Uniform distribution and standard deviation

In summary, the conversation discusses the calculation of standard deviations from the mean and the use of a linear transformation for rescaling to a standard distribution. The conversation also includes the integration of a function and the addition of two means, resulting in a simplified expression. Finally, the conversation concludes with the understanding that fixing the algebra will result in the correct answer.
  • #1
r0bHadz
194
17
Homework Statement
4.19:
According to one of the Western Electric rules for quality control, a produced item is considered conforming if its measurement falls within three standard deviations from the target value. Suppose that the process is in control so that the expected value of each measurement equals the target value. What percent of items will be considered conforming, if the distribution of measurements is: Uniform(a,b)?

4.20:
Refer to exercise 4.19, what percent of items falls beyond 1.5 standard deviations from the mean, if the distribution of measurements is Uniform(a,b)?
Relevant Equations
density f(x) = 1/(b-a), a<x<b
μ = (a+b)/2
σ = (b-a)/sqrt(12)
+(3/2) standard deviations from the mean = [itex] \frac {a+b}{12} + \frac{\sqrt3}{4} (b-a) [/itex]
-(3/2) standard deviations from the mean = [itex] \frac {a+b}{12} - \frac{\sqrt3}{4} (b-a) [/itex]

[itex]\frac {1}{b-a} \int_a^{\frac {a+b}{12} - \frac{\sqrt3}{4} (b-a)} dx [/itex] = m_1= [itex] \frac {(-11+3\sqrt3)a + (1-3\sqrt3)b}{12(b-a)}[/itex]

[itex]\frac {1}{b-a} \int_{\frac {a+b}{12} + \frac{\sqrt3}{4} (b-a)}^{b} dx [/itex] = m_2 = [itex] \frac {(11-3\sqrt3)b + (-1+3\sqrt3)a}{12(b-a)}[/itex]

adding m_1+m_2 I get:

[itex] \frac{(-12+6\sqrt3)a + (12-6\sqrt3)b}{12(b-a)}[/itex]

I got up to this point where I quit the problem.

Now if I let b = 1 and a = 0 I get the answer to my problem which is .13397

I understand that this is the standard uniform distribution. But I don't understand what part of the problem statement let's me assign these values to a and b. Nothing in the problem statement tells me that this is not a general distribution, so why does it take values a = 0 and b = 1 to make it standard?
 
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  • #2
You can always rescale to a standard distribution using a linear transformation.
 
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  • #3
r0bHadz said:
+(3/2) standard deviations from the mean = [itex] \frac {a+b}{12} + \frac{\sqrt3}{4} (b-a) [/itex]
-(3/2) standard deviations from the mean = [itex] \frac {a+b}{12} - \frac{\sqrt3}{4} (b-a) [/itex]

You shouldn't have ##12## in those two means above, or below.

[itex]\frac {1}{b-a} \int_a^{\frac {a+b}{12} - \frac{\sqrt3}{4} (b-a)} dx [/itex] = m_1= [itex] \frac {(-11+3\sqrt3)a + (1-3\sqrt3)b}{12(b-a)}[/itex]

[itex]\frac {1}{b-a} \int_{\frac {a+b}{12} + \frac{\sqrt3}{4} (b-a)}^{b} dx [/itex] = m_2 = [itex] \frac {(11-3\sqrt3)b + (-1+3\sqrt3)a}{12(b-a)}[/itex]

adding m_1+m_2 I get:

[itex] \frac{(-12+6\sqrt3)a + (12-6\sqrt3)b}{12(b-a)}[/itex]

Factor a minus out of the first term and simplify it.

I got up to this point where I quit the problem.

Now if I let b = 1 and a = 0 I get the answer to my problem which is .13397
Fix the algebra and you will get the same answer.
 
  • #4
LCKurtz said:
Fix the algebra and you will get the same answer.

ended up fixing it, and got the same answer >.> damn I hate algebra
 

1. What is a uniform distribution?

A uniform distribution is a probability distribution where all possible outcomes have an equal chance of occurring. This means that the data is evenly spread out and there is no skewness or bias towards any particular value.

2. How is uniform distribution different from normal distribution?

Uniform distribution and normal distribution are two types of probability distributions. The main difference between them is that in a uniform distribution, all outcomes have an equal probability, whereas in a normal distribution, the majority of the data is clustered around the mean with fewer values in the tails.

3. What is the formula for calculating standard deviation?

The formula for calculating standard deviation is:

σ = √(Σ(x-μ)^2 / N)

where σ is the standard deviation, x is each data point, μ is the mean, and N is the total number of data points.

4. How is standard deviation related to uniform distribution?

In a uniform distribution, the standard deviation is a measure of how spread out the data is. A smaller standard deviation indicates that the data is tightly clustered around the mean, while a larger standard deviation indicates that the data is more spread out.

5. Can standard deviation be negative?

No, standard deviation cannot be negative. It is always a positive value or zero. A negative standard deviation would indicate that the data points are further away from the mean than the mean itself, which is not possible.

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