Just multiply the given x values by the function rule, that’s how you get ur y
Answer:
c
Step-by-step explanation:
1 + tan²theta = sec²theta
tan²theta = 3² - 1
tan²theta = 8
tan theta = sqrt(8)
Positive because Quadrant 1
sqrt(8) = sqrt(4×2) = sqrt(4)×sqrt(2)
= 2×sqrt(2)
Answer:
![\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)
And replacing we got:
![\mu_{\bar X}= 2.2](https://tex.z-dn.net/?f=%5Cmu_%7B%5Cbar%20X%7D%3D%202.2)
![\sigma_{\bar X}= \frac{6}{\sqrt{100}}= 0.6](https://tex.z-dn.net/?f=%5Csigma_%7B%5Cbar%20X%7D%3D%20%5Cfrac%7B6%7D%7B%5Csqrt%7B100%7D%7D%3D%200.6)
Step-by-step explanation:
For this case we have the following info given:
represent the mean
represent the deviation
We select a sample size of n=100. This sample is >30 so then we can use the central limit theorem. And we want to find the distribution for the sample mean and we know that the distribution is 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)
And replacing we got:
![\mu_{\bar X}= 2.2](https://tex.z-dn.net/?f=%5Cmu_%7B%5Cbar%20X%7D%3D%202.2)
![\sigma_{\bar X}= \frac{6}{\sqrt{100}}= 0.6](https://tex.z-dn.net/?f=%5Csigma_%7B%5Cbar%20X%7D%3D%20%5Cfrac%7B6%7D%7B%5Csqrt%7B100%7D%7D%3D%200.6)