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3-2 Random Variables
• In an experiment, a measurement is usually  
denoted by a variable such as X. 
• In a random experiment, a variable whose 
measured value can change (from one replicate of 
the experiment to another) is referred to as a 
random variable.
Page 2


3-2 Random Variables
• In an experiment, a measurement is usually  
denoted by a variable such as X. 
• In a random experiment, a variable whose 
measured value can change (from one replicate of 
the experiment to another) is referred to as a 
random variable.
3-2 Random Variables
Page 3


3-2 Random Variables
• In an experiment, a measurement is usually  
denoted by a variable such as X. 
• In a random experiment, a variable whose 
measured value can change (from one replicate of 
the experiment to another) is referred to as a 
random variable.
3-2 Random Variables 3-3 Probability
• Used to quantify likelihood or chance
• Used to represent risk or uncertainty in engineering
applications
•Can be interpreted as our degree of belief or 
relative frequency
Page 4


3-2 Random Variables
• In an experiment, a measurement is usually  
denoted by a variable such as X. 
• In a random experiment, a variable whose 
measured value can change (from one replicate of 
the experiment to another) is referred to as a 
random variable.
3-2 Random Variables 3-3 Probability
• Used to quantify likelihood or chance
• Used to represent risk or uncertainty in engineering
applications
•Can be interpreted as our degree of belief or 
relative frequency
3-3 Probability
• Probability statements describe the likelihood that 
particular values occur. 
• The likelihood is quantified by assigning a number 
from the interval [0, 1] to the set of values (or a 
percentage from 0 to 100%). 
• Higher numbers indicate that the set of values is 
more likely.
Page 5


3-2 Random Variables
• In an experiment, a measurement is usually  
denoted by a variable such as X. 
• In a random experiment, a variable whose 
measured value can change (from one replicate of 
the experiment to another) is referred to as a 
random variable.
3-2 Random Variables 3-3 Probability
• Used to quantify likelihood or chance
• Used to represent risk or uncertainty in engineering
applications
•Can be interpreted as our degree of belief or 
relative frequency
3-3 Probability
• Probability statements describe the likelihood that 
particular values occur. 
• The likelihood is quantified by assigning a number 
from the interval [0, 1] to the set of values (or a 
percentage from 0 to 100%). 
• Higher numbers indicate that the set of values is 
more likely.
3-3 Probability
• A probability is usually expressed in terms of a 
random variable. 
• For the part length example, X denotes the part 
length and the probability statement can be written 
in either of the following forms
• Both equations state that the probability that the 
random variable X assumes a value in [10.8, 11.2] is
0.25.
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FAQs on PPT: Random Variables - Engineering Mathematics - Civil Engineering (CE)

1. What is a random variable?
Ans. A random variable is a variable whose value is determined by the outcome of a random event. It can take on different values with certain probabilities, and it is often used to model uncertain or unpredictable phenomena in statistics and probability theory.
2. How is a discrete random variable different from a continuous random variable?
Ans. A discrete random variable can only take on a countable number of distinct values, while a continuous random variable can take on any value within a certain range. For example, the number of children in a family is a discrete random variable, while the height of a person is a continuous random variable.
3. What is the probability distribution of a random variable?
Ans. The probability distribution of a random variable describes the likelihood of each possible value that the random variable can take on. It assigns probabilities to each value, allowing us to understand the relative likelihood of different outcomes.
4. How can we calculate the expected value of a random variable?
Ans. The expected value of a random variable is a measure of its average value or center. For a discrete random variable, it can be calculated by multiplying each possible value by its corresponding probability and summing them up. For a continuous random variable, it is calculated by integrating the product of each value and its probability density function over its entire range.
5. What is the difference between the cumulative distribution function (CDF) and the probability density function (PDF) of a random variable?
Ans. The cumulative distribution function (CDF) of a random variable gives the probability that the random variable takes on a value less than or equal to a given value. It provides a cumulative view of the probabilities. On the other hand, the probability density function (PDF) represents the probability that the random variable takes on a specific value within a certain range. It gives the probability density at each point along the range.
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