In a tennis match, unforced errors are those types of mistakes that are supposedly not forced by good shots of an opponent. Reducing unforced errors is a key factor to win a tennis match. The following table shows the number of unforced errors of both players (winner and loser reported in pairs) in 10 tennis matches. Match: 1 2 3 4 6 7 8 9. 10 Winner: 68 19 28 28 53 28 50 30 44 22 Loser: 52 39 32 22 75 51 51 37 37 34 N(ux,o) be the average number of unforced errors of the winner in a tennis match, N(uy, o?) be the average number of unforced errors of the loser in a tennis match. Let X - and Y ~ Some R output that may help. > р1 <- с(0.01, 0.025, 0.05, 0.1, 0.9, 0.95, 0.975, 0.99) > qnorm (p1) [1] -2.326 -1.960 -1.645 -1.282 1.282 1.645 1.960 2.326 > qt (p1, 8) [1] -2.896 -2.306 -1.860 -1.397 1.397 1.860 2.306 2.896 > qt (p1, 9) [1] -2.821 -2.262 -1.833 -1.383 1.383 1.833 2.262 2.821 > qt (p1, 18) [1] -2.552 -2.101 -1.734 -1.330 1.330 1.734 2.101 2.552 > qt (p1, 19) [1] -2.539 -2.093 -1.729 -1.328 1.328 1.729 2.093 2.539 (a) Let pi unforced errors. Find a point estimate of pi and derive an approximate 95% confidence interval for nu. Pr(X < 30) be the proportion of the time that the winner has less than 30
In a tennis match, unforced errors are those types of mistakes that are supposedly not forced by good shots of an opponent. Reducing unforced errors is a key factor to win a tennis match. The following table shows the number of unforced errors of both players (winner and loser reported in pairs) in 10 tennis matches. Match: 1 2 3 4 6 7 8 9. 10 Winner: 68 19 28 28 53 28 50 30 44 22 Loser: 52 39 32 22 75 51 51 37 37 34 N(ux,o) be the average number of unforced errors of the winner in a tennis match, N(uy, o?) be the average number of unforced errors of the loser in a tennis match. Let X - and Y ~ Some R output that may help. > р1 <- с(0.01, 0.025, 0.05, 0.1, 0.9, 0.95, 0.975, 0.99) > qnorm (p1) [1] -2.326 -1.960 -1.645 -1.282 1.282 1.645 1.960 2.326 > qt (p1, 8) [1] -2.896 -2.306 -1.860 -1.397 1.397 1.860 2.306 2.896 > qt (p1, 9) [1] -2.821 -2.262 -1.833 -1.383 1.383 1.833 2.262 2.821 > qt (p1, 18) [1] -2.552 -2.101 -1.734 -1.330 1.330 1.734 2.101 2.552 > qt (p1, 19) [1] -2.539 -2.093 -1.729 -1.328 1.328 1.729 2.093 2.539 (a) Let pi unforced errors. Find a point estimate of pi and derive an approximate 95% confidence interval for nu. Pr(X < 30) be the proportion of the time that the winner has less than 30
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
![In a tennis match, unforced errors are those types of mistakes that are supposedly not forced by
good shots of an opponent. Reducing unforced errors is a key factor to win a tennis match. The
following table shows the number of unforced errors of both players (winner and loser reported
in pairs) in 10 tennis matches.
Match:
1
2
3
4
6
7
8
9.
10
Winner:
68
19
28
28
53
28
50
30
44
22
Loser:
52
39
32
22
75
51
51
37
37
34
N(ux,o) be the average number of unforced errors of the winner in a tennis match,
N(uy, o?) be the average number of unforced errors of the loser in a tennis match.
Let X -
and Y ~
Some R output that may help.
> р1 <- с(0.01, 0.025, 0.05, 0.1, 0.9, 0.95, 0.975, 0.99)
> qnorm (p1)
[1] -2.326 -1.960 -1.645 -1.282
1.282
1.645
1.960
2.326
> qt (p1, 8)
[1] -2.896 -2.306 -1.860 -1.397
1.397
1.860
2.306
2.896
> qt (p1, 9)
[1] -2.821 -2.262 -1.833 -1.383
1.383
1.833
2.262
2.821
> qt (p1, 18)
[1] -2.552 -2.101 -1.734 -1.330
1.330
1.734
2.101
2.552
> qt (p1, 19)
[1] -2.539 -2.093 -1.729 -1.328
1.328
1.729
2.093
2.539
(a) Let pi
unforced errors. Find a point estimate of pi and derive an approximate 95% confidence
interval for Pi.
Pr(X < 30) be the proportion of the time that the winner has less than 30](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F84d7412a-0252-4765-a3f4-d04a4d856ec4%2F446f334e-d0d8-49c8-b4e2-59dc75398432%2Fa91d0da_processed.png&w=3840&q=75)
Transcribed Image Text:In a tennis match, unforced errors are those types of mistakes that are supposedly not forced by
good shots of an opponent. Reducing unforced errors is a key factor to win a tennis match. The
following table shows the number of unforced errors of both players (winner and loser reported
in pairs) in 10 tennis matches.
Match:
1
2
3
4
6
7
8
9.
10
Winner:
68
19
28
28
53
28
50
30
44
22
Loser:
52
39
32
22
75
51
51
37
37
34
N(ux,o) be the average number of unforced errors of the winner in a tennis match,
N(uy, o?) be the average number of unforced errors of the loser in a tennis match.
Let X -
and Y ~
Some R output that may help.
> р1 <- с(0.01, 0.025, 0.05, 0.1, 0.9, 0.95, 0.975, 0.99)
> qnorm (p1)
[1] -2.326 -1.960 -1.645 -1.282
1.282
1.645
1.960
2.326
> qt (p1, 8)
[1] -2.896 -2.306 -1.860 -1.397
1.397
1.860
2.306
2.896
> qt (p1, 9)
[1] -2.821 -2.262 -1.833 -1.383
1.383
1.833
2.262
2.821
> qt (p1, 18)
[1] -2.552 -2.101 -1.734 -1.330
1.330
1.734
2.101
2.552
> qt (p1, 19)
[1] -2.539 -2.093 -1.729 -1.328
1.328
1.729
2.093
2.539
(a) Let pi
unforced errors. Find a point estimate of pi and derive an approximate 95% confidence
interval for Pi.
Pr(X < 30) be the proportion of the time that the winner has less than 30
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