Discrete Probability Distributions Example
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Questions and Answers

What is the key difference between continuous probability distributions and discrete probability distributions?

  • Continuous probability distributions only apply to measurements, while discrete probability distributions apply to counts.
  • Continuous probability distributions have an infinite number of values within a range, while discrete probability distributions have a countable number of possible values. (correct)
  • Continuous probability distributions always result in a symmetrical distribution, while discrete probability distributions may be skewed.
  • Continuous probability distributions can only be approximated, while discrete probability distributions are exact.

    • Which of the following represents an example of a continuous random variable?

  • The number of students in a classroom.
  • The weight of an individual. (correct)
  • The number of heads obtained when flipping a fair coin.
  • The color of a car in a traffic jam.

    • What defines a uniform probability distribution for a continuous random variable?

  • A bell-shaped curve with equal probabilities for all values.
  • A distribution that has no limits on the possible outcomes.
  • A distribution that is defined by minimum and maximum values with equal probabilities for each value within the range. (correct)
  • A distribution where extreme values are more likely than the mean values.

    • In a uniform probability distribution, what can we say about the likelihood of the extreme values compared to the middle values?

    <p>Extreme values have an equal likelihood to the middle values.</p> Signup and view all the answers

    Which variable is considered continuous in the context of the uniform distribution example provided?

    <p>The flight time in minutes from Orlando to Atlanta.</p> Signup and view all the answers

    How does a continuous random variable differ from a discrete random variable?

    <p>A continuous random variable can take on any value within a range, while a discrete random variable can only take distinct values.</p> Signup and view all the answers

    Why is it important to understand that continuous random variables have an infinite number of values within a given range?

    <p>To accurately calculate probabilities for specific ranges of values.</p> Signup and view all the answers

    Which feature characterizes a uniform probability distribution?

    <p>Equal likelihood for all potential outcomes</p> Signup and view all the answers

    Why does the concept of an infinite number of values matter in continuous probability distributions?

    <p>It enables accurate calculations for specific ranges of values.</p> Signup and view all the answers

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