Central limit theorem | Inferential statistics | Probability and Statistics | Khan Academy | Forex
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Introduction to the central limit theorem and the sampling distribution of the mean Watch the next lesson: https://www.khanacademy.org/math/probability/statistics-inferential/sampling_distribution/v/sampling-distribution-of-the-sample-mean?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/statistics-inferential/normal_distribution/v/ck12-org-more-empirical-rule-and-z-score-practice?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it! About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to KhanAcademy’s Probability and Statistics channel: https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy
Comments
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Drinking game: drink whenever you hear the word "sample"
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in one word, gazilion!
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Heck yeah, this is a great motivating video... gives an outline of the idea and why it's so cool and important!
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I mean this is brilliant! Got me thinking and understanding deeply.
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If x = 1,3,4 or 6 and the sample size is 4, there would be 4*4*4*4 possibilities i.e. 4^4 possibilities =a maximum of 256 possible outcomes so by taking 10,000 samples you will be repeating each 1 about 40 times.
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how do you denote the number of times you have samples? example: so you say sample size n=4, but 100 of those samples, how do we denote that?
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He says that with an infinite sample size, it approaches a normal distribution, but shouldn't it just be a straight line at the mean? (albeit this is a normal distribution with a standard deviation of 0)
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Simple is beautiful
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Thank u so very much u helped me a lot
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Best explanation out there. Thanks, Sal!
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THANK YOU!!!
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you nailed it. thanx a lot
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You explain this better than a textbook. You are a great!
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Sal.... Your my rockstar!
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Very, very cool stuff. Also, you're obviously a smart guy, Sal. But at the same time, you're incredibly accommodating to us students. Thank you for your sincerity and empathy, sir.
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I love statistics!
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the thumb rule for applying central limit theorem is n should be at least 30. its not valid for samples smaller than 30!
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damn this is actually really cool
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Yeah fuck this I'm going to bed
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What happens to the standart deviation if we work on means of sample instead of means of one large sample ? Someooone? :)