Calculating Probability with Normal Distribution Sample from N(μ=50, σ2=100)

This is the value we get when we plug in (50 + .6505(s)) for xbar in the Z equation, which is then used to find the probability using the t distribution with 15 degrees of freedom. In summary, the solution to this problem is given by P(T(15 d.f.) > 2.602), where T is the t distribution with 15 degrees of freedom and 2.602 is the value obtained by plugging (50 + .6505(s)) into the Z equation, representing the probability of Xbar being greater than 50 + .6505(s) in a sample of size 16 from a normal distribution with mean μ = 50 and variance σ2 = 100.
  • #1
Phox
37
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Homework Statement



Let X1, X2,...,X16 be a random sample of size 16 from N(μ=50, σ2=100) distribution.

Find P(Xbar > 50 + .6505(s))

Homework Equations



Z= (xbar - μ)/(σ/√n)

The Attempt at a Solution



So I know the solution of this problem is given by P( T(15 d.f.) > 2.602 ) but I'm not sure why.

I notice that you get the value 2.602 by plugging (50 + .6505(s)) in for xbar in the Z equation.

I guess I'm not really sure why we use the T distribution here and how you're supposed to come up with the value 2.602. 15 degrees of freedom makes sense.
 
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  • #2
Phox said:

Homework Statement



Let X1, X2,...,X16 be a random sample of size 16 from N(μ=50, σ2=100) distribution.

Find P(Xbar > 50 + .6505(s))

Homework Equations



Z= (xbar - μ)/(σ/√n)

The Attempt at a Solution



So I know the solution of this problem is given by P( T(15 d.f.) > 2.602 ) but I'm not sure why.

I notice that you get the value 2.602 by plugging (50 + .6505(s)) in for xbar in the Z equation.

I guess I'm not really sure why we use the T distribution here and how you're supposed to come up with the value 2.602. 15 degrees of freedom makes sense.

You have to use the Student's t distribution because your sample size is so small. As I recall the cutoff is about n = 28. For your problem, n = 16.
 
  • #3
Mark44 said:
You have to use the Student's t distribution because your sample size is so small. As I recall the cutoff is about n = 28. For your problem, n = 16.

Ok. That makes sense.

I suppose I'm confused because in the previous problem we were asked to find P(xbar >52) and we used z scores/normal cdf
 
  • #4
Phox said:

Homework Statement



Let X1, X2,...,X16 be a random sample of size 16 from N(μ=50, σ2=100) distribution.

Find P(Xbar > 50 + .6505(s))

Homework Equations



Z= (xbar - μ)/(σ/√n)

The Attempt at a Solution



So I know the solution of this problem is given by P( T(15 d.f.) > 2.602 ) but I'm not sure why.

I notice that you get the value 2.602 by plugging (50 + .6505(s)) in for xbar in the Z equation.

I guess I'm not really sure why we use the T distribution here and how you're supposed to come up with the value 2.602. 15 degrees of freedom makes sense.

What, exactly, do you mean by 's' in the expression Xbar > 50 + 0.6505 s ? If 's' is the sample standard deviation, you need to use the t-distribution because you are essentially asking for the distribution of T = (Xbar - μ)/s (or maybe (Xbar - μ)/(s/√n), depending on what s actually represents). You would use the normal distribution if you wanted Xbar > 50 + 0.6505 σ with known σ = 10. In other words, use N for (Xbar - μ)/σ and use T for (Xbar - μ)/s.
 
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  • #5
Ray Vickson said:
What, exactly, do you mean by 's' in the expression Xbar > 50 + 0.6505 s ? If 's' is the sample standard deviation, you need to use the t-distribution because you are essentially asking for the distribution of T = (Xbar - μ)/s (or maybe (Xbar - μ)/(s/√n), depending on what s actually represents). You would use the normal distribution if you wanted Xbar > 50 + 0.6505 σ with known σ = 10. In other words, use N for (Xbar - μ)/σ and use T for (Xbar - μ)/s.

's' represents the sample standard deviation.

I'm still a bit unclear as to how we come up with the value 2.602
 
  • #6
Phox said:
's' represents the sample standard deviation.

I'm still a bit unclear as to how we come up with the value 2.602

[tex] 2.602 = 4 \times 0.6505 = \sqrt{16} \times 0.6505 [/tex]
 

Related to Calculating Probability with Normal Distribution Sample from N(μ=50, σ2=100)

1. What is a normal distribution?

A normal distribution, also known as a Gaussian distribution, is a type of probability distribution that is commonly used to model continuous data. It is characterized by a bell-shaped curve and has a symmetrical shape, with the mean, median, and mode all being equal. Many natural phenomena tend to follow a normal distribution, making it an important concept in statistics.

2. How is a normal distribution different from other distributions?

A normal distribution is unique in that it is completely described by two parameters: the mean (μ) and the standard deviation (σ). This means that once these two values are known, the entire distribution can be determined. Other distributions may have more complex shapes and require more parameters to describe them.

3. What is the central limit theorem and how does it relate to normal distributions?

The central limit theorem states that the sum of a large number of independent random variables will tend towards a normal distribution, regardless of the underlying distribution of the individual variables. This is why the normal distribution is often referred to as the "bell curve," as it is a common result when many small random factors are combined together.

4. How is a normal distribution used in statistical analysis?

The normal distribution is widely used in statistical analysis because it allows for easy calculation of probabilities and confidence intervals. It is also used in hypothesis testing, as many statistical tests assume that the data follows a normal distribution. Additionally, normal distributions are often used as a baseline for comparing other distributions.

5. Can data that does not follow a normal distribution still be analyzed using normal distributions?

Yes, there are techniques and transformations that can be used to approximate non-normal data with a normal distribution. However, it is important to keep in mind that these methods may introduce some error and may not be appropriate for all types of data. It is always best to check the assumptions of the statistical test being used and consider alternative methods if the data does not meet the assumptions for a normal distribution.

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