Normal distribution and probability

But yes, I integrated the PDF from negative infinity to -25, which gives the probability of the printer failing before 1000 hours of operation, which is 0%. In summary, the conversation discusses finding the percentage of printers that will be damaged before the end of 1000 hours of operation based on a normal distribution with a mean of 1500 and standard deviation of 20 hours. The solution involves finding the z-score and using it to calculate the probability using the normal distribution table or integrating the probability density function. The result is 0%.
  • #1
kliker
104
0

Homework Statement


The time until the first failure occurred in supplies ink to a particular printer brand, follows a normal distribution with μ=1500 and standard deviation(σ) 20 hours of operation. What percentage of these printers will be damaged before the end of 1000 hours of operation


Homework Equations



Z = (X - μ)/σ

The Attempt at a Solution



Ok first of all i found the z score, which is (1000-1500)/20 = -25

this gives me 0 percentage, actually i have the table in front of me and it gives values for minimum z = -3 or something

so, is there anything i can do to find the exact result?
 
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  • #2
Well, the value is exactly:

[tex] \frac{1}{\sqrt{2 \pi}}\int_{-\infty}^{-25} e^{-\frac{x^2}{2}} dx [/tex]

Using software I get:

>>> from scipy.stats import norm
>>> norm.cdf(-25)
3.056696706382561e-138
 
Last edited:
  • #3
where did you get this integral from?

it gives me ouput 0, which is the correct asnwer, I guess

edit:

ok i get it now, you integrated the probability density function

thanks
 
  • #4
Oh, I forgot the [itex] \frac{1}{\sqrt{2\pi}} [/itex]. Edited now.
 

1. What is a normal distribution?

A normal distribution is a type of probability distribution that is often used to describe data that clusters around a central value with symmetrical tails. It is often referred to as a bell-shaped curve.

2. What are the characteristics of a normal distribution?

A normal distribution is characterized by its mean, median, and mode being equal, and its shape being symmetrical. Additionally, the area under the curve is equal to 1, meaning that the probability of any outcome is always between 0 and 1.

3. How is the normal distribution used in statistics?

The normal distribution is used in statistics to model real-world data and make predictions. It is commonly used in hypothesis testing, confidence intervals, and in calculating probabilities for events.

4. Can any data be described by a normal distribution?

No, not all data can be described by a normal distribution. Certain criteria must be met for a dataset to be considered normally distributed, such as having a large sample size and being continuous in nature.

5. How is the normal distribution related to the central limit theorem?

The central limit theorem states that as the sample size increases, the distribution of sample means will approach a normal distribution regardless of the shape of the population distribution. This means that the normal distribution is often used as a model for the sampling distribution of a population mean.

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