This applet illustrates 95% confidence intervals for samples from a normal distribution with known variance.

Mathematics

1 answer:

deff fn [24]3 years ago

6 0

Answer:

1- b. False

2 b. We are 95% confident that the true mean is between 76.08 and 83.92.

Step-by-step explanation:

Confidence Interval is the estimated value that is computed from the statistic of data which is observed. The range value is plausible for unknown parameters. To find the critical value the confidence interval value is observed through the t-value table.

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Assume that the heights of men are normally distributed with a mean of "71.3" inches and a standard deviation of 2.1 inches. If

Elza [17]

Answer:

0.0021 = 0.21% probability that they have a mean height greater than 72.3 inches.

Step-by-step explanation:

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

Normal Probability Distribution:

Problems of normal distributions can be solved using the z-score formula.

In a set with mean $\mu$ and standard deviation $\sigma$ , the z-score 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 p-value, 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 $s = \frac{\sigma}{\sqrt{n}}$ .