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Oct 26 2010
The ANOVA table explains the sources of variation.
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25 Responses to “Regression #4: ANOVA table in regression”
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@hannalindberg the practical way is to simply look it up in the F distribution table using the appropriate degrees of freedom
@danvedman a one-way ANOVA would be appropriate
Hi I just want to ask. I’m not sure if it’s the right test or not but say I have 3 independent samples and I want to see if there’s a signifcant difference between their means or not (one sample is a control sample). What test would I conduct?
top man
thank you very much this has been very helpful!!
How do you calculate the p-value?
graphically:
ESS: distance from green to red
RSS: distance from blue to green
thanks for your efforts, it was very helpful.
I still do not find very clear, graphicaly which is the RSS and which the ESS?
Is the RSS the difference between the observation and the average and the ESS the difference between the the regression and the average?
Good one…
God bless your soul
thanks for this video. helps clarify
Hi trknigatu,
need one small clarification.
So, what is regression sum of squares? is it the sum of the square of the distances between regression line and the average line(i.e. red line)
Exccellent tutorial. thank you indeed.
Tariku, Ethiopia
ohhh stats is boring
thanks for your time in making this video it has helped clear up some issues of mine
thanks harper. can i just ask how do you know extent of the strength of relationship between teh independent and dependent variable. Ur calculations show that the coefficient of determination is 0.86 which means the line u have fitted is explaining 86% of the variation in the model? that’s gr8 but what’s extent of the strength of relationship? how do you find it? using Adjusted R Square?
I majored in Statistics
Thank you very much… this video really helped…
Thank you Sir, clearly explained, extremly helpful
K-from Zambia
Wow, that was great! Thank you!
the old-school way would be to previously choose a critical F number from the F tables and compare that to the F ratio that you get from your data. if that ratio’s bigger than the critical F, then you reject the null. but nowadays PCs simplfy everything w/ their magic p-values!
Thanks for this vid. At 7:30 you say we reject the null hypothesis because F is 52.7 which is ‘quite high’. What is this to be compared to to decide if it is relevant or high enough? What is the range? Would 2 have been low enough to accept the null hypothesis and 1000 too high?!
Excellent videos, very very helpful.
Regards from London.
Very helpful.Regards from Beijing.
Very Helpful!
Regards from Taiwan