ANOVA CRD, Problem 1

In this video, we demonstrate how to test a hypothesis that a set of treatment means are all the same. We perform this test using ANOVA for a completely randomized design experiment (CRD experiment). In this example on the topic, we will work out the calculations by hand.
This video is part of the content available for free at www.statsprofe...

[@andreakyletangonan6621]

I'm beyond grateful for this video😭😭 It saved me from my laboratory activity 💜💜 Thank you so much

[@dmcguckian]

It’s great to hear that it is helpful!

[@senaberlyndah4632]

Wow this is really simpler than I thought... Thank you Sir!

[@dmcguckian]

You’re welcome

[@kirtirana2777]

Finally, I understood that topic, Thanks Mr.Dane😁👍👍

[@arjaydeleon5996]

Thank you very much. I learned a lot in this almost 22 minute video than in our 3 hours class. I was so confuse during our lecture. Thank you very much sir.

[@franxzukic03]

Clarified my confusion from the lecture, thank you 💯

[@dmcguckian]

Great!

[@davidntumba341]

Am very grateful for this video, keep it up.

[@chiderastella3939]

Thank you sooo much...my exam is close and you just made me understand it . I'm super excited!!🎉🎉❤❤❤

[@dmcguckian]

Great!

[@themoralshow902]

Fans from Sri Lanka 😊

[@dmcguckian]

😊

[@whitechair9293]

Thank you kind sir, u teached this more clearly than my prof

[@dmcguckian]

You’re welcome ☺️

[@sahithyadharmakeerthi7410]

Thank u very much sir.. very much supportive.. ❤

[@0075prasanna]

Thank You

[@aachinthaka7311]

Thanks a lot. Very nicely explained.

[@dmcguckian]

Thank you!

[@frankthabang6016]

THIS IS HELPFUL, IF I MAY ASK SIR WHY IS IT ONE TAIL?

[@mvuselelomvuseh1569]

Thank You so much

[@kapoorrhea01]

Thank you so so so so so so much

[@labomba8206]

thanksssssssss 😭😭❤️❤️
explanation of parameter estimate for the model

[@Nelkha_Ngwako]

Thank you very much ❤

[@dmcguckian]

Thanks for watching!

[@beast2542]

well appreciated. 🙏

[@dmcguckian]

Thanks

[@harishussain9803]

Wao so welll like this ❤️ love 💕 from Pakistan

[@dmcguckian]

Thank you!

[@kujayasinghe9199]

Thank you so much for this video!
I really admire your teaching style!
Btw, the answer I got for sum of treatments is 92.942925. Why am I not getting the answer you have there? Please clarify! Regards

[@Samy-xt7nu]

Immensely helpful

[@dmcguckian]

Glad to hear it was helpful!

[@saciidmohamed4946]

Thanks

[@dmcguckian]

You’re welcome

[@sullivandwyer9230]

blessed

[@emmanuellaokoro901]

Thank you so much , this was absolutely helpful! pls what figures would we get to be able to support Ho and reject Ha? .

[@robinakhaliq5451]

Sir If we have 30 values in our table so it's very difficult , time taking to find total some of square .... So do you know any shortcut to calculate total some of square by using calculator

[@dmcguckian]

It depends on the calculator. Graphing calculators allow you to calculate the sum of squares with a built in program or you can enter everything into a list and then tell the calculator to square the list and tell it to sum the list. For my students, I try to give them the sum of the squared values on the exam to save them time.

[@sel9358]

Where did 92,97 come from?

[@evanpenas9594]

is two treatments enough for crd?

[@dmcguckian]

Yes, but then the test is equivalent to the independent t test.

[@mercy_amoka]

Thank you sir, for helping students like me understand this topic better, I have a question tho, this Correction Factor explained in this video, is it necessary to square the sum of the Yij values before dividing by n? because I noticed some people don't.

[@dmcguckian]

Yes, it needs to be squared if you’re using the approach I used in the video.

[@mercy_amoka]

@@dmcguckian Alright sir, thanks for clarifying.🙏

[@eternal6323]

So basically CRD is for one way anova, and rbd is for two way?

[@dmcguckian]

Yes, but the RBD assumes that there is no interaction between the two factors.

[@fredrickantwiafrifa9364]

Thanks so much. The only problem I have is, you skipped something around the summation of Yi squared - CF so I don't get that part. Any help?

[@dmcguckian]

The sum of yi squared is found by squaring every value in the original data and adding them together. If you had 3 values only, 2, 6, and 10, you would get: 4+36+100=140. To get the correction factor, you add the original data values together and square the sum. Then divide that by the number of values: (2+6+10)=18, then we perform 18(18)/3=108

[@fredrickantwiafrifa9364]

@@dmcguckian Thanks so much. I found it in the problem 2.

[@jessiequilantang3298]

why was the df decreased by one?

[@dmcguckian]

Hi Jessie, there are a few degrees of freedom in this problem, so I am not sure which one you are asking about. However, in general, for every estimate the data is used for the number of values is reduced by one. For example, if n values are used to estimate the mean, you would have n-1 degrees of freedom.

[@jessiequilantang3298]

@@dmcguckian oh thank you I literally forgot about that AHHAHAHA

[@ayas-3390]

You should learn how to use calculators for these calculation😎

[@dmcguckian]

😆 I have videos on that if you need to learn.

[@florahachote6847]

Thanks

[@Hamdan_Travel_Tourism]

Informative thank you so much

[@lewisnunu8451]

Thanks

[@dmcguckian]

You’re welcome