I have a dataset of survey. how do I use logistic regression method to identify which variables are significant to the result("Satisified"). Please assist. Thanks! Trip Type Boarding experience Delayed arrival Booking
I have a dataset of survey. how do I use logistic regression method to identify which variables are significant to the result("Satisified"). Please assist. Thanks! Trip Type Boarding experience Delayed arrival Booking
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Question
I have a dataset of survey. how do I use logistic regression method to identify which variables are significant to the result("Satisified").
Please assist.
Thanks!
| Trip Type | Boarding experience | Delayed arrival | Booking experience | Gender | Seat Type | Delayed departure | Cleanliness | Trip Distance | Membership | Wifi | Staff friendliness | Age | Quality Food | Seat comfort | Checkin service | Luggage service | Online experience | Departure Convenience | Seat type | Satisfied |
| personal | 3 | 30 | 5 | Female | economy | 40 | 5 | 2366 | points membership | 4 | 5 | 29 | 1 | 5 | 5 | 5 | 4 | 1 | 1 | 1 |
| business | 3 | 1 | 5 | Male | premium | 0 | 5 | 3562 | points membership | 4 | 5 | 48 | 5 | 5 | 4 | 5 | 5 | 1 | 5 | 1 |
| business | 5 | 0 | 5 | Male | economy | 0 | 1 | 3042 | points membership | 5 | 3 | 47 | 1 | 1 | 1 | 2 | 5 | 1 | 5 | 1 |
| business | 5 | 22 | 4 | Male | premium | 24 | 4 | 754 | points membership | 3 | 4 | 53 | 3 | 5 | 3 | 4 | 5 | 3 | 3 | 1 |
| business | 5 | 36 | 5 | Female | premium | 31 | 5 | 77 | points membership | 5 | 5 | 40 | 3 | 5 | 5 | 4 | 4 | 3 | 3 | 1 |
| business | 3 | 0 | 3 | Female | economy | 0 | 3 | 435 | points membership | 1 | 3 | 62 | 2 | 3 | 4 | 3 | 3 | 2 | 3 | 0 |
| business | 5 | 9 | 4 | Male | premium | 16 | 4 | 1670 | points membership | 2 | 4 | 59 | 5 | 4 | 5 | 4 | 4 | 5 | 5 | 1 |
| business | 5 | 0 | 5 | Male | premium | 2 | 4 | 2835 | points membership | 5 | 3 | 30 | 2 | 2 | 5 | 4 | 5 | 3 | 2 | 1 |
| business | 1 | 0 | 4 | Female | standard | 0 | 4 | 533 | points membership | 4 | 4 | 77 | 2 | 4 | 3 | 4 | 3 | 2 | 4 | 0 |
| personal | 2 | 0 | 2 | Male | standard | 0 | 4 | 1719 | points membership | 2 | 4 | 34 | 3 | 2 | 4 | 4 | 1 | 5 | 3 | 0 |
| personal | 5 | 0 | 3 | Female | premium | 0 | 3 | 1388 | points membership | 3 | 3 | 23 | 5 | 4 | 5 | 5 | 4 | 5 | 5 | 1 |
| personal | 3 | 0 | 3 | Male | economy | 0 | 4 | 2031 | points membership | 3 | 1 | 22 | 3 | 3 | 1 | 3 | 3 | 3 | 3 | 0 |
| business | 5 | 0 | 5 | Female | premium | 0 | 4 | 3911 | points membership | 5 | 3 | 25 | 5 | 5 | 4 | 5 | 5 | 5 | 5 | 1 |
| personal | 4 | 0 | 4 | Female | economy | 0 | 5 | 1146 | points membership | 4 | 4 | 20 | 3 | 5 | 5 | 4 | 4 | 5 | 3 | 0 |
| personal | 5 | 0 | 4 | Female | premium | 0 | 4 | 2600 | points membership | 3 | 4 | 65 | 2 | 4 | 3 | 5 | 5 | 1 | 1 | 1 |
| personal | 3 | 25 | 3 | Male | economy | 1 | 4 | 1887 | points membership | 3 | 5 | 59 | 0 | 2 | 5 | 5 | 3 | 5 | 1 | 0 |
| business | 5 | 0 | 4 | Female | premium | 0 | 4 | 1780 | points membership | 2 | 4 | 57 | 5 | 4 | 5 | 4 | 4 | 5 | 5 | 1 |
| business | 2 | 13 | 2 | Female | economy | 0 | 5 | 2106 | non-member | 2 | 4 | 20 | 5 | 3 | 5 | 4 | 2 | 5 | 5 | 1 |
| business | 3 | 13 | 2 | Female | premium | 0 | 2 | 894 | points membership | 4 | 2 | 46 | 2 | 2 | 4 | 2 | 4 | 2 | 2 | 0 |
Transcribed Image Text:Trip Type
▾
personal
business
business
business
business
business
business
business
business
personal
personal
personal
business
personal
personal
personal
business
business
Boarding Delayed Booking
experience arrival experience
▼
3
3
5
5
5
3
5
5
1
2
5
3
5
4
5
3
5
2
▼
30
1
0
22
36
0
9
0
0
0
0
0
0
0
0
25
0
13
Gender
5 Female
5 Male
5 Male
4 Male
5 Female
3 Female
4 Male
5 Male
4 Female
2 Male
3 Female
3 Male
5 Female
4 Female
4 Female
3 Male
4 Female
2 Female
Seat Delayed
Type departure
→
economy
premium
economy
premium
premium
economy
premium
premium
standard
standard
premium
economy
premium
economy
premium
economy
premium
economy
40
0
0
24
31
0
16
2
0
0
0
0
0
0
0
1
0
0
Cleanliness
▼
5
5
1
4
5
3
4
4
4
4
3
4
4
5
4
4
4
5
Trip
Distance
Membership
Y
2366 points membership
3562 points membership
3042 points membership
754 points membership
77 points membership
435 points membership
1670 points membership
2835 points membership
533 points membership
1719 points membership
1388 points membership
2031 points membership
3911 points membership
1146 points membership
2600 points membership
1887 points membership
1780 points membership
2106 non-member
Wifi
▼
4
4
5
3
5
1
2
5
4
2
3
3
5
4
3
3
2
2
Staff
friendliness
Y
5
5
3
4
5
3
4
3
4
4
3
1
3
4
4
5
4
4
Age
▾
29
48
47
53
40
62
59
30
77
34
23
22
25
20
65
59
57
20
Quality
Food
▼
1
5
1
3
3
2
5
2
2
3
5
3
5
3
2
0
5
5
Seat Checkin Luggage Online
Departure
comfort service service
service experience Convenience
Y
5
5
1
5
5
3
4
2
4
2
4
3
5
5
4
2
4
3
Y
5
4
1
3
5
4
5
5
3
4
5
1
4
5
3
5
5
5
Y
5
5
2
4
4
3
4
4₁
4
4
5
3
5
4
5
5
4
4
▾
4
5
5
5
4
3
4
5
3
1
4
3
5
4
5
3
4
2
▼
1
1
1
3
3
2
5
3
2
5
5
3
5
5
1
5
5
5
Seat type Satisfied
1
5
5
3
3
3
5
2
4
3
5
3
5
3
1
1
5
5
▼
1
1
1
1
1
0
1
1
0
0
1
0
1
0
1
0
1
1
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