What is Central Limit Theorem in Statistics ? | Inferential statistics | Explained with Example

[@DigitalELearning]

👀 Hello Friends, 𝐉𝐨𝐢𝐧 my 𝑺𝒐𝒄𝒊𝒂𝒍 𝑴𝒆𝒅𝒊𝒂 𝑳𝒊𝒏𝒌 for regular updates: ------------------------------------------------------------------------------------------------------------------ 📸𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦 : instagram.com/digital_elearning/ 🐦𝐓𝐰𝐢𝐭𝐭𝐞𝐫: twitter.com/DigitalELEARNI1 📢𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦 : t.me/digital_e_learning007 🙋🏻‍♂𝐖𝐡𝐚𝐭𝐬𝐀𝐩𝐩 (𝐋𝐞𝐚𝐧 𝐬𝐢𝐱 𝐬𝐢𝐠𝐦𝐚) : whatsapp.com/channel/0029VaAUuqs4inooet3e4S3O 🙋🏻‍♂𝐖𝐡𝐚𝐭𝐬𝐀𝐩𝐩 (𝐒𝐢𝐱 𝐒𝐢𝐠𝐦𝐚 (𝟔σ ) : whatsapp.com/channel/0029VaEWKWbIiRovZpAduc0p 🙋🏻‍♂𝐖𝐡𝐚𝐭𝐬𝐀𝐩𝐩 (𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 & 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡) :whatsapp.com/channel/0029VaAGwzTDJ6H9S5Q4Ow0m 🙋🏻‍♂𝐖𝐡𝐚𝐭𝐬𝐀𝐩𝐩 (𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭) :whatsapp.com/channel/0029Va8slQdEAKWAhXoQK13B 🙋🏻‍♂𝐖𝐡𝐚𝐭𝐬𝐀𝐩𝐩 (𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐀𝐬𝐬𝐮𝐫𝐚𝐧𝐜𝐞 & 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐂𝐨𝐧𝐭𝐫𝐨𝐥) :whatsapp.com/channel/0029VaAKJBGKGGGDjcK4mw2h

[@prerana306]

Hello sir thanku so much

[@ybyjy7528]

I have a question, nowthat CLM holds when n > 30, why it is possible to apply CLM when 4 cats are chosen in your question?

[@ybyjy7528]

I'm not good at english, please pardon me for the mistakes in my words.

[@vidhithakker5092]

to apply CLT sample size should be greater or equal to 30 but in your example you have taken sample size of 4

[@abhaynishesh]

There are four samples and each sample contains more than 30 element

[@praveenchandrasrivastava6557]

@@abhaynishesh so the sample size is more than 30 right? and so we should divide the population mean with root of sample size and not by the root of number of samples.

[@abhaynishesh]

@@praveenchandrasrivastava6557 Yes Each sample has greater than 30 elements ( here more than 30 students whose height and weight are being taken),. And finding Z value he divided population standard deviation by root of no of samples

[@rasikajoshi4607]

very well explained .thanks

[@mufeedakp3931]

In example sample size is 4 , then how apply CLT

[@mitali8631]

What a nice explanation 🥰 concept is crystal clear 🥰🙂

[@DigitalELearning]

Thank you 🙂

[@pandiyarajc2941]

Kindly do some vedios on Project management RRCA Process capabilities study Sampling inspection

[@DigitalELearning]

Hi Pandiyaraj , Thanks for the suggestions. However, you will find some of these videos already in my Channel. Please check

[@nagaprasadnv6728]

Hi Sir Your vidros are very good for edicational and training purposes. Can u make a video on whole six sigma DMAIC

[@DigitalELearning]

Thank you! 👍 Already uploaded the video on DMAIC on my KZbin channel.

[@iamkraj]

how easily you explain sir 🙌🙌 great

[@DigitalELearning]

Thanks a lot Kraj

[@DaaniaKhalith]

Why subtracting 0.5 from z value, sir please explain

[@nskshivakumar2257]

It is not necessarily sir mistake

[@indranigogoi5190]

since it's a two tail test, we must take the half of the z value.

[@ImodhaHarshana]

It is not a 2 dies question.this is a cat question

[@DigitalELearning]

In normal distribution numerical questions, we sometimes subtract 0.5 from the z-value for two main reasons: 1. Continuity Correction :Subtracting 0.5 accounts for the fact that the probability of a specific value (like 2.5) is actually the probability between 2 and 3. 2) Area calculation for "less than or equal to" situations: Since we only want the probability greater than the z-value, we subtract 0.5 to account for the area already included in the table (below the z-value).

[@owaiskarni825]

Very well explained

[@DigitalELearning]

Thanks for liking

[@hamzaharris9716]

what if the last line of questions says find the probablity that weight lies between so and so..then what will we do with the Zvalues??plz answer coz tomorrow is my exam

[@DigitalELearning]

If the question asks for the probability that the weight lies between certain values, you find the Z-values for those values and then find the probability using the Z-table or calculator. The process is similar, just with different Z-values. Always make sure to check whether the Z-table or calculator provides probabilities for the specific range you're interested in.

[@user-mg2cf6hb9g]

Thank you

[@DigitalELearning]

You're welcome

[@nishatahmadrather6074]

Why 30?

[@DigitalELearning]

The "30 samples" rule in the context of the Central Limit Theorem (CLT) is a guideline suggesting that a sample size of 30 is often sufficient for the sampling distribution of the sample mean to be approximately normally distributed.

[@ambedkardhaba9077]

Awesome. He explains it very well.

[@DigitalELearning]

Ambedkar. Glad you liked it 😎

[@sebamalairoy1958]

z value =0.9994

[@DigitalELearning]

100 % correct Champ 😊

[@rahultech9510]

first ans true second flase

[@DigitalELearning]

Correct Rahul

[@mahendraprasadyadav806]

Add is not required

[@chenchunivedhak.s2415]

1. True 2. False

[@DigitalELearning]

100 % correct Champ 😊

[@keerthikababu8445]

1 a 2 b

[@DigitalELearning]

Correct Keerthika 😎

[@DigitalELearning]

👀 Hello Friends, 𝐉𝐨𝐢𝐧 my 𝐖𝐡𝐚𝐭𝐬𝐀𝐩𝐩 𝐂𝐡𝐚𝐧𝐧𝐞𝐥𝐬 for regular updates: ------------------------------------------------------------------------------------------------------------------ 1) 𝐋𝐞𝐚𝐧 𝐬𝐢𝐱 𝐬𝐢𝐠𝐦𝐚: whatsapp.com/channel/0029VaAUuqs4inooet3e4S3O 2) 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 & 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 : whatsapp.com/channel/0029VaAGwzTDJ6H9S5Q4Ow0m 3) 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭: whatsapp.com/channel/0029Va8slQdEAKWAhXoQK13B 4) 𝐒𝐢𝐱 𝐒𝐢𝐠𝐦𝐚 (𝟔σ ) : whatsapp.com/channel/0029VaEWKWbIiRovZpAduc0p 5) 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐀𝐬𝐬𝐮𝐫𝐚𝐧𝐜𝐞 & 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 : whatsapp.com/channel/0029VaAKJBGKGGGDjcK4mw2h