확률 변수

수학노트
Pythagoras0 (토론 | 기여)님의 2021년 2월 17일 (수) 00:58 판
(차이) ← 이전 판 | 최신판 (차이) | 다음 판 → (차이)
둘러보기로 가기 검색하러 가기

노트

위키데이터

말뭉치

  1. A random variable is a variable whose value is unknown or a function that assigns values to each of an experiment's outcomes.[1]
  2. A random variable has a probability distribution that represents the likelihood that any of the possible values would occur.[1]
  3. Let’s say that the random variable, Z, is the number on the top face of a die when it is rolled once.[1]
  4. If random variable, Y, is the number of heads we get from tossing two coins, then Y could be 0, 1, or 2.[1]
  5. Since a random variable can take on different values, it is commonly labeled with a letter (e.g., variable “X”).[2]
  6. A discrete random variable is a (random) variable whose values take only a finite number of values.[2]
  7. Each outcome of a discrete random variable contains a certain probability.[2]
  8. When these are finite (e.g., the number of heads in a three-coin toss), the random variable is called discrete and the probabilities of the outcomes sum to 1.[3]
  9. A random variable that may assume only a finite...[3]
  10. The probability distribution for a random variable describes how the probabilities are distributed over the values of the random variable.[4]
  11. For a discrete random variable, x, the probability distribution is defined by a probability mass function, denoted by f(x).[4]
  12. This function provides the probability for each value of the random variable.[4]
  13. A continuous random variable may assume any value in an interval on the real number line or in a collection of intervals.[4]
  14. This graph shows how random variable is a function from all possible outcomes to real values.[5]
  15. As a function, a random variable is required to be measurable, which allows for probabilities to be assigned to sets of its potential values.[5]
  16. The domain of a random variable is called a sample space.[5]
  17. A random variable has a probability distribution, which specifies the probability of Borel subsets of its range.[5]
  18. The probability distribution of a discrete random variable is a list of probabilities associated with each of its possible values.[6]
  19. A continuous random variable is not defined at specific values.[6]
  20. Suppose a random variable X may take all values over an interval of real numbers.[6]
  21. In correspondence with general definition of a vector we shall call a vector random variable or a random vector any ordered set of scalar random variables.[7]
  22. A random variable is a statistical function that maps the outcomes of a random experiment to numerical values.[8]
  23. What I want to discuss a little bit in this video is the idea of a random variable.[9]
  24. This is actually a fairly typical way of defining a random variable, especially for a coin flip.[9]
  25. We can define another random variable capital Y as equal to, let's say, the sum of rolls of let's say 7 dice.[9]
  26. and we are defining a random variable in that way.[9]
  27. X Here X is a random variable: every time we select a new bead the outcome changes randomly.[10]
  28. We are going to define a random variable \(S\) that will represent the casino’s total winnings.[10]
  29. The probability distribution of a random variable tells us the probability of the observed value falling at any given interval.[10]
  30. a)\), then we will be able to answer any question related to the probability of events defined by our random variable \(S\), including the event \(S<0\).[10]
  31. These ideas are unified in the concept of a random variable which is a numerical summary of random outcomes.[11]
  32. The probability distribution of a discrete random variable is the list of all possible values of the variable and their probabilities which sum to \(1\).[11]
  33. The cumulative probability distribution function gives the probability that the random variable is less than or equal to a particular value.[11]
  34. The probability distribution of a discrete random variable is nothing but a list of all possible outcomes that can occur and their respective probabilities.[11]
  35. A discrete random variable may be defined for the random experiment of flipping a coin.[12]
  36. A random variable, Y, could be defined to be the number of times tails occurs in n trials.[12]
  37. It turns out that the probability mass function for this random variable is P Y ( k ) = ( n k ) ( 1 2 ) n , k = 0 , 1 , … , n .[12]
  38. The random variable Z will represent the number of times until the first occurrence of a heads.[12]
  39. A density curve describes the probability distribution of a continuous random variable, and the probability of a range of events is found by taking the area under the curve.[13]
  40. The mathematical function describing the possible values of a random variable and their associated probabilities is known as a probability distribution.[13]
  41. Their probability distribution is given by a probability mass function which directly maps each value of the random variable to a probability.[13]
  42. As a result, the random variable has an uncountable infinite number of possible values, all of which have probability 0, though ranges of such values can have nonzero probability.[13]
  43. One such example was the term "random quantity", introduced by the outstanding Russian mathematician Chebyshev.[14]
  44. Once we have a probability space, we can define a random variable on it.[14]
  45. if it is a tail"; the probability space (or experiment) itself does not tell us what random variable to use, though some may be more natural than others.[14]
  46. Note, therefore, that a random variable is neither random nor a variable: it is just any function we care to choose.[14]
  47. In essence, a random variable is a real-valued function that assigns a numerical value to each possible outcome of the random experiment.[15]
  48. We'll begin our exploration of the distributions of functions of random variables, by focusing on simple functions of one random variable.[16]
  49. Then, once we have that mastered, we'll learn how to modify the change-of-variable technique to find the probability of a random variable that is derived from a two-to-one function.[16]
  50. The equivalent quantity for a continuous random variable, not surprisingly, involves an integral rather than a sum.[17]
  51. Recall that mean is a measure of 'central location' of a random variable.[17]
  52. Guess the probability that the corresponding random variable lies between the limits of the shaded region.[17]
  53. The module Discrete probability distributions gives formulas for the mean and variance of a linear transformation of a discrete random variable.[17]
  54. We’ll first discuss the probability distribution of a discrete random variable, ways to display it, and how to use it in order to find probabilities of interest.[18]
  55. We’ll then move on to talk about the mean and standard deviation of a discrete random variable, which are measures of the center and spread of its distribution.[18]
  56. Recall our first example, when we introduced the idea of a random variable.[18]
  57. What is the probability distribution of X, where the random variable X is the number of tails appearing in two tosses of a fair coin?[18]
  58. A random variable—unlike a normal variable—does not have a specific value, but rather a range of values and a density that gives different probabilities of obtaining values for each subset.[19]
  59. The Wolfram Language uses symbolic distributions to represent a random variable.[19]
  60. A random variable is often introduced to students as a value that is created by some random process.[20]
  61. Give students roll dice, flip coins, or draw cards so you can get the idea of a random variable across.[20]
  62. However, you need to get students to see that the term “random variable” is used in both a more abstract way and a more varied way in most statistics textbooks.[20]
  63. This point value, call it X , is a random variable because its value is determined by the outcome of a random process.[20]
  64. A Random Variable in Slide2, is any model input parameter that you have selected and defined a statistical distribution for, using the options in the Statistics menu.[21]
  65. A Statistical Distribution must be chosen for each Random Variable in Slide2.[21]
  66. The larger the Standard Deviation, then the wider the range of values which the Random Variable may assume (within the limits of the Minimum and Maximum values).[21]
  67. Note that in the case of the shear strength random variable, coefficient of variation (COV) is entered instead of Standard Deviation.[21]
  68. Such a number varies from trial to trial of the corresponding experiment, and does so in a way that cannot be predicted with certainty; hence, it is called a random variable.[22]
  69. Random Variables A random variable is a number generated by a random experiment.[22]
  70. A random variable is called discrete if its possible values form a finite or countable set.[22]
  71. The probability distribution of a discrete random variable X is a list of each possible value of X together with the probability that X takes that value in one trial of the experiment.[22]
  72. We can define a random variable for a quantity of interest by assigning a numerical value to each event in a sample space .[23]
  73. In this experiment, we can define random variable X as the total number of tails.[23]
  74. X = x ) is usually used to represent the probability of a random variable, where the X is random variable and x is one of the values of random variable.[23]
  75. The realization that the concept of a random variable is a special case of the general concept of a measurable function came much later.[24]
  76. This made it clear that a random variable is nothing but a measurable function on a probability space.[24]
  77. Random variable refers to a variable whose value is not known or a function which obtains its values from the outcome of a random experiment.[25]
  78. The value of a random variable is not calculated like an algebraic variable.[25]
  79. In probability, a real-valued function, defined over the sample space of a random experiment, is called a random variable.[26]
  80. A random variable’s likely values may express the possible outcomes of an experiment, which is about to be performed or the possible outcomes of a preceding experiment whose existing value is unknown.[26]
  81. The domain of a random variable is a sample space, which is represented as the collection of possible outcomes of a random event.[26]
  82. A random variable is a rule that assigns a numerical value to each outcome in a sample space.[26]
  83. Let X be a discrete random variable and Y be a continuous random variable.[27]
  84. An exponentiated Weibull continuous random variable.[28]
  85. A folded Cauchy continuous random variable.[28]
  86. A Frechet left (or Weibull maximum) continuous random variable.[28]
  87. A generalized Pareto continuous random variable.[28]
  88. A random variable is a measurable mapping from the sample space asociated with a random experiment into the set of real numbers, \(X:S\mapsto{\mathbb R}\).[29]
  89. The support or range of a random variable \(X(S)\) is the set of all values that it can assume.[29]

소스

  1. ↑ 1.0 1.1 1.2 1.3 Random Variable
  2. ↑ 2.0 2.1 2.2 Definition, Types, and Role in Finance
  3. ↑ 3.0 3.1 Random variable | statistics
  4. ↑ 4.0 4.1 4.2 4.3 Statistics - Random variables and probability distributions
  5. ↑ 5.0 5.1 5.2 5.3 Random variable
  6. ↑ 6.0 6.1 6.2 Random Variables
  7. ↑ Random Variable ξ - an overview
  8. ↑ Alpha Examples: Random Variables
  9. ↑ 9.0 9.1 9.2 9.3 Random variables (video)
  10. ↑ 10.0 10.1 10.2 10.3 Introduction to Data Science
  11. ↑ 11.0 11.1 11.2 11.3 Introduction to Econometrics with R
  12. ↑ 12.0 12.1 12.2 12.3 Discrete Random Variable - an overview
  13. ↑ 13.0 13.1 13.2 13.3 Discrete Random Variables
  14. ↑ 14.0 14.1 14.2 14.3 What Is a Random Variable, Really?
  15. ↑ Random Experiments
  16. ↑ 16.0 16.1 Lesson 22: Functions of One Random Variable
  17. ↑ 17.0 17.1 17.2 17.3 Mean and variance of a continuous random variable
  18. ↑ 18.0 18.1 18.2 18.3 Discrete Random Variables
  19. ↑ 19.0 19.1 Random Variables—Wolfram Language Documentation
  20. ↑ 20.0 20.1 20.2 20.3 AP Statistics: Random Variables vs. Algebraic Variables
  21. ↑ 21.0 21.1 21.2 21.3 Random Variables
  22. ↑ 22.0 22.1 22.2 22.3 4: Discrete Random Variables
  23. ↑ 23.0 23.1 23.2 Random Variable
  24. ↑ 24.0 24.1 Encyclopedia of Mathematics
  25. ↑ 25.0 25.1 Definition, Latest News, and Why Random Variable is Important?
  26. ↑ 26.0 26.1 26.2 26.3 Definition, Types Formula and Example
  27. ↑ Independence test of a continuous random variable and a discrete random variable
  28. ↑ 28.0 28.1 28.2 28.3 Statistical functions (scipy.stats) — SciPy v1.5.4 Reference Guide
  29. ↑ 29.0 29.1 Chapter 2 Discrete random variables

메타데이터

위키데이터

Spacy 패턴 목록

  • [{'LOWER': 'random'}, {'LEMMA': 'variable'}]
  • [{'LOWER': 'random'}, {'LEMMA': 'quantity'}]
  • [{'LOWER': 'aleatory'}, {'LEMMA': 'variable'}]
  • [{'LOWER': 'stochastic'}, {'LEMMA': 'variable'}]
원본 주소 "https://wiki.mathnt.net/index.php?title=확률_변수&oldid=51179"