Answer:
The sample consisting of 64 data values would give a greater precision.
Step-by-step explanation:
The width of a (1 - <em>α</em>)% confidence interval for population mean μ is:

So, from the formula of the width of the interval it is clear that the width is inversely proportion to the sample size (<em>n</em>).
That is, as the sample size increases the interval width would decrease and as the sample size decreases the interval width would increase.
Here it is provided that two different samples will be taken from the same population of test scores and a 95% confidence interval will be constructed for each sample to estimate the population mean.
The two sample sizes are:
<em>n</em>₁ = 25
<em>n</em>₂ = 64
The 95% confidence interval constructed using the sample of 64 values will have a smaller width than the the one constructed using the sample of 25 values.
Width for n = 25:
Width for n = 64:
![\text{Width}=2\cdot z_{\alpha/2}\cdot \frac{\sigma}{\sqrt{64}}=\frac{1}{8}\cdot [2\cdot z_{\alpha/2}\cdot \sigma]](https://tex.z-dn.net/?f=%5Ctext%7BWidth%7D%3D2%5Ccdot%20z_%7B%5Calpha%2F2%7D%5Ccdot%20%5Cfrac%7B%5Csigma%7D%7B%5Csqrt%7B64%7D%7D%3D%5Cfrac%7B1%7D%7B8%7D%5Ccdot%20%5B2%5Ccdot%20z_%7B%5Calpha%2F2%7D%5Ccdot%20%5Csigma%5D)
Thus, the sample consisting of 64 data values would give a greater precision
Answer:
choose all except " The outcome of events A and B are dependent on each other"
Step-by-step explanation:
I got it right on K.A! (:
Answer: See explanation
Step-by-step explanation:
You have to remeber that x to the power of r/q is the same thing as the qth root of x to the power of r.
For the second one, you would have the cubic root of 2 to the power of 2, or 4. So, your answer would be
.
For the third one, you would have the square root of 3 to the power of 3, or 27. So, your answer would be
.
For the fourth one, you would have the cubic root of 3 to the power of 1, or 3. So, your answer would be
.
Hope this helps! :)
Answer:
1543.59
Step-by-step explanation:
See image below for equation!
PLZZZ give me brainliest!