1. 54 Ounces
2. 21 Hours
3. Spent 180$ Saved 120$
4. 480 ounces
5.18 Hours
6. 176$
7. 126 cups
Hope This Helps!!!!
Answer:
The smallest sample size n that will guarantee at least a 90% chance of the sample mean income being within $500 of the population mean income is 48.
Step-by-step explanation:
The complete question is:
The mean salary of people living in a certain city is $37,500 with a standard deviation of $2,103. A sample of n people will be selected at random from those living in the city. Find the smallest sample size n that will guarantee at least a 90% chance of the sample mean income being within $500 of the population mean income. Round your answer up to the next largest whole number.
Solution:
The (1 - <em>α</em>)% confidence interval for population mean is:

The margin of error for this interval is:

The critical value of <em>z</em> for 90% confidence level is:
<em>z</em> = 1.645
Compute the required sample size as follows:

![n=[\frac{z_{\alpha/2}\cdot\sigma}{MOE}]^{2}\\\\=[\frac{1.645\times 2103}{500}]^{2}\\\\=47.8707620769\\\\\approx 48](https://tex.z-dn.net/?f=n%3D%5B%5Cfrac%7Bz_%7B%5Calpha%2F2%7D%5Ccdot%5Csigma%7D%7BMOE%7D%5D%5E%7B2%7D%5C%5C%5C%5C%3D%5B%5Cfrac%7B1.645%5Ctimes%202103%7D%7B500%7D%5D%5E%7B2%7D%5C%5C%5C%5C%3D47.8707620769%5C%5C%5C%5C%5Capprox%2048)
Thus, the smallest sample size n that will guarantee at least a 90% chance of the sample mean income being within $500 of the population mean income is 48.
Answer:
g(x) = -∛(x-7)
Step-by-step explanation:
Reflection in the y-axis changes the sign of x, so after this transformation, the function is ...
g(x) = f(-x)
Then, translation 7 units to the right replaces x by (x-7), so after both transformations, the function is ...
g(x) = f(-(x -7)) = f(-(x -7))
Using the given definition for f(x), we have ...
![g(x)=\sqrt[3]{-(x-7)}\\\\g(x)=-\sqrt[3]{x-7}](https://tex.z-dn.net/?f=g%28x%29%3D%5Csqrt%5B3%5D%7B-%28x-7%29%7D%5C%5C%5C%5Cg%28x%29%3D-%5Csqrt%5B3%5D%7Bx-7%7D)
Answer:
2nd one is right but first one is not mark 1st one right
-4x+8z is the combined like terms