All categories

3 years ago

What is the critical F value when the sample size for the numerator is seven and the sample size for the denominator is six

Mathematics

1 answer:

PilotLPTM [1.2K]3 years ago

3 0

Answer:

Critical F value = 4.9503

Step-by-step explanation:

Given that:

The sample size of the numerator = 7

The sample size of the denominator = 6

The degree of freedom for the numerator df = n -1

The degree of freedom for the numerator df = 7 - 1

The degree of freedom for the numerator df = 6

The degree of freedom for the denominator df = n - 1

The degree of freedom for the denominator df = 6 - 1

The degree of freedom for the denominator df = 5

The assume that the test is two tailed and using a level of significance of ∝ = 0.10

The significance level for the two tailed test = 0.10/2 = 0.05

From the standard normal F table at the level of significance of 0.05

Critical F value = 4.9503

You might be interested in

The earned run average (ERA) a of a baseball pitcher varies directly as the number of earned runs r and inversely as the number

7nadin3 [17]

Answer:

The number of earned runs is 28.

Step-by-step explanation:

Earned runs average:

The earned runs average of a pitcher is the number of runs the he/she gives each 9 innings on average.

Find the number of earned runs when the pitcher has an ERA of 2.25 after pitching 112 innings in the next season.

Solved by proportions, using a rule of three.

For each 9 innits, the pitcher gives 2.25 runs. How many runs for 112 innings?

9 innings - 2.25 runs

112 innings - x runs

Applying cross multiplication:

$9x = 2.25*112$

$x = \frac{2.25*112}{9}$

$x = 28$

The number of earned runs is 28.

6 0

3 years ago

Juries should have the same racial distribution as the surrounding communities. According to the U.S. Census Bureau, 18% of resi

Ymorist [56]

Answer:

0.997 = 99.7% probability that the resulting sample proportion to be between 0.066 and 0.294

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

For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.

For a proportion p in a sample of size n, the sampling distribution of the sample proportion will be approximately normal with mean $\mu = p$ and standard deviation $s = \sqrt{\frac{p(1-p)}{n}}$

18% of residents in Minneapolis, Minnesota, are African Americans. Suppose a local court will randomly sample 100 state residents and will then observe the proportion in the sample who are African American.

This means that $p = 0.18, n = 100$