Standard discrete distributions<\/a> (Binomial, Poisson, Geometric) For CSIR NET<\/h2>\nStandard discrete distributions refer to a set of probability distributions that describe the probability of different values of a discrete random variable. A discrete random variable is a variable that can take on only distinct, countable values, often modeled using Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. In statistics, these distributions are used to model real-world phenomena where the outcome is countable, such as the number of successes in a fixed number of trials, which is a key concept in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>.<\/p>\nThere are several types of standard discrete distributions, including Binomial<\/strong>, Poisson<\/strong>, and Geometric distributions<\/strong>, all of which are part of Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. The Binomial distribution <\/em>models the number of successes in a fixed number of independent trials, where each trial has a constant probability of success. The Poisson distribution <\/em>models the number of events occurring in a fixed interval of time or space, where these events occur with a known constant mean rate. The Geometric distribution <\/em>models the number of trials until the first success, where each trial has a constant probability of success, all of which are crucial for Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>.<\/p>\nUnderstanding Standard discrete distributions (Binomial, Poisson, Geometric)<\/strong>is crucial for CSIR NET, as well as other competitive exams like IIT JAM and GATE, specifically through the lens of Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. These distributions are used to solve problems in probability and statistics, which are essential topics in various fields, including physics, engineering, and computer science, all of which rely on Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. Familiarity with these distributions helps students to analyze and solve problems related to random experiments, probability calculations, and statistical inference, particularly in the context of Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>.<\/p>\nThe following table summarizes the key characteristics of these distributions:<\/p>\n
\n\n\n| Distribution<\/th>\n | Parameters<\/th>\n | Description<\/th>\n<\/tr>\n |
\n| Binomial<\/td>\n | n, p<\/td>\n | Number of successes in n trials with probability p of success, a classic example of Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong><\/td>\n<\/tr>\n\n| Poisson<\/td>\n | \u03bb<\/td>\n | Number of events in a fixed interval with mean rate \u03bb, a key concept in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong><\/td>\n<\/tr>\n\n| Geometric<\/td>\n | p<\/td>\n | Number of trials until first success with probability p of success, part of Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nStandard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET: Key Features of Binomial Distribution<\/h2>\nThe binomial distribution is a discrete probability distribution that models the number of successes in a fixed number of independent trials, a fundamental concept in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. It is characterized by two parameters: number of trials<\/strong>(n<\/em>) and probability of success<\/strong>(p<\/em>). The probability of success remains constant for each trial, which is crucial for applying Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>.<\/p>\nThe binomial distribution gives the probability of number of successes<\/strong>(x<\/em>) in n <\/em>trials, where the probability of success in each trial isp<\/em>, directly related to Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. The probability mass function of the binomial distribution is given by P(X = x) = (n choose x)p^x<\/em>(1-p)^(n-x)<\/code>, where(n choose x)<\/code>represents the binomial coefficient, essential for Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>.<\/p>\nThe key features of the binomial distribution are:<\/p>\n \n- Number of trials (n<\/em>): a fixed positive integer, a concept in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong><\/li>\n
- Probability of success (p<\/em>): a constant probability between 0 and 1, crucial for Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong><\/li>\n
- Number of successes (x<\/em>): a random variable that takes values from 0 ton<\/em>, related to Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong><\/li>\n<\/ul>\n
Understanding these features is essential for applying the binomial distribution to problems in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>.<\/p>\nWorked Example: Binomial Distribution in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/h2>\nA fair coin is tossed 5 times. What is the probability of getting exactly 3 heads, a problem type found in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>?<\/p>\nThe binomial distribution <\/strong>is a discrete probability distribution that models the number of successes in a fixed number of independent trials, where each trial has a constant probability of success, a scenario often discussed in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. In this case, we have 5 trials (coin tosses), and each trial has a probability of success (getting a head) of 1\/2<\/code>, directly applicable to Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>.<\/p>\nThe probability mass function (PMF) of the binomial distribution is given by:<\/p>\n P(X = k) = (nCk) \\(p^k) \\<\/em>(q^(n-k))<\/code><\/p>\nwhere n <\/code>is the number of trials, k <\/code>is the number of successes, nCk <\/code>is the number of combinations of n <\/code>items taken k <\/code>at a time, p <\/code>is the probability of success, and q <\/code>is the probability of failure, all relevant to Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>.<\/p>\nFor this problem, n = 5<\/code>,k = 3<\/code>,p = 1\/2<\/code>, and q = 1\/2<\/code>. Plugging these values into the PMF, we get:<\/p>\nP(X = 3) = (5C3) \\((1\/2)^3) \\<\/em>((1\/2)^(5-3))<\/code>P(X = 3) = 10 \\(1\/8) \\<\/em>(1\/4) = 10\/32 = 5\/16<\/code><\/p>\nThe probability of getting exactly 3 heads in 5 coin tosses is 5\/16 <\/strong>or approximately0.3125<\/code>, an example solution in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. This example illustrates the application of the binomial distribution, one of the standard discrete distributions <\/em>useful in solving problems for CSIR NET <\/em>and other exams, specifically Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>.<\/p>\nCommon Misconceptions about Poisson Distribution in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/h2>\nStudents often hold misconceptions about the Poisson distribution, a discrete probability distribution that models the number of events occurring in a fixed interval of time or space, a topic covered in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. One common misconception is that the Poisson distribution is only applicable to rare events, a misunderstanding that can be clarified by studying Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. This understanding is incorrect because the Poisson distribution can be applied to events that are not necessarily rare, but occur independently and at a constant average rate, as discussed in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>.<\/p>\nAnother misconception is that the Poisson distribution is only suitable for continuous data, a mistake addressed in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. This is incorrect because the Poisson distribution is, in fact, a discrete distribution, which models count data, crucial for Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. It is characterized by a single parameter,\u03bb<\/em>(lambda), which represents the average rate of events occurring in a given interval, a key concept in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. The probability of observing k <\/code>events in a fixed interval is given by the Poisson probability mass function: P(k) = (e^(-\u03bb) \\* (\u03bb^k)) \/ k!<\/code>, essential for Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>.<\/p>\nTo clarify, the Poisson distribution is widely used in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET <\/strong>and other statistical analyses, particularly when dealing with count data that follows a Poisson process, directly related to Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. It is essential to understand the correct application and characteristics of the Poisson distribution to tackle problems in CSIR NET, IIT JAM, and GATE exams effectively, using Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET <\/strong>as a guide.<\/p>\nReal-World Application of Geometric Distribution in CSIR NET through Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/h2>\nThe geometric distribution, one of the standard discrete distributions<\/strong>, finds applications in various fields, including quality control and reliability engineering, areas where Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET <\/strong>is relevant. A real-world example is in the monitoring of manufacturing processes, where the geometric distribution models the number of trials (e.g., items produced) until a specific event occurs, such as the first defective item, a scenario that can be analyzed using Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>.<\/p>\nIn such scenarios, the geometric distribution helps in estimating the probability of achieving a certain number of successes before encountering a failure, a concept in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. This is crucial in quality control<\/em>, where it aids in setting up control charts to monitor production processes, directly related to Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. For instance, it can be used to determine the probability that a certain number of items will be produced before a defective one is found, an application of Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>.<\/p>\nThe geometric distribution’s significance in CSIR NET <\/strong>and other competitive exams like IIT JAM and GATE lies in its application to model real-world phenomena, specifically through Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. It tests the ability to apply statistical concepts to practical problems, using Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET <\/strong>as a foundation. Key points to focus on include understanding the distribution’s properties, such as its memoryless property<\/em>, and being able to calculate probabilities and expectations, all of which are part of Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>.<\/p>\n\n- Practice problems involving the calculation of probabilities, mean, and variance, related to Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>.<\/li>\n
- Understand the conditions under which the geometric distribution is applied, as outlined in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>.<\/li>\n
- Review standard discrete distributions<\/strong>, including binomial, Poisson, and geometric distributions, specifically Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>.<\/li>\n<\/ul>\n
Exam Strategy: Mastering Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/h2>\nMastering Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET <\/strong>is crucial for success in CSIR NET, IIT JAM, and GATE exams, directly related to Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. The topic encompasses Binomial<\/strong>, Poisson<\/strong>, and Geometric <\/strong>distributions, which are fundamental concepts in statistics and probability, all of which are covered in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. To approach this topic effectively, focus on key concepts and formulas, such as the probability mass function, mean, and variance of each distribution, using Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET <\/strong>as a guide.<\/p>\nA thorough understanding of these concepts can be achieved by practicing questions and problems, specifically those found in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. VedPrep <\/em>study materials offer a comprehensive collection of practice questions, mock tests, and expert guidance to help students reinforce their understanding of Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. By practicing with these resources, students can develop problem-solving skills and improve their ability to apply theoretical concepts to real-world problems, all within the context of Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>.<\/p>\n\n- Binomial Distribution<\/strong>: Focus on the conditions for its application, such as a fixed number of trials and constant probability of success, as discussed in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>.<\/li>\n
- Poisson Distribution<\/strong>: Understand its use in modeling rare events and the conditions for its application, outlined inStandard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>.<\/li>\n
- Geometric Distribution<\/strong>: Study its application in modeling the number of trials until the first success, a concept in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>.<\/li>\n<\/ul>\n
VedPrep provides expert guidance and study materials to help students master Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>, directly leading to success in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. By following a structured study plan and practicing with VedPrep’s<\/a> resources, students can gain confidence and excel in their exams, specifically in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>.<\/p>\nPractice Problems and Solutions for Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/h2>\nA random variable $X$ follows a binomial distribution with $n = 10$ and $p = 0.3$. Find $P(X = 3)$ and $P(X \\geq 2)$, examples of problems related to Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>.<\/p>\nThe probability mass function of a binomial distribution is given by $P(X = k) = \\binom{n}{k} p^k (1-p)^{n-k}$, a formula used in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. Here, $\\binom{n}{k}$ denotes the binomial coefficient, which is equal to $\\frac{n!}{k!(n-k)!}$, essential for solving problems in Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>.<\/p>\nFor $P(X = 3)$, we have $k = 3$, $n = 10$, and $p = 0.3$. Substituting these values, we get:<\/p>\n $P(X = 3) = \\binom{10}{3} (0.3)^3 (1-0.3)^{10-3}$
\n$P(X = 3) = 120 \\times 0.027 \\times (0.7)^7$
\n$P(X = 3) = 120 \\times 0.027 \\times 0.082354$
\n$P(X = 3) \\approx 0.2668$<\/code><\/p>\nFor $P(X \\geq 2)$, we can use the complement rule: $P(X \\geq 2) = 1 – P(X< 2) = 1 – [P(X = 0) + P(X = 1)]$, a strategy applicable to Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. Calculating these probabilities:<\/p>\n$P(X = 0) = (0.7)^{10} \\approx 0.028247$
\n$P(X = 1) = 10 \\times 0.3 \\times (0.7)^9 \\approx 0.121063$
\n$P(X< 2) = 0.028247 + 0.121063 \\approx 0.14931$
\n$P(X \\geq 2) = 1 - 0.14931 \\approx 0.85069$
\n<\/code><\/p>\nMastering Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET <\/strong>requires practice, specifically with problems like these, directly related to Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET<\/strong>. The key is to understand the properties and formulas of each distribution and apply them to different problems, using Standard discrete distributions (Binomial, Poisson, Geometric) For CSIR NET <\/strong>as a reference.<\/p>\n\nFrequently Asked Questions<\/h2>\nCore Understanding<\/h3>\n\n What are standard discrete distributions?<\/h4>\nStandard discrete distributions are probability distributions that describe the probability of different outcomes in a fixed number of independent trials. The three main standard discrete distributions are Binomial, Poisson, and Geometric distributions.<\/p>\n<\/div>\n \n What is a Binomial distribution?<\/h4>\nA Binomial distribution is a discrete probability distribution that models the number of successes in a fixed number of independent trials, each with a constant probability of success. It is characterized by parameters n (number of trials) and p (probability of success).<\/p>\n<\/div>\n \n What is a Poisson distribution?<\/h4>\nA Poisson distribution is a discrete probability distribution that models the number of events occurring in a fixed interval of time or space, where these events occur with a known constant mean rate and independently of the time since the last event. It is characterized by a single parameter \u03bb (lambda).<\/p>\n<\/div>\n \n What is a Geometric distribution?<\/h4>\nA Geometric distribution is a discrete probability distribution that models the number of trials until the first success, where each trial is independent and has a constant probability of success. It is characterized by a single parameter p (probability of success).<\/p>\n<\/div>\n \n What are the key differences between Binomial, Poisson, and Geometric distributions?<\/h4>\nThe key differences lie in their applications and characteristics: Binomial distribution models the number of successes in a fixed number of trials, Poisson distribution models the number of events in a fixed interval, and Geometric distribution models the number of trials until the first success.<\/p>\n<\/div>\n \n How are the mean and variance calculated for each distribution?<\/h4>\nFor Binomial distribution, mean = np and variance = np(1-p). For Poisson distribution, mean = \u03bb and variance = \u03bb. For Geometric distribution, mean = 1\/p and variance = (1-p)\/p^2.<\/p>\n<\/div>\n \n What are the conditions for using each distribution?<\/h4>\nBinomial distribution: fixed number of trials, constant probability of success. Poisson distribution: events occur independently, constant mean rate. Geometric distribution: trials are independent, constant probability of success.<\/p>\n<\/div>\n \n Can standard discrete distributions be used for continuous data?<\/h4>\nNo, standard discrete distributions are specifically designed for discrete data. For continuous data, continuous probability distributions like Normal or Uniform distributions are used.<\/p>\n<\/div>\n \n Are standard discrete distributions mutually exclusive?<\/h4>\nNo, they are not mutually exclusive. Data can sometimes be modeled by more than one distribution, but the choice depends on the specific characteristics of the data and the problem context.<\/p>\n<\/div>\n \n What are the limitations of standard discrete distributions?<\/h4>\nLimitations include their assumptions of independence, fixed probabilities, and specific conditions for each distribution, which might not always hold in real-world scenarios.<\/p>\n<\/div>\n Exam Application<\/h3>\n\n How are standard discrete distributions applied in CSIR NET?<\/h4>\nStandard discrete distributions are crucial in CSIR NET for solving problems related to probability and statistics, particularly in topics like hypothesis testing, confidence intervals, and probability theory.<\/p>\n<\/div>\n \n What types of questions can be expected in CSIR NET regarding standard discrete distributions?<\/h4>\nCSIR NET may include questions on identifying distributions, calculating probabilities, finding mean and variance, and applying these distributions to real-world scenarios.<\/p>\n<\/div>\n \n How to choose the correct distribution for a given problem?<\/h4>\nTo choose the correct distribution, carefully read the problem to identify key characteristics such as the type of data (discrete or continuous), the number of trials, the probability of success, and the interval of observation.<\/p>\n<\/div>\n \n How to solve problems involving standard discrete distributions in CSIR NET?<\/h4>\nTo solve problems, first identify the distribution, then apply the appropriate formulas for probability, mean, and variance, and finally use these to answer the question posed.<\/p>\n<\/div>\n Common Mistakes<\/h3>\n\n What are common mistakes when working with standard discrete distributions?<\/h4>\nCommon mistakes include misapplying the wrong distribution to a problem, incorrect calculation of mean and variance, and misunderstanding the conditions for using each distribution.<\/p>\n<\/div>\n \n How can one avoid confusion between Binomial and Poisson distributions?<\/h4>\nTo avoid confusion, clearly identify if the problem involves a fixed number of trials (Binomial) or a fixed interval with a constant mean rate of events (Poisson).<\/p>\n<\/div>\n \n What are common misconceptions about Geometric distribution?<\/h4>\nA common misconception is that the Geometric distribution models the number of successes, whereas it actually models the number of trials until the first success.<\/p>\n<\/div>\n \n How to ensure accuracy when calculating probabilities?<\/h4>\nEnsure accuracy by carefully substituting the given values into the correct formula for the identified distribution and performing calculations step by step.<\/p>\n<\/div>\n Advanced Concepts<\/h3>\n\n What are some advanced applications of standard discrete distributions?<\/h4>\nAdvanced applications include using these distributions in stochastic processes, queueing theory, and reliability engineering, among others.<\/p>\n<\/div>\n \n How do standard discrete distributions relate to other areas of statistics?<\/h4>\nStandard discrete distributions form the basis for more complex statistical models and techniques, such as Bayesian statistics, time series analysis, and machine learning algorithms.<\/p>\n<\/div>\n \n What is the relationship between Binomial and Poisson distributions?<\/h4>\nThe Poisson distribution can be seen as a limiting case of the Binomial distribution when the number of trials (n) is very large and the probability of success (p) is very small.<\/p>\n<\/div>\n \n Can standard discrete distributions be applied to non-random phenomena?<\/h4>\nStandard discrete distributions are primarily used for modeling random phenomena. Applying them to non-random phenomena would not be appropriate.<\/p>\n<\/div>\n<\/section>\n https:\/\/www.youtube.com\/watch?v=kJxoTZNoDgQ<\/p>\n","protected":false},"excerpt":{"rendered":" Standard discrete distributions (Binomial, Poisson, Geometric) are crucial for CSIR NET, IIT JAM, and GATE exams. With VedPrep, you can master these distributions and improve your chances of success. Our study materials and resources are designed to help you understand and apply these concepts effectively.<\/p>\n","protected":false},"author":10,"featured_media":11275,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","rank_math_seo_score":81},"categories":[29],"tags":[2923,19051,19052,19053,19050,6322,19049,2922],"class_list":["post-11276","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-csir-net","tag-competitive-exams","tag-geometric-for-csir-net","tag-geometric-for-csir-net-notes","tag-geometric-for-csir-net-questions","tag-poisson","tag-probability-distributions-for-csir-net","tag-standard-discrete-distributions-binomial","tag-vedprep","entry","has-media"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.vedprep.com\/exams\/wp-json\/wp\/v2\/posts\/11276","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.vedprep.com\/exams\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.vedprep.com\/exams\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.vedprep.com\/exams\/wp-json\/wp\/v2\/users\/10"}],"replies":[{"embeddable":true,"href":"https:\/\/www.vedprep.com\/exams\/wp-json\/wp\/v2\/comments?post=11276"}],"version-history":[{"count":3,"href":"https:\/\/www.vedprep.com\/exams\/wp-json\/wp\/v2\/posts\/11276\/revisions"}],"predecessor-version":[{"id":22736,"href":"https:\/\/www.vedprep.com\/exams\/wp-json\/wp\/v2\/posts\/11276\/revisions\/22736"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.vedprep.com\/exams\/wp-json\/wp\/v2\/media\/11275"}],"wp:attachment":[{"href":"https:\/\/www.vedprep.com\/exams\/wp-json\/wp\/v2\/media?parent=11276"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.vedprep.com\/exams\/wp-json\/wp\/v2\/categories?post=11276"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.vedprep.com\/exams\/wp-json\/wp\/v2\/tags?post=11276"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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