Thank you for your work! I am currently watching all of your videos to refresh some statistics topics. Are you still working on new videos? A video about post-hoc tests for a MANOVA would be appreciated.
Best explanation so far. My question is, this specific example is not a 1-way Monova because of the bacteria and viral as two factors? If it’s a one way Monova, can you please elaborate on that? Thanks for the video.
This is a one-way MANOVA because we only have one factor, the type of infection. Suppose that we would also include sex as an independent variable, then we will have a two-way MANOVA because we will then have two factors.
If you like this video, you can buy the corresponding ebook (plain PDF) that I used to record this video at www.tilestats.com/ On this page, you will also find all of my videos in a logical order.
If you violate the assumptions of MANOVA (or have ordinal data), the rankMANOVA might be an alternative since it is a non-parametric test based on ranks.
The F-stat requires a lot of calculations and is different for the four different statistics. That is why I did not include it in the video. I would recommend this page for calculating the F-stat by hand: ncss-wpengine.netdna-ssl.com/wp-content/themes/ncss/pdf/Procedures/NCSS/Multivariate_Analysis_of_Variance-MANOVA.pdf Also, if you have only two groups, you can instead use the Hotelling's T-square test, which has simpler math, explained in this video: ua-cam.com/video/RmktDvMrhGw/v-deo.html
@@tilestatsoh okay, can I ask some of your references of research? MANOVA is my report in statistics and I am basing my explanation off of your explanation since you executed it easily and clearly
The number of DV = number of eigenvalues. The number of groups does not effect how many eigenvalues we will compute. It seems like you have 3 DV -> 3 eigenvalues.
@@tilestats Awesome thanks! Here is another question for you :) Using the regress procedure, a researcher tests the interaction between a binary variable condition (0=control, 1=experimental) and a numeric variable, age. She enters condition using the indicator coding i. She enters age as centred around the mean. She finds that the simple slope of age in the control condition is 1.0, and the coefficient for the interaction is -.5 What will the simple slope of age be in the experimental condition?
That is calculated by the numerator in the equation for the covariance. Watch this video, starting at around 6 min, to see how this is calculated: ua-cam.com/video/2bcmklvrXTQ/v-deo.html
See my answer to one of the comments below for how to calculate the F-statistics. Once you have the F-statistics, you can use an F-distribution to compute the p-value.
great videos . its summary and not forget the important points . Thanks Prof
Thank you for your work! I am currently watching all of your videos to refresh some statistics topics. Are you still working on new videos? A video about post-hoc tests for a MANOVA would be appreciated.
Thank you! You can run an ANOVA after a significant MANOVA, and then use some method for multiple comparison, as I have several videos on.
It was an awesome lecture! Thank you so much!!!
Best explanation so far. My question is, this specific example is not a 1-way Monova because of the bacteria and viral as two factors? If it’s a one way Monova, can you please elaborate on that? Thanks for the video.
This is a one-way MANOVA because we only have one factor, the type of infection. Suppose that we would also include sex as an independent variable, then we will have a two-way MANOVA because we will then have two factors.
Here is the nonparametric alternativ to MANOVA
PERMANOVA and permutation tests - explained
ua-cam.com/video/v7u8lHgoWig/v-deo.html
If you like this video, you can buy the corresponding ebook (plain PDF) that I used to record this video at
www.tilestats.com/
On this page, you will also find all of my videos in a logical order.
May I ask if the example data is bivariate?
Thank you so much for these videos!!!
Thank you!
Manova: excellent didactic
Is the example data is bivariate data?
Yes, since the data set consists of two variables.
could you do a video on factorial manova? I'm so lost as the text i'm reading does a poor job of explaining the formulas, notations, etc
I do not have any material for that at the moment. I'm planning to do factorial ANOVA first. I hope to do this in the future.
@@tilestats sounds good! Love your channel by the way :)
Are there differences between MANOVA and rank MANOVA?
If you violate the assumptions of MANOVA (or have ordinal data), the rankMANOVA might be an alternative since it is a non-parametric test based on ranks.
Thank you Sir
Thank you!
How did you calculate P Value and F Statistic? Can you please point to any documentation where it is explained?
The F-stat requires a lot of calculations and is different for the four different statistics. That is why I did not include it in the video. I would recommend this page for calculating the F-stat by hand:
ncss-wpengine.netdna-ssl.com/wp-content/themes/ncss/pdf/Procedures/NCSS/Multivariate_Analysis_of_Variance-MANOVA.pdf
Also, if you have only two groups, you can instead use the Hotelling's T-square test, which has simpler math, explained in this video:
ua-cam.com/video/RmktDvMrhGw/v-deo.html
Can you tell me your reference on the MANOVA? What book is it
I have no book for my examples. It is based on my own research.
@@tilestatsoh okay, can I ask some of your references of research? MANOVA is my report in statistics and I am basing my explanation off of your explanation since you executed it easily and clearly
Will you always have 2 Eigen values, no matter how many IV's or DV's you use?
No, if you, for example, would have a third DV, you will instead get 3-by-3 matrices and thus 3 eigenvalues and 3 eigenvectors, and so forth.
The number of DV = number of eigenvalues. The number of groups does not effect how many eigenvalues we will compute. It seems like you have 3 DV -> 3 eigenvalues.
@@tilestats Awesome thanks! Here is another question for you :) Using the regress procedure, a researcher tests the interaction between a binary variable condition (0=control, 1=experimental) and a numeric variable, age. She enters condition using the indicator coding i. She enters age as centred around the mean.
She finds that the simple slope of age in the control condition is 1.0, and the coefficient for the interaction is -.5 What will the simple slope of age be in the experimental condition?
I have a video on multiple linear regression with interaction (second example) that might help you:
ua-cam.com/video/AP_K7SaKkIE/v-deo.html
super helpful!!!
Great!
Thank you so much for explanation. But could you please explain how to calculate the -121 values in SSCPt matrix (9:30)
That is calculated by the numerator in the equation for the covariance. Watch this video, starting at around 6 min, to see how this is calculated:
ua-cam.com/video/2bcmklvrXTQ/v-deo.html
@@tilestats thank you.
How do you calculate the p-value can someone explain it please
See my answer to one of the comments below for how to calculate the F-statistics. Once you have the F-statistics, you can use an F-distribution to compute the p-value.
Manowar
Manowar