Gaussian PDF: Probability of P(x), Usable Shafts %

In summary, the conversation discusses the determination of probabilities for a Gaussian PDF with specific parameters and the percentage of usable shafts in a manufacturing process with a certain normal distribution. The method of converting normal distribution to standard normal distribution and using tables to find probabilities is highlighted.
  • #1
axnman
15
0
Que. 1 Consider a Gaussian PDF with µ = 20, σ = 30, a = 50 and b =80. Determine i)Probability that P(x>b)
ii)P(x ≤ b)
iii)P(x ≤ - b)
iv)P(a ≤ x ≤ b)


Que. 2 In a certain manufacturing process only shafts whose diameters are less than 1.5 inches can be used. Given the shaft diameters are normally distributed with mean (µ) 1.490 inches and standard deviation (σ) 0.005 inches, determine the percentage of shafts that are usable.
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  • #2
We can't just give you the answers...you need to show some working
Do you know how to turn a normal distribution into a standard normal distribution?
That is always the key to determining probabilities

You have all the information you'll need once you scale "a" and "b" correctly using the mean and standard deviation given

If you don't know how to manipulate the figures then i'll give you some hints
 
  • #3
z = (X - µ ) / σ...am talking @ que. 2...This way z = (1.5-1.49) / 0.005 = 2...Then i get confused as i have to look into the tables...I get 0.0228...hmmm so well the answer should be 100 - 0.0228 % of shafts = 97.72%...Is that correct?

Q 1 am still trying...
 
  • #4
Your answer to q2 looks pretty reasonable

For q1, follow the same lines as you did for q2
i.e. find z, then look up phi(z) in tables. Draw a bell curve if you have to etc.
 

Related to Gaussian PDF: Probability of P(x), Usable Shafts %

1. What is a Gaussian PDF?

A Gaussian PDF, also known as a Normal distribution, is a probability distribution that is commonly used to model real-world data. It is characterized by its bell-shaped curve and is symmetrical around its mean value.

2. What does the probability of P(x) represent in a Gaussian PDF?

The probability of P(x) in a Gaussian PDF represents the likelihood of a random variable falling within a certain range of values. In other words, it tells us the probability of a specific event occurring.

3. How is the probability of P(x) calculated in a Gaussian PDF?

The probability of P(x) is calculated using a mathematical formula that takes into account the mean, standard deviation, and value of x. It is given by the equation P(x) = (1/σ√(2π)) * e^(-0.5((x-μ)/σ)^2), where μ is the mean, σ is the standard deviation, and e is the base of the natural logarithm.

4. What is the significance of "usable shafts %" in a Gaussian PDF?

"Usable shafts %" in a Gaussian PDF refers to the percentage of shafts that fall within a specified tolerance range. This is important in industries such as manufacturing, where products must meet certain specifications in order to be deemed usable.

5. How can a Gaussian PDF be used in practical applications?

A Gaussian PDF can be used in many practical applications, such as quality control in manufacturing, analyzing stock market trends, and predicting weather patterns. It is also commonly used in data analysis and statistical modeling to understand the distribution of data and make predictions based on that distribution.

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