Normal Distribution Porbability

In summary, the conversation discusses a normal distribution with mean ##\mu## and variance 9/25. It also mentions the standard deviation and an interval for the mean. The question is to find the probability of ##\bar{X}## being greater than ##\mu + 1.05## and it is solved by normalizing the distribution through Z-score transformation. This results in the events ##\{\bar{X} > \mu + 1.05\}## and ##\{Z > 1.05/0.6\}## being equivalent.
  • #1
Calu
73
0
I have ##\bar{X}## ~ ##N(\mu , 9/25)##

I have ##E[X] = \mu##
##Var[X] = 9/25##
##SD[X] = 3/5 = 0.6##

An interval for ##\bar{X}## has been recorded: ##\bar{X} \pm 1.05##.

I asked to find ##P(\bar{X} > \mu + 1.05)##

I can "normalize" the distribution through:

##Z = \frac{\bar{X} - \mu}{0.6}## ~ ##N(0,1)##

I'm confused by this next step:

##P(\bar{X} > \mu + 1.05) = P(Z > \frac{1.05}{0.6} = 1.75)##

I'm not sure how you go from the first probability to the other. Could any help please?
 
Physics news on Phys.org
  • #2
Calu said:
I have ##\bar{X}## ~ ##N(\mu , 9/25)##

I have ##E[X] = \mu##
##Var[X] = 9/25##
##SD[X] = 3/5 = 0.6##

An interval for ##\bar{X}## has been recorded: ##\bar{X} \pm 1.05##.

I asked to find ##P(\bar{X} > \mu + 1.05)##

I can "normalize" the distribution through:

##Z = \frac{\bar{X} - \mu}{0.6}## ~ ##N(0,1)##

I'm confused by this next step:

##P(\bar{X} > \mu + 1.05) = P(Z > \frac{1.05}{0.6} = 1.75)##

I'm not sure how you go from the first probability to the other. Could any help please?

The events ##\{\bar{X} > \mu + 1.05\}## and ##\{Z > 1.05/0.6\}## are the same:
[tex] \{ \bar{X} > \mu + 1.05\} = \{\bar{X} - \mu > 1.05 \}
= \{ (\bar{X} - \mu)/0.6 > 1.05/0.6 \}[/tex]
 
Last edited:
  • Like
Likes 1 person
  • #3
Ray Vickson said:
The events ##\{\bar{X} > \mu + 1.05\}## and ##\{Z > 1.05/0.6\}## are the same:
[tex] \{ \bar{X} > \mu + 1.05\} = \{\bar{X} - \mu > 0.15\}
= \{ (\bar{X} - \mu)/0.6 > 1.05/0.6 \}[/tex]

Oh I see, I was being a bit silly there, thanks!
 

FAQ: Normal Distribution Porbability

What is Normal Distribution Probability?

Normal distribution probability is a statistical concept that describes the likelihood of a continuous variable falling within a certain range of values. It is often used to describe natural phenomena and is represented by a bell-shaped curve.

How is the Normal Distribution Curve Calculated?

The normal distribution curve is calculated using a formula that takes into account the mean and standard deviation of the data. The curve is symmetrical around the mean, with most of the data falling within one standard deviation of the mean.

What is the Central Limit Theorem?

The central limit theorem states that the means of a large number of independent samples from any population will be approximately normally distributed. This is why the normal distribution is often used in statistical analysis, as it allows for the use of certain statistical tests.

How is Normal Distribution Probability Used in Real Life?

Normal distribution probability is used in a variety of fields, including finance, engineering, and social sciences. It can be used to model and predict outcomes, such as stock prices or test scores, and to determine the probability of certain events occurring within a given range.

What is the Difference between Standard Normal Distribution and Normal Distribution?

The standard normal distribution is a specific case of the normal distribution with a mean of 0 and a standard deviation of 1. Normal distribution, on the other hand, can have any mean and standard deviation. However, both types of distributions follow the same bell-shaped curve and have similar properties.

Similar threads

Replies
7
Views
5K
Replies
2
Views
1K
Replies
4
Views
2K
Replies
1
Views
1K
Replies
1
Views
1K
Replies
1
Views
974
Replies
25
Views
3K
Back
Top