Probability of Low Grade Gas Shipment from Two Plants

In summary, the conversation discusses a problem regarding a company that produces gas from two plants, A and B, with continuous random variables X and Y respectively. The probability density functions for both plants are given, and the octane rating of the gasoline determines whether it is low or high grade. The probability of today's shipment being low grade is calculated to be 0.3125, with equal chances of it coming from either plant. The probability of it coming from plant A, given that it is low grade, is 1/2. The conversation also mentions the need for integration to find the low grade probability.
  • #1
Disar
28
0
I've been working on a problem and was wondering if someone could check and see if I am on the right track.

A company produces gas from two plants, A and B. (both are considered to be continuous randm variables; X and Y respectively)

For Plant A, its probability density function is:

f(x) = 0.005(x-80) for 80<x<100
0 otherwise

For plant B, it's probability denisity function is:

f(y) = 0.02(y-80) for 80<y<90

r is the octane rating of the gasoline and the gas is:

low grade if r<85
High grade if r>=85

There is an equal probability that the gas was produced at plant A or plant B.

a. What is the probabilty that today's shipment is low grade?
b. If it is low grade what is the probabiliy that it came from plant A?

Here are my answers:

a. f(x) = .0615 (for r<85)
f(y) = .25 (r<85)
The probability that it is low grade is f(x) +f(y) = .3125

b. P(A|(r<85))

P(A) = 1/2
P(r<85) = .3125

P(A|r<85) = P(A and r<85)/P(r<85) = I don't know if this is the right set up for this portion of the problem

Thanks for the help!
 
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  • #2
To find low grade probability aren't you supposed to be integrating f(x) and f(y) over 0 < r < 85?
 
  • #3
I am sorry I did not note that I performed the necessary integration for f(x) and f(y) to determine their respective values.
 
  • #4
So you mistyped F(.) as f(.)? E.g. Fx(85) = [itex]\int_0^{85}f_x(x)dx[/itex] = .0615?
 

1. What is a continuous random variable?

A continuous random variable is a type of random variable that can take on any numerical value within a certain range. Unlike discrete random variables, which can only take on specific values, continuous random variables can take on any value within a range, making them ideal for modeling real-world phenomena such as time, distance, and temperature.

2. What is the difference between a continuous random variable and a discrete random variable?

The main difference between a continuous random variable and a discrete random variable is that a continuous random variable can take on any value within a certain range, while a discrete random variable can only take on specific, isolated values. Another difference is that continuous random variables are typically measured on a real number scale, while discrete random variables are often measured in categories or counts.

3. How do you calculate the probability of a continuous random variable?

The probability of a continuous random variable is calculated using a probability density function (PDF). This function represents the relative likelihood of the random variable taking on a particular value. The probability of a continuous random variable falling within a certain range can be found by integrating the PDF over that range. Alternatively, the cumulative distribution function (CDF) can be used to calculate the probability of a continuous random variable falling below a particular value.

4. What are some examples of continuous random variables?

Some examples of continuous random variables include height, weight, time, and temperature. Other examples include stock prices, wind speed, and blood pressure. Essentially, any variable that can take on any numerical value within a range can be considered a continuous random variable.

5. How are continuous random variables used in statistics and data analysis?

Continuous random variables are used in a variety of statistical and data analysis techniques. They are often used to model real-world phenomena and make predictions based on data. For example, the normal distribution, which is a type of continuous random variable, is commonly used to model natural processes such as human height and IQ scores. Continuous random variables are also used in regression analysis, hypothesis testing, and other statistical methods to analyze and interpret data.

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