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Mean of a Random Variable - Probability, Class 12, Math Video Lecture | Mathematics (Maths) Class 12 - JEE

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FAQs on Mean of a Random Variable - Probability, Class 12, Math Video Lecture - Mathematics (Maths) Class 12 - JEE

1. What is the meaning of a random variable in probability theory?
Ans. A random variable in probability theory is a variable that can take on different values based on the outcomes of a random event or experiment. It is often denoted by a capital letter, such as X, and represents a numerical measurement of the outcomes of the experiment.
2. How is the mean of a random variable calculated?
Ans. The mean of a random variable is calculated by multiplying each possible value of the random variable by its corresponding probability and then summing up these products. Mathematically, it can be represented as E(X) = ∑(x * P(X = x)), where x represents the possible values of the random variable and P(X = x) represents their respective probabilities.
3. Can the mean of a random variable be negative?
Ans. Yes, the mean of a random variable can be negative. The mean is a measure of the central tendency of a random variable and does not necessarily have to be positive. It can be negative if the values of the random variable and their corresponding probabilities result in a net negative value when calculated.
4. What is the significance of calculating the mean of a random variable?
Ans. Calculating the mean of a random variable is significant as it provides a measure of the average value or expected value of the random variable. It helps in understanding the central tendency of the variable and is useful in making predictions and making decisions based on probabilities.
5. How is the mean of a discrete random variable different from the mean of a continuous random variable?
Ans. The mean of a discrete random variable is calculated by summing the products of each possible value and its corresponding probability, as mentioned earlier. On the other hand, the mean of a continuous random variable is calculated by integrating the product of the variable's values and their respective probability density function (PDF). The concept and calculation method are similar, but the difference lies in the summation versus integration process.
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