Normal Distribution: Sample Mean & Variance

In summary, the conversation discusses using both moment generating function and cumulative function to prove that z=(x(bar)-\mu)/(\sigma/\sqrt{n}) if x(bar) is based on a random sample of size n from a normal(\mu,\sigma^2). The conversation also mentions the possibility of proving that a normalized normal is a normal (0,1) and clarifies that x(bar) refers to the sample mean. The speaker also mentions that they were able to prove the statement with help from the other person.
  • #1
axe69
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ok guys , i need an answer to this question , use both moment generating function and cummulative function to show that z=(x(bar)-[tex]\mu[/tex])/([tex]\sigma[/tex]/[tex]\sqrt{n}[/tex]) if x(bar) is based on a random sample of size n from a normal([tex]\mu[/tex],[tex]\sigma[/tex]^2)
 
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  • #2
Sorry this question is rather vague. Do you wish to prove that a normalized normal is a normal (0,1)? The x(bar) is deceiving, as it usually means sample mean, in the case you are taking a sample mean then z is not a distribution.
 
  • #3
mannaged to prove it ,and jst b4 the submition date ,thnx 4 you help
 

FAQ: Normal Distribution: Sample Mean & Variance

What is a normal distribution?

A normal distribution is a type of probability distribution that is used to describe the distribution of a set of data. It is a bell-shaped curve that is symmetrical around the mean value, with most of the data falling within one standard deviation of the mean.

What is the sample mean?

The sample mean is the average value of a set of data. It is calculated by adding up all the values in the data set and dividing by the number of values. In a normal distribution, the sample mean is also the same as the population mean.

What is the sample variance?

The sample variance is a measure of how spread out a set of data is. It is calculated by taking the average of the squared differences between each data point and the sample mean. In a normal distribution, the sample variance is also the same as the population variance.

How is the sample mean affected by outliers?

The sample mean can be greatly affected by outliers, which are data points that are significantly different from the rest of the data. In a normal distribution, outliers can pull the mean in their direction, making it an inaccurate representation of the data.

What is the significance of the standard deviation in a normal distribution?

The standard deviation is a measure of how much the data is spread out from the mean. In a normal distribution, approximately 68% of the data falls within one standard deviation of the mean, and approximately 95% falls within two standard deviations. It is an important measure for understanding the shape and spread of the data.

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