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            <Attribute name="title">Reliability of a test - Intro to Bayes theorem</Attribute>
            <Attribute name="description">This video provides an introduction to Bayes&#39; Theorem by examining the reliability of a medical diagnostic test. We&#39;ll explore a scenario where a test isn&#39;t 100% accurate and a disease is rare. Using a tree diagram, we&#39;ll calculate the probability that a person who tests positive for the disease actually has it. This example provides an intuitive understanding of why Bayes&#39; theorem is so important.</Attribute>
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            <video:title>Reliability of a test - Intro to Bayes theorem</video:title>
            <video:description>This video provides an introduction to Bayes&#39; Theorem by examining the reliability of a medical diagnostic test. We&#39;ll explore a scenario where a test isn&#39;t 100% accurate and a disease is rare. Using a tree diagram, we&#39;ll calculate the probability that a person who tests positive for the disease actually has it. This example provides an intuitive understanding of why Bayes&#39; theorem is so important.</video:description>
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            <Attribute name="description">In this video, we solve several classic probability problems involving drawing balls from different bags, which are perfect applications of Bayes&#39; theorem and the law of total probability. We&#39;ll tackle scenarios where a bag is chosen at random before a ball is drawn, and then use the outcome of the draw to determine the probability it came from a specific bag. We&#39;ll also look at a problem where the composition of the urn changes based on the first draw.</Attribute>
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            <video:description>In this video, we solve several classic probability problems involving drawing balls from different bags, which are perfect applications of Bayes&#39; theorem and the law of total probability. We&#39;ll tackle scenarios where a bag is chosen at random before a ball is drawn, and then use the outcome of the draw to determine the probability it came from a specific bag. We&#39;ll also look at a problem where the composition of the urn changes based on the first draw.</video:description>
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        <lastmod>2025-06-01T14:50:00.385158177Z</lastmod>
        
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            <Attribute name="description">This video demonstrates how to apply Bayes&#39; theorem to solve real-world problems related to manufacturing and quality control. We will work through scenarios where different machines or operators produce items at different rates and with different defect percentages. Given that a randomly selected item is defective, we will calculate the probability that it was produced by a specific machine or operator.</Attribute>
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            <video:description>This video demonstrates how to apply Bayes&#39; theorem to solve real-world problems related to manufacturing and quality control. We will work through scenarios where different machines or operators produce items at different rates and with different defect percentages. Given that a randomly selected item is defective, we will calculate the probability that it was produced by a specific machine or operator.</video:description>
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        <lastmod>2025-06-01T14:50:00.385158177Z</lastmod>
        
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            <Attribute name="description">In this video, we explore the classic conditional probability problem involving a person who doesn&#39;t always tell the truth. We&#39;ll analyze a scenario where a person tosses a coin and reports the outcome, given that they speak the truth with a certain probability (e.g., 4 out of 5 times). Using a tree diagram and Bayes&#39; theorem, we&#39;ll calculate the probability that the coin toss was actually heads, given that the person reported heads. A similar problem involving a die roll is also covered.</Attribute>
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            <video:title>Probability of telling the truth</video:title>
            <video:description>In this video, we explore the classic conditional probability problem involving a person who doesn&#39;t always tell the truth. We&#39;ll analyze a scenario where a person tosses a coin and reports the outcome, given that they speak the truth with a certain probability (e.g., 4 out of 5 times). Using a tree diagram and Bayes&#39; theorem, we&#39;ll calculate the probability that the coin toss was actually heads, given that the person reported heads. A similar problem involving a die roll is also covered.</video:description>
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        <xhtml:link rel="alternate" hreflang="en"
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            <Attribute name="description">In this video, we explore how to apply the binomial theorem to solve probability problems. We&#39;ll work through an example where we need to find the probability that at most 6 people in a random sample of 10 are right-handed, given that 90% of the population is right-handed. We&#39;ll define the parameters for a binomial distribution (n, p, q) and use the complement event (finding the probability of 7 or more right-handed people) to simplify the calculation and arrive at the final answer.</Attribute>
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            <video:description>In this video, we explore how to apply the binomial theorem to solve probability problems. We&#39;ll work through an example where we need to find the probability that at most 6 people in a random sample of 10 are right-handed, given that 90% of the population is right-handed. We&#39;ll define the parameters for a binomial distribution (n, p, q) and use the complement event (finding the probability of 7 or more right-handed people) to simplify the calculation and arrive at the final answer.</video:description>
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        <lastmod>2025-05-05T08:14:25.350149109Z</lastmod>
        
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