Answer:
points (2,3) and (-2,9)
Step-by-step explanation:
Answer:
4 over 5
Step-by-step explanation:
its 4 over 5 because your going to get a decimal and the other answers you will get a integer which the question isnt asking for so the answer is 4 over 5
Answer: the answer is 521
Step-by-step explanation: i don't know how i got that i am a human calculator :)
First, let's calculate the mean and the mean absolute deviation of the first bowler.
FIRST BOWLER: <span>8,5,5,6,8,7,4,7,6
Mean = (Sum of all data)/(Number of data points) = (8+5+5+6+8+7+4+7+6)/9
<em>Mean = 6.222</em>
Mean absolute deviation or MAD = [</span>∑(|Data Point - Mean|]/Number of Data Points
MAD = [|8 - 6.222| + |5 - 6.222| + |5 - 6.222| + |6 - 6.222| + |8 - 6.222| + |7 - 6.222| + |4 - 6.222| + |7 - 6.222| + |6 - 6.222|]/9
<em>MAD = 1.136</em>
SECOND BOWLER: <span>10,6,8,8,5,5,6,8,9
</span>Mean = (Sum of all data)/(Number of data points) = (<span>10+6+8+8+5+5+6+8+9</span>)/9
<em>Mean = 7.222</em>
Mean absolute deviation or MAD = [∑(|Data Point - Mean|]/Number of Data Points
MAD = [|10 - 7.222| + |6 - 7.222| + |8 - 7.222| + |8 - 7.222| + |5 - 7.222| + |5 - 7.222| + |6 - 7.222| + |8 - 7.222| + |9 - 7.222|]/9
<em>MAD = 1.531
</em>
The mean absolute deviation represents the average distance of each data to the mean. Thus, the lesser the value of the MAD is, the more consistent is the data to the mean. <em>B</em><em>etween the two, the first bowler is more consistent.</em>
Answer:
(a) The null hypothesis will be rejected.
(b) Type I error
(c) The null hypothesis will not be rejected. The error is type II error.
Step-by-step explanation:
The hypothesis provided is:

The p-value of the test obtained is 0.03
(a)
Decision rule for hypothesis testing, based on p-value, states that if the p-value is less than the significance level (α) then the null hypothesis is rejected and vice versa.
The significance level is α = 0.10
Then,

Thus, the null hypothesis will be rejected.
Conclusion:
The null hypothesis is rejected stating that the value of μ is more than 0.
(b)
If the decision in (a) is an error, i.e. the null hypothesis is rejected when in fact it is true, this type of error is known as type I error.
(c)
The significance level is α = 0.01
Then,

Thus, the null hypothesis will not be rejected.
Conclusion:
The null hypothesis was not rejected stating that the value of μ is 0.
If this decision is an error, i.e. the null hypothesis was not rejected when in fact it is false, this type of error is known as type II error.