What transformation of Figure 1 results in Figure 2.?<br><br> Thank you so your time c:

A square piece of paper 10 cm on a side is rolled to form the lateral surface area of a cylinder and then a top and bottom are a

Anika [276]

Answer:

The surface = pi*10cm2

Step-by-step explanation:

The area of any rectangle is Base * Height,

We know that the square originally has 10cm High, when is rolled to form a cylinder, it's new shape has a bottom side that is equals to a circle.

The length of a circle is 2*pi*Radio= pi*Diameter.

Since the square has the same length in each side, we know that pi*Diameter= 10cm*pi

The surface (area) of the cylinder is= Base * Height= 10cm*10cm*pi

6 0

3 years ago

The mean points obtained in an aptitude examination is 159 points with a standard deviation of 13 points. What is the probabilit

Korolek [52]

Answer:

0.4514 = 45.14% probability that the mean of the sample would differ from the population mean by less than 1 point if 60 exams are sampled

Step-by-step explanation:

To solve this question, we have to understand the normal probability distribution and the central limit theorem.

Normal probability distribution:

Problems of normally distributed samples are solved using the z-score formula.

In a set with mean $\mu$ and standard deviation $\sigma$ , the zscore of a measure X is given by:

$Z = \frac{X - \mu}{\sigma}$

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.

Central limit theorem:

The Central Limit Theorem estabilishes that, for a random variable X, with mean $\mu$ and standard deviation $\sigma$ , a large sample size can be approximated to a normal distribution with mean $\mu$ and standard deviation $s = \frac{\sigma}{\sqrt{n}}$