Create a table of counts and percentages for the preferred award type. To do this, try stat> tables>tally individual variables. Choose the variable and select both counts and percentages. Copy the Minitab output and paste it below. Overall which award was listed most frequently? Age Gender
Create a table of counts and percentages for the preferred award type. To do this, try stat> tables>tally individual variables. Choose the variable and select both counts and percentages. Copy the Minitab output and paste it below. Overall which award was listed most frequently? Age Gender
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
Create a table of counts and percentages for the preferred award type. To do this, try stat> tables>tally individual variables. Choose the variable and select both counts and percentages. Copy the Minitab output and paste it below. Overall which award was listed most frequently?
Age | Gender | Height | Arm Length |
Number Siblings
|
Birth Order | Handedness |
Number Classes
|
Number Credits
|
Hrs. Exercise
|
Athlete at Broome
|
Hrs. TV | Award | Pulse |
Number Piercings
|
Facebook Friends
|
Followers on Instagram
|
Tattoo | Division | Local | section |
18 | f | 63 | 27.5 | 2 | 3 | r | 4 | 14 | 1.5 | n | 3 | NP | 74 | 2 | 82 | 72 | n | HS | y | 4 |
19 | m | 74 | 31.5 | 4 | 5 | r | 4 | 11 | 5 | n | 1.5 | O | 0 | 600 | 700 | n | STEM | y | 4 | |
17 | f | 66 | 29.2 | 2 | 2 | l | 6 | 15 | 5 | y | 21 | NP | 64 | 2 | 1801 | 1370 | n | HS | y | 4 |
18 | f | 66 | 30 | 5 | 5 | r | 4 | 12 | 30 | n | 15 | NP | 91 | 1 | 500 | 700 | n | LA | y | 4 |
30 | m | 67 | 30 | 3 | 1 | r | 4 | 12 | 5 | n | 5 | OG | 83 | 2 | y | STEM | n | 4 | ||
20 | f | 66 | 27 | 1 | r | 5 | 15 | 7 | n | 7 | AA | 65 | 1 | 50 | 1015 | n | LA | y | 4 | |
26 | f | 67 | 29 | 1 | 1 | r | 5 | 14 | 3 | n | 0 | OG | 22 | 5 | 130 | 120 | y | HS | y | 4 |
18 | f | 64 | 28 | 1 | 2 | r | 5 | 13 | 7 | y | 6 | NP | 20 | 5 | 50 | 50 | y | STEM | y | 4 |
17 | f | 64 | 28 | 3 | 2 | r | 5 | 13 | 2 | n | 3 | NP | 9 | 407 | 472 | n | y | 4 | ||
19 | m | 69 | 2 | 1 | r | 5 | 13 | 1 | n | 10 | NP | 0 | 0 | 70 | y | STEM | y | 4 | ||
18 | f | 66 | 27.5 | 4 | 1 | r | 5 | 12 | n | 48 | AA | 70 | 2 | 289 | 246 | y | HS | n | 4 | |
19 | m | 75 | 30.5 | 3 | 2 | r | 5 | 13 | 4 | n | 2 | OG | 86 | 0 | 254 | 317 | n | LA | y | 4 |
18 | m | 70 | 31 | 2 | 2 | r | 4 | 14 | 1 | n | 18 | NP | 69 | 0 | 0 | 280 | n | HS | n | 4 |
18 | m | 73 | 30.5 | 2 | 2 | r | 6 | 17 | 4 | n | 6 | NP | 0 | 0 | 1000 | n | n | 4 | ||
18 | m | 70 | 31.5 | 2 | 2 | r | 5 | 16 | 3 | n | 40 | OG | 71 | 0 | 300 | 0 | n | STEM | n | 4 |
19 | f | 68 | 29 | 2 | 2 | r | 5 | 12 | 0 | n | 4 | OG | 2 | 0 | 1000 | n | n | 4 | ||
18 | f | 64 | 27.5 | 3 | 4 | r | 5 | 15 | 0 | n | 10 | OG | 92 | 9 | 200 | 400 | y | y | 4 | |
18 | m | 71 | 31.5 | 1 | 1 | r | 4 | 12 | 3 | y | 8 | OG | 1 | 0 | 792 | y | BPS | n | 4 | |
19 | m | 71 | 30 | 9 | 2 | r | 6 | 15 | 10 | n | 1 | OG | 2 | 0 | 2000 | y | HS | y | 4 | |
24 | m | 70 | 30 | 5 | 6 | a | 1 | 3 | 3.5 | n | 15.5 | AA | 58 | 0 | 2000 | 450 | n | LA | y | 12 |
21 | m | 72 | 31 | 2 | 2 | r | 5 | 15 | 5 | n | 10 | NP | 52 | 1 | 0 | 378 | y | BPS | y | 12 |
18 | m | 69 | 30 | 1 | 2 | r | 7 | 20.5 | 5 | n | 3 | NP | 62 | 0 | 186 | 716 | n | HS | n | 12 |
44 | f | 64 | 25 | 5 | 2 | r | 4 | 13 | 3 | n | 3 | NP | 65 | 6 | 500 | 25 | y | HS | y | 12 |
18 | f | 63 | 28 | 0 | 1 | r | 4 | 14 | 15 | n | 5 | NP | 58 | 7 | 0 | 600 | n | HS | y | 12 |
20 | f | 66 | 29 | 1 | 2 | r | 5 | 13 | 6 | n | 5 | OG | 62 | 4 | 200 | 1000 | y | HS | n | 12 |
22 | f | 68 | 30 | 1 | 2 | r | 2 | 7 | 7 | n | 20 | AA | 68 | 0 | 0 | 0 | n | n | 12 | |
18 | f | 64 | 28 | 0 | 1 | r | 6 | 18 | 7 | n | 1 | NP | 81 | 4 | 600 | 2000 | y | STEM | n | 12 |
32 | f | 63 | 26.5 | 2 | 1 | r | 4 | 12 | 0 | n | 7 | NP | 5 | 1600 | 0 | n | HS | y | 12 | |
19 | f | 67 | 27 | 4 | 1 | r | 5 | 16 | 0 | n | 2 | NP | 88 | 4 | 923 | 1597 | y | HS | n | 12 |
28 | m | 66 | 29 | 5 | 2 | r | 4 | 14 | 1 | n | 20 | NP | 70 | 0 | 250 | 0 | y | HS | n | 12 |
19 | f | 63 | 27 | 1 | 2 | r | 6 | 17 | 6 | y | 9 | OG | 610 | 7 | 0 | 1500 | n | HS | y | 12 |
18 | f | 63 | 27 | 3 | r | 5 | 15 | 7 | y | 2 | OG | 68 | 4 | 100 | 1000 | y | LA | n | 12 | |
19 | f | 61.5 | 27 | 3 | 1 | r | 5 | 14 | 2.5 | y | 2.5 | OG | 77 | 8 | 400 | 800 | n | HS | y | 12 |
19 | f | 66 | 28 | 1 | 1 | r | 4 | 12 | 7 | n | 12 | NP | 70 | 5 | 150 | 900 | n | HS | y | 12 |
19 | f | 62 | 25.5 | 2 | 3 | r | 4 | 12 | 0 | n | 20 | AA | 81 | 2 | 1000 | 340 | n | LA | y | 12 |
21 | f | 65 | 28 | 7 | 4 | r | 4 | 12 | 6 | unsure | 8 | NP | 7 | 0 | 2000 | y | STEM | y | 12 | |
18 | f | 68 | 29.5 | 1 | 2 | l | 5 | 16 | 3 | n | 5 | OG | 45 | 2 | 0 | 65 | n | HS | y | 12 |
20 | f | 66 | 29.5 | 0 | 1 | r | 5 | 13 | 0 | n | 8 | NP | 60 | 2 | 500 | 401 | n | LA | n | 12 |
24 | f | 68 | 28 | 0 | 1 | r | 5 | 19 | 3 | n | 3.5 | AA | 86 | 2 | 0 | 62 | y | BPS | n | 11 |
18 | f | 64 | 29 | 1 | 1 | r | 5 | 15 | 3 | n | 7 | NP | 75 | 2 | 120 | 1624 | n | LA | n | 11 |
18 | m | 71 | 33 | 0 | 1 | r | 5 | 1 | n | 4 | AA | 74 | 0 | 0 | 30 | n | LA | y | 11 | |
19 | f | 66 | 2 | 2 | r | 5 | 16 | 2 | n | 7 | AA | 82 | 4 | 60 | 585 | n | BPS | n | 11 | |
19 | f | 61 | 28 | 1 | 2 | l | 4 | 12 | 0 | n | 10 | AA | 93 | 1 | 346 | 836 | y | HS | n | 11 |
18 | f | 69 | 29 | 1 | 2 | l | 5 | 15 | 60 | unsure | 14 | AA | 104 | 4 | 2000 | 1000 | y | LA | y | 11 |
18 | f | 64 | 28.5 | 2 | 2 | r | 5 | 15 | 4 | n | 2 | OG | 78 | 4 | 0 | 1580 | y | HS | y | 11 |
19 | f | 66 | 27 | 1 | 1 | r | 5 | 13 | 3 | n | 10 | OG | 75 | 7 | 500 | 1200 | y | LA | y | 11 |
18 | f | 65 | 25.3 | 4 | 3 | r | 5 | 15 | 14 | n | 20 | AA | 74 | 4 | 250 | 1800 | y | LA | y | 11 |
20 | f | 66 | 28.5 | 5 | 3 | r | 5 | 15 | 0.5 | n | 60 | AA | 74 | 3 | 2000 | 500 | y | LA | y | 11 |
20 | m | 71 | 30 | 1 | 1 | r | 7 | 17 | 8.5 | n | NP | 0 | 950 | 1112 | n | LA | n | 11 | ||
19 | f | 63 | 25 | 7 | 6 | r | 4 | 12 | 7 | n | 3 | AA | 101 | 2 | 2000 | 1600 | n | LA | y | 11 |
18 | f | 63 | 26 | 8 | 7 | r | 5 | 16 | 5 | n | 2 | OG | 70 | 6 | 500 | 730 | y | HS | y | 11 |
21 | m | 66 | 29 | 0 | 1 | l | 4 | 14 | 10 | n | 2 | OG | 78 | 0 | 0 | 600 | y | STEM | n | 11 |
22 | f | 69 | 27 | 0 | 1 | r | 2 | 8 | 5 | n | 3 | OG | 86 | 6 | 2000 | 1500 | y | HS | y | 11 |
39 | f | 64 | 25.5 | 6 | 6 | r | 2 | 6 | 5 | n | 10 | NP | 80 | 2 | 50 | 0 | y | HS | y | 11 |
20 | f | 62 | 25 | 1 | 1 | r | 4 | 14 | 20 | n | 0 | NP | 0 | 0 | 0 | n | LA | y | 11 | |
18 | m | 67 | 28.5 | 5 | 2 | r | 5 | 16 | 8 | y | 0 | NP | 65 | 2 | 350 | 955 | y | y | 11 | |
18 | f | 61 | 27 | 4 | 1 | r | 5 | 15 | 14 | unsure | 7 | OG | 88 | 0 | 0 | 0 | n | HS | y | 11 |
24 | f | 62 | 27.5 | 7 | 5 | r | 5 |
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