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Serjik [45]
3 years ago
7

Cube root -125 over 216

Mathematics
1 answer:
Temka [501]3 years ago
3 0
The answer is 0.023.
Hope this helps
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Which inequality is true if p=3.4
Katyanochek1 [597]

Answer:

F.3p < 10.2

G.13.6 ≤ 3.9p

H.5p > 17.1

J.8.5

4 0
3 years ago
The perimeter of a certain soccer field is 310 m. The length is 65 m more than the width. What are the dimensions?
Dmitriy789 [7]

Answer:

50x105m

Step-by-step explanation:

310 (length of field) - 65-65= 200/4=50

Start with the perimeter subtract 65 twice because the length is on two sides.  Now the length and width will be equal so divide by 4.  Add 65 back because you subtracted it earlier.

3 0
3 years ago
Please help i have been on this for hours.
timurjin [86]

Answer:

2. 8

3. 6

4. 8

5. -8

Step-by-step explanation:

4 0
3 years ago
Read 2 more answers
The following information is used for questions 18, 19, 20, 21 and 22: You come up with what you think is a great idea for a new
Olenka [21]

Answer:

a) A Type II error happens when the null hypothesis failed to be rejected even  when the alternative hypothesis is true (<em>false negative</em>).

In this case the ad was effective (true alternative hypothesis), but the results of the sample had no enough statistical evidence to prove that the ad really had an effect increasing sales (reject the null hypothesis).

b) No. This type of errors are not evident, as the study is conduct to infere characteristics of the population. As it is an inference, there is not 100% accurate, and there is a probability of making this type of errors.

The only thing it can be done is limiting the probability of making this errors (type I and type II), affecting the power of the test (to affect Type II error) and the significance level (to affect Type I error). Obviously there is a trade-off, and minimizing one type of error increases the probability of making the other type.

c) The business consequences are that an effective ad campaign is not recognize and a business opportunity is lost. The ad would have been effective, but the study wasn't capable of demostrating its efectiveness.

d) One explanation could be a sample size not big enough. Increasing the sample size increases the power of the test, which decrease the probability of making a Type II error.

Other explanation could be a significance level that was too conservative (very low significance level). That means that the sample result was not considered a unlikely result becuase the threshold for unlikely results was set to a very low probability. This minimizes the probability of making a Type I error, but makes harder for true alternative hypothesis to be demonstrated.

Step-by-step explanation:

6 0
3 years ago
ܨ ܛܛܛܛ
aliina [53]

Answer:

it would be 910

Step-by-step explanation:

i just had a quiz with this on it

6 0
4 years ago
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