Use the babylonian method to approximate square root of 24 to the nearest hundredth.

1 answer:

6 0

We will start with our guess of 12, since 12*2 = 24.
Divide 24 by 12; 24/12=2. Average this answer with our guess: (12+2)/2=7. This is our new guess.
24/7=3.428571429. Average this with our guess of 7: (3.428571429+7)/2=5.214285715. This is our new guess.
24/5.214285715=4.602739726. Averaging with our guess: (4.602739726+5.214285715)/2=4.90851272. New guess!
24/4.90851272=4.889464766. Averaging with our guess: (4.889464766+4.90851272)/2=4.898988743. New guess! You can see as we go through our guesses are closer and closer to the same number...)
24/4.898988743=4.898970228. Averaging: (4.898970228+4.898988743)/2=4.898979486. At this point our answer is the same every time down to the hundred-thousandth. Our estimate to the nearest hundredth would be 4.90.

You might be interested in

A population has a mean of 200 and a standard deviation of 50. Suppose a sample of size 100 is selected and x is used to estimat

zmey [24]

Answer:

a) 0.6426 = 64.26% probability that the sample mean will be within +/- 5 of the population mean.

b) 0.9544 = 95.44% probability that the sample mean will be within +/- 10 of the population mean.

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 $s = \frac{\sigma}{\sqrt{n}}$ .