give the dataset link?
Please can this be done with SPSS?
Great explanation. and I also like you put Trump above Biden!!
Thank you for this video! This was so helpful!
what about first and last name? Can they be positioned side by side instead of vertically stacked?
Is it possible to have your email address to make an enquiry?
Hi, it is very useful. In Johnson-Neyman plot, n.s. =not significant ? is it right?
Does the output from the anova() function run on the full model give the correct Wilks lambda for every predictor variable? I have been using this video as a guide for some of my own analyses, and in my case the output only gives the calculated Wilks lambda value for the last predictor variable in the model formula (I have even switched the order up). Am I doing something wrong?
Hello. Thank you! This was helpful. Question: Could I use multivariate multiple linear regression if I have 5 predictor variables, but only one is truly an IV and the others control variables? Of course I also have 3 DVs. I already ran it on Stata and seems to have worked (since all predictor variables are treated the same way), but technically I only identify one of my predictor variables as the IV of interest. Thank you so much!
Thank you.
Very helpful thank you! Especially where you explained F1 = level one fixed effects F2 = level 2 fixed effects v= random slopes m = random intercepts f = total fixed effects fv= total fixed effects + random slopes fvm= total fixed effects + random slopes + random intercepts I couldn't find that in the manual
Hi Mark. Thanks for your video. I have a question. How do you interpret the moderator effect of one variable since the Johnson-Neyman plot? I mean, in a practical way. Specifically when my previous analisys gave me a non-significant P-value. But when I run the J-N plot, it tells me that for some specific J-R region there is significance moderation. In a practical way, can I say there is a moderating effect? Under which circumstances? Thanks a lot in advance!
when u do ANOVA or the simple OLS,u may just test the whole(in other words,u just group them to "HIgh" and ''LOW"),. when u do J-N procedure,u test every spot of ur moderative variety.
@@lewisyicheng268 thanks a lot for your answer. Very useful
Hey, it's a really helpful video, thanks 😁
.......Bravo! 👍........ What an insightful Intro! 👍👍 ☄Can you please introduce Visual/Statistical Reporting via Stats iQ & Crosstabs? (Real examples will be appreciated). Merci beaucoup!👍
Great video.
Excellent video.