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  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

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