Answer:
The new image will be 3x bigger than the original image.
Answer:
Where
and ![\sigma=14](https://tex.z-dn.net/?f=%5Csigma%3D14)
Since the distribution for X is normal then the distribution for the sample mean is also normal and given by:
![\bar X \sim N(\mu, \frac{\sigma}{\sqrt{n}})](https://tex.z-dn.net/?f=%5Cbar%20X%20%5Csim%20N%28%5Cmu%2C%20%5Cfrac%7B%5Csigma%7D%7B%5Csqrt%7Bn%7D%7D%29)
![\mu_{\bar X} = 61](https://tex.z-dn.net/?f=%20%5Cmu_%7B%5Cbar%20X%7D%20%3D%2061)
![\sigma_{\bar X}= \frac{14}{\sqrt{6}}= 5.715](https://tex.z-dn.net/?f=%5Csigma_%7B%5Cbar%20X%7D%3D%20%5Cfrac%7B14%7D%7B%5Csqrt%7B6%7D%7D%3D%205.715)
So then is appropiate use the normal distribution to find the probabilities for ![\bar X](https://tex.z-dn.net/?f=%5Cbar%20X)
Step-by-step explanation:
Previous concepts
Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".
The Z-score is "a numerical measurement used in statistics of a value's relationship to the mean (average) of a group of values, measured in terms of standard deviations from the mean". The letter
is used to denote the cumulative area for a b quantile on the normal standard distribution, or in other words: ![\phi(b)=P(z](https://tex.z-dn.net/?f=%5Cphi%28b%29%3DP%28z%3Cb%29)
Solution to the problem
Let X the random variable that represent the variable of interest of a population, and for this case we know the distribution for X is given by:
Where
and ![\sigma=14](https://tex.z-dn.net/?f=%5Csigma%3D14)
Since the distribution for X is normal then the distribution for the sample mean
is also normal and given by:
![\bar X \sim N(\mu, \frac{\sigma}{\sqrt{n}})](https://tex.z-dn.net/?f=%5Cbar%20X%20%5Csim%20N%28%5Cmu%2C%20%5Cfrac%7B%5Csigma%7D%7B%5Csqrt%7Bn%7D%7D%29)
![\mu_{\bar X} = 61](https://tex.z-dn.net/?f=%20%5Cmu_%7B%5Cbar%20X%7D%20%3D%2061)
![\sigma_{\bar X}= \frac{14}{\sqrt{6}}= 5.715](https://tex.z-dn.net/?f=%5Csigma_%7B%5Cbar%20X%7D%3D%20%5Cfrac%7B14%7D%7B%5Csqrt%7B6%7D%7D%3D%205.715)
So then is appropiate use the normal distribution to find the probabilities for ![\bar X](https://tex.z-dn.net/?f=%5Cbar%20X)
A, perhaps,plz check the definition of functions
Step-by-step explanation:
Combing like terms means that you combine the terms that are alike. So, if you have integers you would combine the integers. If you had a variable like x, you would combine everything with x. For instance, 2x+4+3+7x, to combine like terms add 4+3 and 2x+7x.