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Kazeer [188]
3 years ago
10

HELP! Will give brainiest and 20 points! Please please help!

Mathematics
1 answer:
sasho [114]3 years ago
3 0
The mean is the average so it is 15.
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deff fn [24]

Answer:b

Step-by-step explanation:I took it

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3 years ago
Associations In Data:Question 10
Karo-lina-s [1.5K]

Answer:

\bar X = \frac{62+63+68+72+79+80+83+93+94+95}{10}= 78.9

|62-78.9| = 16.9

|63-78.9| = 15.9

|68-78.9| = 10.9

|72-78.9| = 6.9

|79-78.9| = 0.1

|80-78.9| = 1.1

|83-78.9| = 4.1

|93-78.9| = 14.1

|94-78.9| = 15.1

|95-78.9| = 16.1

MAD = \frac{\sum_{i=1}^n |X_i -\bar X|}{n}

And replacing we got:

MAD =\frac{16.9+15.9+10.9+6.9+0.1+1.1+4.1+14.1+15.1+16.1}{10}= 10.12

And the best anwer is

10.12

Step-by-step explanation:

We have the following data given:

62 63 68 72 79 80 83 93 94 95

And we need to begin finding the mean with the following formula:

\bar X = \frac{\sum_{i=1}^n X_i}{n}

And replacing we got:

\bar X = \frac{62+63+68+72+79+80+83+93+94+95}{10}= 78.9

Now we can find the mean absolute deviation like this:

|62-78.9| = 16.9

|63-78.9| = 15.9

|68-78.9| = 10.9

|72-78.9| = 6.9

|79-78.9| = 0.1

|80-78.9| = 1.1

|83-78.9| = 4.1

|93-78.9| = 14.1

|94-78.9| = 15.1

|95-78.9| = 16.1

And finally we can find the mean abslute deviation with the following formula:

MAD = \frac{\sum_{i=1}^n |X_i -\bar X|}{n}

And replacing we got:

MAD =\frac{16.9+15.9+10.9+6.9+0.1+1.1+4.1+14.1+15.1+16.1}{10}= 10.12

And the best anwer is

10.12

8 0
3 years ago
Please answer quickly
olchik [2.2K]

Answer:

a and c I think if I'm wrong I will try again

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The side length of aDavid is a statistician. He has a sample size of 40 (which he cannot change). What element of his hypothesis
Sergeeva-Olga [200]

Answer: D) the significance level of the test

=======================================================

Explanation:

The significance level of the test, also known as "alpha", is the probability of making a type 1 error. A type 1 error is where you reject the null hypothesis but it was true all along.

The null hypothesis is where we test a certain probability distribution (eg: normal distribution). Specifically we gather a sample of values and compute the test statistic. If the probability of getting that test statistic or more extreme is smaller than alpha, then we reject the null. This probability value is known as the p-value.

If you lower the alpha value, then that will make it more likely you do not reject the null. Consider an example where alpha = 0.10 to start with. If you get a p-value of 0.02, then you would reject the null. The same would apply for alpha = 0.05; however, with alpha = 0.01, the p-value is no longer smaller than alpha. At this point we do not reject the null. Your textbook may use the phrasing "fail to reject the null".

Going in the opposite direction, increasing the alpha value will make it more likely to reject the null. Each time you adjust the alpha value, keep the p-value to some fixed number (between 0 and 1).

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How is do does I don’t know how do does
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Huh? I’m not sure I understand what I just said...
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