Sampling distribution of sample mean

In summary, the problem involves independent normal random variables with a mean of 10 and standard deviation of 4. By taking the sum of these variables and dividing by 10, we get a new variable X with a mean of 10 and standard deviation of 1.6. By converting X to a standard normal distribution and using a z table, we can solve for the probability of X being less than 8, which is 0.
  • #1
Shackman
22
2

Homework Statement


Let X1,X2,...X10 be independent normal random variables with mean 10 and std dev 4. Let Y = X1+X2+...+X10. Let X be Y/10. What is P(X < 8)


Homework Equations


E(X) = 10 and Var(X) = 42 / 10 = 1.6
std dev of X = 1.6.5 = 1.26


The Attempt at a Solution



To convert to standard normal distribution to use z table to solve I get..

P(X-10/1.26 < (8-10)/1.26) = P(Z < -1.58) = 0

I really doubt that I have done this problem correctly because it is on a sample exam and seems too easy. Have I done anything incorrectly or am I just second guessing myself needlessly?
 
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  • #2
Shackman said:
Have I done anything incorrectly

Just the typo in the last line where you say the probability is 0.

am I just second guessing myself needlessly?

Yes!
 
  • #3
Ah. A silly mental error as I was using an applet to find the value for z that matched the probability but with a curve that had the mean and standard deviation of the original variable. Thanks for helping me again Billy Bob!
 

Related to Sampling distribution of sample mean

1. What is a sampling distribution of sample mean?

A sampling distribution of sample mean is a theoretical distribution that shows the possible values of the sample mean for all possible samples of a fixed size that can be drawn from a population. It helps us understand the variability of sample means and the likelihood of obtaining a particular sample mean.

2. Why is the sampling distribution of sample mean important?

The sampling distribution of sample mean is important because it allows us to make inferences about the population mean based on a sample. It also helps us assess the accuracy and reliability of our sample mean as an estimate of the population mean.

3. How is the sampling distribution of sample mean different from the distribution of individual scores?

The distribution of individual scores shows the variation of scores within a sample, while the sampling distribution of sample mean shows the variability of sample means across multiple samples. The distribution of individual scores is affected by sample size, while the sampling distribution of sample mean is not.

4. What is the central limit theorem and how does it relate to the sampling distribution of sample mean?

The central limit theorem states that the sampling distribution of sample mean will be approximately normally distributed, regardless of the shape of the population distribution, as long as the sample size is large enough (typically n ≥ 30). This is important because it allows us to use the normal distribution to make inferences about the population mean.

5. How can the standard error of the sample mean be calculated?

The standard error of the sample mean can be calculated by dividing the standard deviation of the population by the square root of the sample size. It is represented by the symbol σ/√n, where σ is the population standard deviation and n is the sample size.

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