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Pythagoras0 (토론 | 기여)님의 2020년 12월 26일 (토) 04:03 판 (→‎메타데이터: 새 문단)
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  1. The Central Limit Theorem (CLT) is possibly the most famous theorem in all of statistics, being widely used in any field that wants to infer something or make predictions from gathered data.[1]
  2. The most common example of the CLT in action is when considering a binomial distribution.[1]
  3. Remember, the CLT states that the averages or sums of \({i.i.d.}\) random variables will resemble a normal distribution.[1]
  4. A question to answer regarding the CLT is how big we need \(n\) to be.[1]
  5. The central limit theorem states that the sampling distribution of the mean approaches a normal distribution, as the sample size increases.[2]
  6. • Sample size equal to or greater than 30 are required for the central limit theorem to hold true.[2]
  7. Because I want to show you the power of the central limit theorem.[3]
  8. The central limit theorem would have still applied.[3]
  9. As a general rule, sample sizes equal to or greater than 30 are deemed sufficient for the CLT to hold, meaning that the distribution of the sample means is fairly normally distributed.[4]
  10. The CLT is useful when examining the returns of an individual stock or broader indices, because the analysis is simple, due to the relative ease of generating the necessary financial data.[4]
  11. At least 30 randomly selected stocks, across various sectors, must be sampled, for the central limit theorem to hold.[4]
  12. That’s right, the idea that lets us explore the vast possibilities of the data we are given springs from CLT.[5]
  13. We will understand the concept of Central Limit Theorem (CLT) in this article.[5]
  14. Let’s understand the central limit theorem with the help of an example.[5]
  15. If you take your learning through videos, check out the below introduction to the central limit theorem.[5]
  16. Here’s what the Central Limit Theorem is saying, graphically.[6]
  17. An essential component of the Central Limit Theorem is that the average of your sample means will be the population mean.[6]
  18. In other words, the remaining small amounts of variation can be described by the central limit theorem, and the remaining variation will typically approximate a normal distribution.[7]
  19. Generally speaking, a sample size of 30 or more is considered to be large enough for the central limit theorem to take effect.[8]
  20. Although it might not be frequently discussed by name outside of statistical circles, the Central Limit Theorem is an important concept.[8]
  21. Before illustrating the use of the Central Limit Theorem (CLT) we will first illustrate the result.[9]
  22. In order for the result of the CLT to hold, the sample must be sufficiently large (n > 30).[9]
  23. This population is not normally distributed, but the Central Limit Theorem will apply if n > 30.[9]
  24. Note that n=10 does not meet the criterion for the Central Limit Theorem, and the small samples on the right give a distribution that is not quite normal.[9]
  25. Central Limit Theorem Let X 1 , X 2 , … , X n be a sample from a population having mean μ and standard deviation σ.[10]
  26. Indeed, one of the first uses of the central limit theorem was to provide a theoretical justification of the empirical fact that measurement errors tend to be normally distributed.[10]
  27. That is, by regarding an error in measurement as being composed of the sum of a large number of small independent errors, the central limit theorem implies that it should be approximately normal.[10]
  28. Therefore, by the central limit theorem, the total measurement error will approximately follow a normal distribution.[10]
  29. The mathematical statement of the central limit theorem is as follows.[11]
  30. If this procedure is performed many times, the central limit theorem says that the probability distribution of the average will closely approximate a normal distribution.[12]
  31. The convergence in the central limit theorem is uniform because the limiting cumulative distribution function is continuous.[12]
  32. The central limit theorem applies in particular to sums of independent and identically distributed discrete random variables.[12]
  33. The law of the iterated logarithm specifies what is happening "in between" the law of large numbers and the central limit theorem.[12]
  34. Kallenberg (1997) gives a six-line proof of the central limit theorem.[13]
  35. The really good news is that if the CLT did not exist, many familiar statistical methods would not be valid.[14]
  36. The simple part of the CLT is that for any sample of N independent determinations, the means of n values tend to a normal distribution irrespective of the underlying population distribution.[14]
  37. When referring to this article, please cite it as C. Burgess, "Distribution of Data: The Central Limit Theorem," Pharmaceutical Technology 43 (10) 2019.[14]
  38. According to the Central Limit Theorem, if we repeatedly take samples from this distribution then the frequency distribution of the sample means will be normally distributed.[15]
  39. Here, we state a version of the CLT that applies to i.i.d. random variables.[16]
  40. To get a feeling for the CLT, let us look at some examples.[16]
  41. The importance of the central limit theorem stems from the fact that, in many real applications, a certain random variable of interest is a sum of a large number of independent random variables.[16]
  42. In these situations, we are often able to use the CLT to justify using the normal distribution.[16]
  43. Click "Show Normal curve" to compare this distribution with the Normal curve predicted by the Central Limit Theorem.[17]
  44. The Central Limit Theorem says that the distribution of sample means of n observations from any population with finite variance gets closer and closer to a Normal distribution as n increases.[17]
  45. This applet illustrates the Central Limit Theorem by allowing you to generate thousands of samples with various sizes n from a exponential, uniform, or Normal population distribution.[17]
  46. You can then compare the distribution of sample means against the Normal distribution with the standard deviation predicted by the Central Limit Theorem.[17]
  47. The CLT applies to the case of non-identical distributions so long as the set of distributions is bounded in terms of mean and variance.[18]
  48. The CLT can be also extended to sample statistics beyond sums and means.[18]
  49. Since the CLT applies for any distribution of finite variance it would apply to the distribution of x2.[18]
  50. The above distributions suggests that for an extension of the central limit theorem to apply the sample statistic must be representable as a sum.[18]
  51. The Central Limit Theorem says that the sampling distribution looks more and more like a normal distribution as the sample size increases.[19]
  52. It is easy for beginners to get confused when trying to apply the Central Limit Theorem.[19]
  53. According to the central limit theorem, the distribution of the sample mean follows a normal distribution.[20]
  54. The central limit theorem applies to almost all types of probability distributions, but there are exceptions.[21]
  55. Additionally, the central limit theorem applies to independent, identically distributed variables.[21]
  56. And, the definition of the central limit theorem states that when you have a sufficiently large sample size, the sampling distribution starts to approximate a normal distribution.[21]
  57. Let’s get more specific about the normality features of the central limit theorem.[21]
  58. The central limit theorem forms the basis of the probability distribution.[22]
  59. Apart from showing the shape that the sample means will take, the central limit theorem also gives an overview of the mean and variance of the distribution.[22]
  60. The initial version of the central limit theorem was coined by Abraham De Moivre, a French-born mathematician.[22]
  61. Later in 1901, the central limit theorem was expanded by Aleksandr Lyapunov, a Russian mathematician.[22]
  62. The central limit theorem states that the sampling distribution of the mean approaches a normal distribution as N, the sample size, increases.[23]
  63. Firstly, the central limit theorem is impressive, especially as this will occur no matter the shape of the population distribution from which we are drawing samples.[24]
  64. The central limit theorem states that even if a population distribution is strongly non‐normal, its sampling distribution of means will be approximately normal for large sample sizes (over 30).[25]
  65. There are two basic ways to consider the central limit theorem.[26]
  66. A second way to consider the central limit theorem is often used to describe the physical phenomenon behind items that exhibit a normal distribution.[26]
  67. This video describes the central limit theorem and some properties of the normal distribution.[27]
  68. However, understanding the central limit theorem is not essential for a good understanding of statistics.[27]
  69. In other words, the central limit theorem is exactly what the shape of the distribution of means will be when we draw repeated samples from a given population.[28]
  70. Dice are ideal for illustrating the central limit theorem.[28]
  71. Lévy's Brownian motion as a set-indexed process and a related central limit theorem.[29]
  72. As the title of this lesson suggests, it is the Central Limit Theorem that will give us the answer.[30]
  73. The convergence of the PDF to a normal distribution depends on the applicability of the classical central limit theorem (CLT).[31]
  74. The most notable stable distribution is Gaussian and by the classical CLT we know that all distributionswith finite variance belong to the domain of attraction of the Gauss Law.[31]
  75. In all versions of the CLT mentioned so far, the assumption of finite variance was crucial.[31]
  76. ’s condition is satisfied, then the central limit theorem ( 8 ) holds.[31]
  77. We now turn to a q-generalized central limit theorem (q-CLT) formulated by Umarov et al.[32]
  78. Nothing can beat a central limit theorem.[32]

소스

  1. ↑ 1.0 1.1 1.2 1.3 The Central Limit Theorem and its misuse
  2. ↑ 2.0 2.1 What Is the Central Limit Theorem?
  3. ↑ 3.0 3.1 Central limit theorem (video)
  4. ↑ 4.0 4.1 4.2 What Is the Central Limit Theorem (CLT)?
  5. ↑ 5.0 5.1 5.2 5.3 What is A Central Limit Theorem
  6. ↑ 6.0 6.1 Central Limit Theorem: Definition and Examples in Easy Steps
  7. ↑ Central limit theorem | mathematics
  8. ↑ 8.0 8.1 Dice, Dragons and Getting Closer to Normal Distribution: The Central Limit Theorem
  9. ↑ 9.0 9.1 9.2 9.3 Central Limit Theorem
  10. ↑ 10.0 10.1 10.2 10.3 Central Limit Theorem - an overview
  11. ↑ Central Limit Theorem - an overview
  12. ↑ 12.0 12.1 12.2 12.3 Central limit theorem
  13. ↑ Central Limit Theorem -- from Wolfram MathWorld
  14. ↑ 14.0 14.1 14.2 Distribution of Data: The Central Limit Theorem
  15. ↑ Introduction to Quantitative Methods
  16. ↑ 16.0 16.1 16.2 16.3 Central Limit Theorem
  17. ↑ 17.0 17.1 17.2 17.3 The Central Limit Theorem
  18. ↑ 18.0 18.1 18.2 18.3 Extensions of the Central Limit Theorem
  19. ↑ 19.0 19.1 Sampling Distributions and the Central Limit Theorem
  20. ↑ From the Central Limit Theorem to the Z- and t-distributions
  21. ↑ 21.0 21.1 21.2 21.3 Central Limit Theorem Explained
  22. ↑ 22.0 22.1 22.2 22.3 Central Limit Theorem
  23. ↑ Central Limit Theorem
  24. ↑ A Gentle Introduction to the Central Limit Theorem for Machine Learning
  25. ↑ Central Limit Theorem
  26. ↑ 26.0 26.1 Central Limit Theorem
  27. ↑ 27.0 27.1 Central Limit Theorem and the Normal Distribution
  28. ↑ 28.0 28.1 Understanding the central limit theorem
  29. ↑ A central limit theorem for empirical processes
  30. ↑ Lesson 27: The Central Limit Theorem
  31. ↑ 31.0 31.1 31.2 31.3 The Role of the Central Limit Theorem in the Heterogeneous Ensemble of Brownian Particles Approach
  32. ↑ 32.0 32.1 Central limit theorems for correlated variables: some critical remarks

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