Answer:
The answer is 7.433
Step-by-step explanation:
Bc:
25.643 the 5 turned to 15, and the 2 is a 1.
- 18.210
7.433
Hope my answer has helped you, if not i'm sorry.
Answer:
93 pieces
Step-by-step explanation:
To cover his backyard with sod, he needs to cover the area of the backyard.
His backyard is a rectangle, and area of a rectangle is given as:
Where,
is the length, and
is the width
So, the area of Jim's backyard is 
square yards.
The area of each sods are solved using the area of the rectangle formula. So we have 
square yards as the area of each sod.
<em>To find </em><em>HOW MANY</em><em> of these sods we would need to cover the whole backyard, we divide the backyard area by each sod area:</em>
<em>
</em>
<u>Since, fractional sods can't be bought, Jim has to buy </u><u>93 sods</u><u> to cover his backyard.</u>
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:
Answer:
option a x²-25
Step-by-step explanation:
(x-5)²
x²-25
