Central Limit Theorem Practice Problem #1
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- Опубліковано 29 бер 2013
- This video describes the solving process for Mr. Roberg's Central Limit Theorem Practice Problem #1.
Here is my book (linked with 100 UA-cam videos) that explains all of basic / AP Statistics: goo.gl/t9pfIj
Thank you so much! I am having such a difficult time with these complicated formulas. You explained it very well with no frills and I appreciate that very much. I may just pass afterall...
Thank you. I am glad I could help.
Grace Kirkendall I'm tutoring someone and we both got stuck. It's been over 20 years since I've had this level of statistics. I'm very happy to have find such good examples. Thanks
best of the best explanation, i never understand CLT but after this video I really DO, thank you
Please show the questions next time but besides that I love the way you explained the concept.
You explained so well
You got my first comment ever on a video. That was the best explanation has been done so far, and believe me I have watched more than 100 videos on youtube about stats. You explained and brigthened me perfectly :)
Comment on my vid now
THIS IS EXACTLY WHAT I NEEDED THANK YOU
Thanks for putting up a clearer picture of CLT !
Thank you!!!! I was struggling with this so much. Confusion obliterated. Lol :)
I always revert back to your videos
these videos are a godsend
This is a very helpful video but it would be nice if you at least showed the picture of the problem.
THIS HELPED SO MUCH THANK YOU
You sound like Bill Gates.
Thanks for teaching me stats bro!
Good explanation.
actually you're good at explaining things and I understand it very well but please give us the questions.
Thanks man, appreciate it.
Great explanation
how do you know which solution to follow, a or b?
What if I dont have that chart how do I do it with a calculator
thanks for your videos sir
IF the underlying population distribution is NOT NORMAL, and we have samples less than 30. Let's say the samples are size
n = 5. I know the distribution of the sample means will not be normal according to the CLT. However, will the distribution have the same mean as the population mean, and will the variance be equal to the variance of the population divided by 5? Please let me know? thanks?
what if you do not have a stabdard deviation and a fraction as a mean?
thank you
Thank you!!
Amazing explanation! Just one flaw: You did not explain what n was or how you got n=100 from, thankfully the comment section helped with that. Everything else is top notch.
In part a, how can you use the normal distribution chart to find the probability for the z-score if you do not know the salary is normally distributed? I mean, since you did it, I assume it was normally distributed, but I think it would have been good if you stressed the point.
Hi Shashank, the central limit theorem states that sample data is distributed normally across whole population.
That is not what the theorem states. The sample MEAN is approximately normally distributed when sample size n is large.
@@sargamgupta7194 Let's suppose he drew a sample of size n from a population having a distribution whatsoever.
Now this sample will be normally distributed according to CLT.
Right?
Hence he has assumed a normal distribution.
I guess this is correct!
@@Freeeeeeeezing Could you please elaborate what you mean to say!
So does the problem state that the distribution is normal? someone with the book could reply. Problem 1a seems unrelated to CLT.
What does the central limit theorem tell us about samples from a bi-modal distribution?
That as the sample size increases the sampling distribution of a bi-modal distribution becomes approximately normal.
thank you!!! muah muah
Thank you sir
Sir from where u got small n value =100
Mr. Roberg is an AP and regular high school statistics teacher
What if you get -6.61 after you solve it in the Z-score? How can I find that in the z-score table? if it doesn't go further, it stops in -3.4
well, then the cumulative probability (area under the pdf) is zero
Where did you get the 100 as the n in part b
TheRealDavieLondon n=100, number of Random variables or (teachers)
that was the sample size
thank u
I want to know the skills needed to be able to do advanced statistical work such as proving the theorems etc. I am not in grad school but I glanced over the stuff inside the textbooks and it looks like only aliens from outer space are able to do it. Is the advanced stuff only for people with High IQ's or something? I have no clue how people are able to figure that stuff out.
EvaSlash learn the basics and slowly build off of it
It would help to take a proof course and an analysis course. This is not graduate level work. I'm an undergrad, I'm terrible at math, and I've taken both classes.
take a proof course and start digging into more abstract mathematics
Lotta patience if you really want to learn. :)
its good keep it up
Why use a table when you could ask a snake?
def normalDist(mean, sd, x):
return 1.0 - ((1.0 + math.erf((x - mean) / (sd * math.sqrt(2)))) / 2)
süpersin hocam
Jesus Christ bro you make this shit seem so easy like I understand it now haha
where did you get n = 100?
That's because he was looking at what you expect to see from a sample of 100 teachers.
Isn't it normal distribution??? why you using the tables??? is the same as normal distribution?
It looks same as we are solving a standardization problem. Is there a difference ?
You can take any Normally distributed data and standardize it (get a z-score) than it is a standardization problem. So, no difference.
In Part 'a' it will be true only if the distribution is normal. No where is the question it mentions that?
Do you have the original question? I am actually trying to find them, I thought I had linked the questions to this video, but I do not see them. I do agree that it needs to be stated that the distribution is Normal in the original question for part a to be correct.
Can i get the Z Chart ???
what if i am using an absolute value chart for z-score?
All your answers should be either what it says in your chart or the complement (1 minus what it says in your chart). If you sketch the curve, you should be able to determine which probability it is.
when do i need use the compliment? when i want to know the area to the left of the z score? or when the z-score is negative?
Assuming that the given mean and standard deviation have not changed, find the probability of randomly selecting 40 cigarettes with a mean of 0.882 g or less.
Reduced Nicotine in Cigarettes The amounts of nicotine in HOPE cigarettes have a mean of 0.941 g and a standard deviation of 0.313 g. The Huntington Tobacco Company, which produces HOPE cigarettes, claims that it has now reduced the amount of nicotine. The supporting evidence consists of a sample of 40 cigarettes with a mean nicotine amount of 0.882 g.
one it would have been nice if the question was already printed out in text....on the screen.
Two.....Where is the 51,000 he talks about at the beginning of the video...where does it come from?
Three....Why is the area 1?
...i feel so confused.......SOS
Did u ever figure it out lol
@@Zuggest-v8g I didn't.
If you have already figured it out.
Please help.
Both in part a and part b he has drawn a sample from a population right?
What is the difference between the two parts?
How n = 100 ? Why did we assume n = 100 ?
Where is the question
I'm confused 😂 and also my table is all in positive sign
This is pretty late, but if you want a z-score that is negative you can use 1- (the z score of the positive version) to get the negative one.
The z score of 3.0 is .9987 and the z score of -3.0 is 1-.9987=.0013
Hope this helps!
Álvaro Carvalho Did you read it right? You should start with the left column for the first digit before the decimal and the first digit after. Then the row up top is for the second decimal spot. Also, these numbers represent the amount of area under that curve that has an absolute value of 3 standard deviations away from the mean so that high of a percentage would be too much.
in this video z= x-μ*n / (Square root of Var[x]/Square root of n)
in my lecture slide z = x-μ / Square root of (n*Var[x])
wtf
You have used wrong formula brother for sigma that is sigma is equal to s/squared root n
I believe s over square root n is for samples. The Greek sigma is for population.
Muhammad Bilal n=1 for this first question
Yup I’m abt to fail 👍
6,75 / sq root of 100 = 6,75 / 10 = 0,675 -> not 675
oh he is right the number is six thousand seven hundred and fifty then divided by 10 is equal to 675 ( 6750/10 = 675)
Anyone here because the Anderson textbook on Prob and Stats is too damn complicated ? lmao
so what if I dont have that little stupid chart? Bad.
you sound like bill gates. lol. great video tho
6,750 divided by the sq root of 100 = 67,500 not 675 smh
tf are you talking about? The sq root of 100 is 10. 6750/10 is not 67,500. smh
oh he is right the number is six thousand seven hundred and fifty then divided by 10 is equal to 675 ( 6750/10 = 675)
Is the salary distribution normal? I never see that.