4) Baseball Data: a) Using the teams as psus (N = with equal 30), draw a one-stage cluster sample of 6 teams (n = 6) probabilities. Your sample should have approximately 150 players altogether. b) Use your sample to estimate the mean of logsal = log(salary) along with its SE. Hint: Create a new dataset with an additional column for logsal. If samo is your sample, then saml = data.frame (sam0, logsal=log (sam0$ salary)) Here is the data A B C D E F G H J K L M N о P Q R S T U V W X Y AA AB AC AD AE AF team leagueID player salary POS G GS InnOuts PO A E DP PB GB AB R H SecB ThiB HR RBI SB CS BB SO IBB HBP SH SF GIDP pitcher 2 ANA AL anderga0 6200000 CF 112 3 ANA AL colonba0 1.1E+07 P 4 ANA AL davanjeO 375000 CF 108 5 ANA AL donnebro 375000 P 6 ANA AL eckstda0 7 ANA AL erstada0 8 ANA AL 2150000 SS 7750000 1B escobke0 5750000 P 142 125 9 ANA AL figgicho 320000 3B 148 10 ANA AL glaustro 9900000 3B 11 ANA AL greggke0 301500 P 12 ANA AL guerrvl0 1.1E+07 RF 156 13 ANA 14 ANA AL AL guilljoo 2200000 LF 148 haltesho 575000 3B 15 ANA AL 16 ANA AL kenneado 2500000 2B lackejo0 375000 P 144 17 ANA AL molinbe0 2025000 C 18 ANA AL molinjo0 335000 C 19 ANA AL ortizra0 20 ANA AL pauljo01 3266667 P 335000 C 21 ANA AL percitro 7833333 P 22 ANA AL rodrifro 375000 P 23 ANA AL salmotio 24 ANA AL seleaa01 9900000 RF 8666667 P 25 ANA AL shielsc0 375000 P 26 ANA AL washbja0 5450000 P 27 ANA AL weberbe0 900000 P 28 ARI NL alomaro0 1000000 2B 29 ARI NL baergca0 1000000 1B 30 ARI 31 ARI NL bautidao 4000000 RF NL choatra0 325750 P 32 ARI NL cintralo 335000 SS 33 ARI NL colbrgro 2750000 1B 34 ARI NL daiglca0 300000 P 35 ARI NL desseel0 4000000 P 36 ARI NL ADI estalbo0 baseball 550000 C ༄^8༞¥8ཨྠཧྨ^ 8ཨྠ¥ཝཱ"b " " ྴ "ng - nn༅%ę¥818E 8" 3 5 136 124 1 58 5 143 135 46 2 97 1 46 3 3 60 3 3 38 79 141 69 154 20 11 36 8#h°⌘ཟླ⌘⌘9°⌘d⌘ལྐ⌘h!༠༠༠༠ཟླ°༢°ཀླསྒྱུg °⌘--༠ 92 2375 211 34 625 27 743 0 126 3575 3196 986 33 625 80 2116 19 495 0 263 3702 3471 22 640 138 3675 32 595 2286 57 1573 14 384 16 504 0 149 0 252 5 117 24 396 0 316 25 448 0 67 23 610 4 135 107 3536 0 152 ་ྒུ༩མྦྷཝ༔༤༦=ས ༔ ༔ 8 ཧྨ ཎྷེ ་ྒུ ཙྩུ ཎྜ ༞ ཤྩ ༠ ༠ ąསྙ༠ ཉྩཱ 5 2 1 NA 112 442 57 133 20 1 14 75 2 1 29 75 8 30 3 4 NA 3 3 0 0 0 0 0 0 0 0 0 75 1 0 1 NA 108 285 41 79 11 4 7 34 18 3 46 2 2 0 Ο ΝΑ 5 0 0 0 0 0 0 0 0 0 0 198 309 6 75 NA 142 566 92 156 24 1 2 35 16 5 42 49 66 4 83 NA 125 495 79 146 29 1 7 69 16 1 37 16 24 0 1 NA 1 2 0 0 0 0 0 0 0 0 0 57 129 11 9 NA 148 577 83 171 22 17 5 60 34 13 49 11 27 2 2 NA 58 207 47 52 11 1 18 42 2 3 31 2 5 0 1 NA 5 0 0 0 0 0 0 0 0 0 0 308 13 9 2 NA 156 612 124 206 39 2 39 126 15 3 52 266 9 6 1 NA 148 565 88 166 28 3 27 104 5 4 37 26 46 10 2 NA 46 114 10 23 5 0 4 13 1 1 7 255 388 12 71 NA 144 468 70 130 20 5 10 48 15 5 41 15 23 0 1 NA 2 2 0 0 0 0 0 0 0 0 0 597 56 3 5 6 97 337 36 93 13 0 10 54 0 1 18 441 37 3 4 3 73 203 26 53 10 2 3 25 4 1 10 6 13 2 1 NA 1 3 0 0 0 0 0 0 0 0 0 134 9 1 2 2 46 70 11 17 3 0 2 10 2 1 7 1 3 0 Ο ΝΑ 3 0 0 0 0 0 0 0 0 0 0 5 9 0 Ο ΝΑ 3 0 0 0 0 0 0 0 0 0 0 15 1 0 Ο ΝΑ 60 186 15 47 7 0 2 23 1 0 14 gp- ་॰༠!=་ལྟ°!88སྐྱ- ad° °°{¥ 6 1 0 3 3 0 1 0 0 0 0 0 1 54 2 0 1 5 2 0 0 0 0 0 0 0 1 1 13 14 2 11 0 74 1 4 3 4 9 0 0 0 0 0 0 0 1 94 0 0 10 2 6 0 52 3 3 0 1 6 0 0 0 0 0 0 0 1 74 14 8 0 8 19 0 92 5 15 0 3 14 0 30 0 0 0 0 3 0 92 13 9 2 10 0 1 0 0 0 0 0 1 35 1 2 2 4 18 0 52 0 0 5 0 6 0 0 0 0 0 0 1 1 17 0 0 3 1 2 0 0 Ο ΝΑ 0 0 0 1 0 0 0 0 0 0 1 41 0 2 0 4 2 0 3 10 2 2 NA 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 13 1 Ο ΝΑ 3 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 3 22 1 2 NA 3 5 0 2 0 0 0 1 0 0 0 0 0 0 2 0 0 1 0 0 Ο ΝΑ 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 48 53 3 10 NA 38 110 14 34 5 2 3 16 0 2 12 18 0 1 2 0 2 0 32 4 0 2 NA 79 85 6 20 2 0 2 11 0 0 6 12 0 3 0 0 7 0 265 8 4 1 NA 141 539 64 154 27 1 11 65 6 2 35 66 2 4 1 3 20 0 3 10 0 1 NA 69 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 125 3297 141 383 15 61 NA 154 564 56 148 31 7 4 49 3 3 31 59 2 2 12 4 11 0 1 27 7 1 0 Ο ΝΑ 20 27 1 3 0 0 0 1 0 0 1 5 0 0 0 0 0 0 10 147 5 6 0 Ο ΝΑ 11 17 2 2 2 0 0 0 0 0 0 7 0 0 1 0 1 1 9 256 5 12 1 Ο ΝΑ 36 18 0 3 2 0 0 0 0 0 3 3 0 0 4 0 1 1 Insassa er 7 100 3 92 19 2 0 0 0 7 14 2 2 0 0 2 4 0 0 0 6 0 0 0 0 0 0 + 23 - - c = - 3 ་ ་
4) Baseball Data: a) Using the teams as psus (N = with equal 30), draw a one-stage cluster sample of 6 teams (n = 6) probabilities. Your sample should have approximately 150 players altogether. b) Use your sample to estimate the mean of logsal = log(salary) along with its SE. Hint: Create a new dataset with an additional column for logsal. If samo is your sample, then saml = data.frame (sam0, logsal=log (sam0$ salary)) Here is the data A B C D E F G H J K L M N о P Q R S T U V W X Y AA AB AC AD AE AF team leagueID player salary POS G GS InnOuts PO A E DP PB GB AB R H SecB ThiB HR RBI SB CS BB SO IBB HBP SH SF GIDP pitcher 2 ANA AL anderga0 6200000 CF 112 3 ANA AL colonba0 1.1E+07 P 4 ANA AL davanjeO 375000 CF 108 5 ANA AL donnebro 375000 P 6 ANA AL eckstda0 7 ANA AL erstada0 8 ANA AL 2150000 SS 7750000 1B escobke0 5750000 P 142 125 9 ANA AL figgicho 320000 3B 148 10 ANA AL glaustro 9900000 3B 11 ANA AL greggke0 301500 P 12 ANA AL guerrvl0 1.1E+07 RF 156 13 ANA 14 ANA AL AL guilljoo 2200000 LF 148 haltesho 575000 3B 15 ANA AL 16 ANA AL kenneado 2500000 2B lackejo0 375000 P 144 17 ANA AL molinbe0 2025000 C 18 ANA AL molinjo0 335000 C 19 ANA AL ortizra0 20 ANA AL pauljo01 3266667 P 335000 C 21 ANA AL percitro 7833333 P 22 ANA AL rodrifro 375000 P 23 ANA AL salmotio 24 ANA AL seleaa01 9900000 RF 8666667 P 25 ANA AL shielsc0 375000 P 26 ANA AL washbja0 5450000 P 27 ANA AL weberbe0 900000 P 28 ARI NL alomaro0 1000000 2B 29 ARI NL baergca0 1000000 1B 30 ARI 31 ARI NL bautidao 4000000 RF NL choatra0 325750 P 32 ARI NL cintralo 335000 SS 33 ARI NL colbrgro 2750000 1B 34 ARI NL daiglca0 300000 P 35 ARI NL desseel0 4000000 P 36 ARI NL ADI estalbo0 baseball 550000 C ༄^8༞¥8ཨྠཧྨ^ 8ཨྠ¥ཝཱ"b " " ྴ "ng - nn༅%ę¥818E 8" 3 5 136 124 1 58 5 143 135 46 2 97 1 46 3 3 60 3 3 38 79 141 69 154 20 11 36 8#h°⌘ཟླ⌘⌘9°⌘d⌘ལྐ⌘h!༠༠༠༠ཟླ°༢°ཀླསྒྱུg °⌘--༠ 92 2375 211 34 625 27 743 0 126 3575 3196 986 33 625 80 2116 19 495 0 263 3702 3471 22 640 138 3675 32 595 2286 57 1573 14 384 16 504 0 149 0 252 5 117 24 396 0 316 25 448 0 67 23 610 4 135 107 3536 0 152 ་ྒུ༩མྦྷཝ༔༤༦=ས ༔ ༔ 8 ཧྨ ཎྷེ ་ྒུ ཙྩུ ཎྜ ༞ ཤྩ ༠ ༠ ąསྙ༠ ཉྩཱ 5 2 1 NA 112 442 57 133 20 1 14 75 2 1 29 75 8 30 3 4 NA 3 3 0 0 0 0 0 0 0 0 0 75 1 0 1 NA 108 285 41 79 11 4 7 34 18 3 46 2 2 0 Ο ΝΑ 5 0 0 0 0 0 0 0 0 0 0 198 309 6 75 NA 142 566 92 156 24 1 2 35 16 5 42 49 66 4 83 NA 125 495 79 146 29 1 7 69 16 1 37 16 24 0 1 NA 1 2 0 0 0 0 0 0 0 0 0 57 129 11 9 NA 148 577 83 171 22 17 5 60 34 13 49 11 27 2 2 NA 58 207 47 52 11 1 18 42 2 3 31 2 5 0 1 NA 5 0 0 0 0 0 0 0 0 0 0 308 13 9 2 NA 156 612 124 206 39 2 39 126 15 3 52 266 9 6 1 NA 148 565 88 166 28 3 27 104 5 4 37 26 46 10 2 NA 46 114 10 23 5 0 4 13 1 1 7 255 388 12 71 NA 144 468 70 130 20 5 10 48 15 5 41 15 23 0 1 NA 2 2 0 0 0 0 0 0 0 0 0 597 56 3 5 6 97 337 36 93 13 0 10 54 0 1 18 441 37 3 4 3 73 203 26 53 10 2 3 25 4 1 10 6 13 2 1 NA 1 3 0 0 0 0 0 0 0 0 0 134 9 1 2 2 46 70 11 17 3 0 2 10 2 1 7 1 3 0 Ο ΝΑ 3 0 0 0 0 0 0 0 0 0 0 5 9 0 Ο ΝΑ 3 0 0 0 0 0 0 0 0 0 0 15 1 0 Ο ΝΑ 60 186 15 47 7 0 2 23 1 0 14 gp- ་॰༠!=་ལྟ°!88སྐྱ- ad° °°{¥ 6 1 0 3 3 0 1 0 0 0 0 0 1 54 2 0 1 5 2 0 0 0 0 0 0 0 1 1 13 14 2 11 0 74 1 4 3 4 9 0 0 0 0 0 0 0 1 94 0 0 10 2 6 0 52 3 3 0 1 6 0 0 0 0 0 0 0 1 74 14 8 0 8 19 0 92 5 15 0 3 14 0 30 0 0 0 0 3 0 92 13 9 2 10 0 1 0 0 0 0 0 1 35 1 2 2 4 18 0 52 0 0 5 0 6 0 0 0 0 0 0 1 1 17 0 0 3 1 2 0 0 Ο ΝΑ 0 0 0 1 0 0 0 0 0 0 1 41 0 2 0 4 2 0 3 10 2 2 NA 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 13 1 Ο ΝΑ 3 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 3 22 1 2 NA 3 5 0 2 0 0 0 1 0 0 0 0 0 0 2 0 0 1 0 0 Ο ΝΑ 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 48 53 3 10 NA 38 110 14 34 5 2 3 16 0 2 12 18 0 1 2 0 2 0 32 4 0 2 NA 79 85 6 20 2 0 2 11 0 0 6 12 0 3 0 0 7 0 265 8 4 1 NA 141 539 64 154 27 1 11 65 6 2 35 66 2 4 1 3 20 0 3 10 0 1 NA 69 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 125 3297 141 383 15 61 NA 154 564 56 148 31 7 4 49 3 3 31 59 2 2 12 4 11 0 1 27 7 1 0 Ο ΝΑ 20 27 1 3 0 0 0 1 0 0 1 5 0 0 0 0 0 0 10 147 5 6 0 Ο ΝΑ 11 17 2 2 2 0 0 0 0 0 0 7 0 0 1 0 1 1 9 256 5 12 1 Ο ΝΑ 36 18 0 3 2 0 0 0 0 0 3 3 0 0 4 0 1 1 Insassa er 7 100 3 92 19 2 0 0 0 7 14 2 2 0 0 2 4 0 0 0 6 0 0 0 0 0 0 + 23 - - c = - 3 ་ ་
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
Could you solve it with R only?
Since the program does not let me upload the data I took a screenshot of the data, So just solve the question with the given data (take max as 36 instead of 150).
Thank you.
Transcribed Image Text:4)
Baseball Data:
a) Using the teams as psus (N
=
with equal
30), draw a one-stage cluster sample of 6 teams (n = 6)
probabilities. Your sample should have approximately 150 players altogether.
b) Use your sample to estimate the mean of logsal = log(salary) along with its SE.
Hint: Create a new dataset with an additional column for logsal. If samo is your sample, then
saml = data.frame (sam0, logsal=log (sam0$ salary))
Here is
the data
A
B
C
D
E
F
G
H
J
K
L
M
N
о
P
Q
R
S
T
U
V
W
X
Y
AA
AB
AC
AD
AE
AF
team
leagueID player
salary POS
G
GS
InnOuts
PO
A
E
DP
PB
GB
AB
R
H
SecB
ThiB
HR
RBI
SB
CS
BB
SO
IBB
HBP
SH
SF
GIDP
pitcher
2
ANA
AL
anderga0 6200000 CF
112
3 ANA
AL
colonba0
1.1E+07 P
4 ANA
AL
davanjeO
375000 CF
108
5
ANA
AL
donnebro
375000 P
6 ANA
AL
eckstda0
7 ANA
AL
erstada0
8 ANA
AL
2150000 SS
7750000 1B
escobke0 5750000 P
142
125
9 ANA
AL
figgicho
320000 3B
148
10 ANA
AL
glaustro
9900000 3B
11 ANA
AL
greggke0
301500 P
12 ANA
AL
guerrvl0
1.1E+07 RF
156
13 ANA
14 ANA
AL
AL
guilljoo
2200000 LF
148
haltesho
575000 3B
15 ANA AL
16 ANA
AL
kenneado 2500000 2B
lackejo0 375000 P
144
17 ANA
AL
molinbe0 2025000 C
18 ANA
AL
molinjo0
335000 C
19 ANA
AL
ortizra0
20 ANA
AL
pauljo01
3266667 P
335000 C
21 ANA
AL
percitro
7833333 P
22 ANA
AL
rodrifro
375000 P
23 ANA
AL
salmotio
24 ANA
AL
seleaa01
9900000 RF
8666667 P
25 ANA
AL
shielsc0
375000 P
26 ANA
AL
washbja0 5450000 P
27 ANA
AL
weberbe0 900000 P
28 ARI
NL
alomaro0
1000000 2B
29 ARI
NL
baergca0 1000000 1B
30 ARI
31 ARI
NL
bautidao 4000000 RF
NL
choatra0
325750 P
32 ARI
NL
cintralo
335000 SS
33 ARI
NL
colbrgro
2750000 1B
34 ARI
NL
daiglca0
300000 P
35 ARI
NL
desseel0
4000000 P
36 ARI
NL
ADI
estalbo0
baseball
550000 C
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263
3702
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138
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32
595
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14
384
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504
0
149
0
252
5
117
24
396
0
316
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448
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112
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29
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30
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3
3
0
0
0
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108
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42
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125
495
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1
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1
37
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24
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1
2
0
0
0
0
0
0
0
0
0
57
129
11
9 NA
148
577
83
171
22
17
5
60
34
13
49
11
27
2
2 NA
58
207
47
52
11
1
18
42
2
3
31
2
5
0
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5
0
0
0
0
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308
13
9
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156
612
124
206
39
2
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266
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148
565
88
166
28
3
27
104
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4
37
26
46
10
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46
114
10
23
5
0
4
13
1
1
7
255
388
12
71 NA
144
468
70
130
20
5
10
48
15
5
41
15
23
0
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2
2
0
0
0
0
0
0
0
0
0
597
56
3
5
6
97
337
36
93
13
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10
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0
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18
441
37
3
4
3
73
203
26
53
10
2
3
25
4
1
10
6
13
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1 NA
1
3
0
0
0
0
0
0
0
0
0
134
9
1
2
2
46
70
11
17
3
0
2
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1
7
1
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9
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0
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0
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0
2
23
1
0
14
gp- ་॰༠!=་ལྟ°!88སྐྱ- ad° °°{¥
6
1
0
3
3
0
1
0
0
0
0
0
1
54
2
0
1
5
2
0
0
0
0
0
0
0
1
1
13
14
2
11
0
74
1
4
3
4
9
0
0
0
0
0
0
0
1
94
0
0
10
2
6
0
52
3
3
0
1
6
0
0
0
0
0
0
0
1
74
14
8
0
8
19
0
92
5
15
0
3
14
0
30
0
0
0
0
3
0
92
13
9
2
10
0
1
0
0
0
0
0
1
35
1
2
2
4
18
0
52
0
0
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0
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0
0
0
0
0
0
1
1
17
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0
0
1
0
0
0
0
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2 NA
1
1
0
0
0
0
0
0
0
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1
0
0
0
0
0
1
13
1
Ο ΝΑ
3
1
0
0
0
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0
0
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0
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1
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1
3
22
1
2 NA
3
5
0
2
0
0
0
1
0
0
0
0
0
0
2
0
0
1
0
0
Ο ΝΑ
18
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
48
53
3
10 NA
38
110
14
34
5
2
3
16
0
2
12
18
0
1
2
0
2
0
32
4
0
2 NA
79
85
6
20
2
0
2
11
0
0
6
12
0
3
0
0
7
0
265
8
4
1 NA
141
539
64
154
27
1
11
65
6
2
35
66
2
4
1
3
20
0
3
10
0
1 NA
69
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
125
3297
141
383
15
61 NA
154
564
56
148
31
7
4
49
3
3
31
59
2
2
12
4
11
0
1
27
7
1
0
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20
27
1
3
0
0
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1
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1
5
0
0
0
0
0
0
10
147
5
6
0
Ο ΝΑ
11
17
2
2
2
0
0
0
0
0
0
7
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0
1
0
1
1
9
256
5
12
1
Ο ΝΑ
36
18
0
3
2
0
0
0
0
0
3
3
0
0
4
0
1
1
Insassa er
7
100
3
92
19
2
0
0
0
7
14
2
2
0
0
2
4
0
0
0
6
0
0
0
0
0
0
+
23
- -
c
=
-
3
་
་
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Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
Elementary Statistics: Picturing the World (7th E…
Statistics
ISBN:
9780134683416
Author:
Ron Larson, Betsy Farber
Publisher:
PEARSON
The Basic Practice of Statistics
Statistics
ISBN:
9781319042578
Author:
David S. Moore, William I. Notz, Michael A. Fligner
Publisher:
W. H. Freeman
Introduction to the Practice of Statistics
Statistics
ISBN:
9781319013387
Author:
David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:
W. H. Freeman