Convert 96.4% to a fraction in simplest form and a decimal.

Question#1<br><br>will give brainliest

Travka [436]

Step-by-step explanation:

y = 16x²

you put the x values one after the other in place of x and do the calculations.

x = -4

y = 16×(-4)² = 16×16 = 256

x = -3

y = 16×(-3)² = 16×9 = 144

x = -2.5

y = 16×(-2.5)² = 16×6.25 = 100

x = -2

y = 16×(-2)² = 16×4 = 64

x = -1.5

y = 16×(-1.5)² = 16×2.25 = 36

x = -1

y = 16×(-1)² = 16×1 = 16

x = -0.5

y = 16×(-0.5)² = 16×0.25 = 4

x = 0

y = 16×0² = 16×0 = 0

x = 0.5

y = 16×0.5² = 16×0.25 = 4

x = 1

y = 16×1² = 16×1 = 16

and so on.

36, 64, 100, 144, 256

as you can see, the y values are the same for the positive and the negative values.

because squaring a negative number is the same as squaring the same positive number. that is, because (if you remember) squaring something means to multiply that something with itself. and minus multiplied by minus is plus, as plus multiplied by plus is plus.

and that creates the symmetry around the y-axis or x = 0. everything left of the y-axis is mirrored on the right side of the y-axis (and vice versa).

5 0

1 year ago

The average amount of water in randomly selected 16-ounce bottles of water is 16.15 ounces with a standard deviation of 0.45 oun

vekshin1

Answer:

0.0179 = 1.79% probability that the mean of this sample is less than 15.99 ounces of water.

Step-by-step explanation:

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

Normal probability distribution

When the distribution is normal, we use 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 normally distributed random variable X, with mean $\mu$ and standard deviation $\sigma$ , the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean $\mu$ and standard deviation $s = \frac{\sigma}{\sqrt{n}}$ .