Gaussian Distribution Question.

In summary, the probability that a single measurement lies in the range of the following..a.) 385.0-385.1b.) 400.0-400.1c.) 451.0-415.1d.) 370.0-400.0e.) 355.0-415.0f.) 340.0-430.0 is 0.399.
  • #1
Xyius
508
4
I am studying the Gaussian distribution and am doing one of the problems for practice. The problem states that the standard deviation is equal to 15 and the actual value recorded in the experiment is 385.0. It then asks what is the probability that a single measurement lies in the range of the following..
a.) 385.0-385.1
b.) 400.0-400.1
c.) 451.0-415.1
d.) 370.0-400.0
e.) 355.0-415.0
f.) 340.0-430.0

The following equations are to be used, based on the Gaussian distribution.
[PLAIN]http://img62.imageshack.us/img62/8594/what2c.gif
I couldn't get the same answer as the book. This is the books work for a.)
[PLAIN]http://img585.imageshack.us/img585/1271/whati.gif

There is no explanation as to where 0.399 came from, and the section on Gaussian distribution is very short (As this isn't a statistics course). It says for problems d e and f use the integral equation. Why can't I use the integral equation for all of them? Can anyone help?
 
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  • #2
Xyius said:
the actual value recorded in the experiment is 385.0.
The problem would make sense if it said that 385.0 was the actual population mean.


There is no explanation as to where 0.399 came from

Taking [itex] z= \frac{(385.1 - 385.0)}{15} [/itex] and substituting this in the expression [itex] \frac{1}{\sqrt{2 \pi} } e^{\frac{-z^2}{2}} [/itex] is probably where it came from. Or they might have used 385.05 as being more representative of the interval from 385 to 385.1 than the endpoint 385.1.

and the section on Gaussian distribution is very short (As this isn't a statistics course). It says for problems d e and f use the integral equation. Why can't I use the integral equation for all of them

I think you could use the integral expression for all of them if you have numerical table of that integral.
 
  • #3
Stephen Tashi said:
Taking [itex] z= \frac{(385.1 - 385.0)}{15} [/itex] and substituting this in the expression [itex] \frac{1}{\sqrt{2 \pi} } e^{\frac{-z^2}{2}} [/itex] is probably where it came from. Or they might have used 385.05 as being more representative of the interval from 385 to 385.1 than the endpoint 385.1.

Yup that was it! But what confuses me is, wouldn't that mean that a and b have the same answer? Since the differences are all 0.1? The book as the the answer to b as, 0.0161.
 
  • #4
The z for a value v is calculated by (v - 385.0)/15. It just happens that in a) the interval begins at 385. In the other examples, the intervals don't begin there.
 
  • #5
Ohh! Thank you very much! I understand now :]
 

1. What is a Gaussian distribution?

A Gaussian distribution, also known as a normal distribution, is a type of probability distribution that is often used to describe and analyze data that falls into a bell-shaped curve. It is characterized by its mean, or average, and standard deviation, which determines the spread of the data.

2. What is the difference between a Gaussian distribution and a normal distribution?

There is no difference between a Gaussian distribution and a normal distribution. They are two names for the same type of probability distribution.

3. How is a Gaussian distribution different from other types of distributions?

A Gaussian distribution is different from other types of distributions because it has a symmetric, bell-shaped curve and its measurements of central tendency (mean, median, and mode) are all equal. This makes it useful for analyzing data that is continuous and normally distributed.

4. How is a Gaussian distribution used in science?

In science, a Gaussian distribution is often used to describe and analyze data in many fields, including physics, biology, psychology, and economics. It is also used in statistical analysis and modeling to make predictions and draw conclusions about a population based on a sample of data.

5. What are some real-world examples of data that follows a Gaussian distribution?

Some real-world examples of data that follow a Gaussian distribution include people's heights and weights, IQ scores, and blood pressure measurements. It is also commonly seen in natural phenomena such as the distribution of weights of seeds on a plant or the distribution of particles in a gas.

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