Identify: What would be a good research question for each explanatory variable? Q1: Q2: Who and what would be the individual sampling unit? (What will the observations be(not variable)? Who would be the population of interest? Use this dataset for the questions(data was extracted from a larger dataset): Occupatio Monthly Income Credit Sco Years of Employme Finance Status Teacher 40000 750 10 Good Engineer 60000 800 8 Good Nurse 25000 650 5 Fair Doctor 80000 900 Businessm 100000 700 Engineer 750 Teacher 700 Nurse 600 850 800 Businessm Doctor Engineer Salespersc Teacher Entreprene Numeric Explanatory Variable, Categorical Explanatory Variable, Binary Categorical Explanatory variable, Response variable Lawyer Doctor Accountan Software [ Nurse Electrician Architect Marketing Chef Police Offi Real Estat 50000 35000 20000 120000 90000 6000 3000 4000 8000 9000 12000 5000 7000 3500 2500 5500 4500 3000 4000 6000 700 600 750 800 820 900 720 780 650 550 740 690 620 680 760 12 Excellent 15 Good 6 Fair 8 Fair 3 Poor 20 Excellent 10 Good 3 Good 1 Fair 5 Excellent 7 Good 10 Excellent 12 Excellent 4 Good 6 Excellent 2 Fair 1 Poor 8 Good 4 Fair 2 Fair 3 Fair 5 Good Car Finance History No issues Yes No issues Yes Late payment 2 months ag No No issues Yes Late payment 6 months ag Yes No issues No Late payment 3 months ag Yes Late payment 1 month agc No No issues Yes No issues Yes Excellent Poor Excellent Excellent Good Excellent Fair Good Poor Poor Excellent Fair Poor Fair Good Yes No Yes Yes Yes Yes No Yes No No Yes No No No Yes Number of Childre 2 1 0 3 2 1

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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/&/&/&:&($;&/&&:&;&;&/&:&;$;&&:&/;&;$;$&:&;&;&;&:
Identify:
What would be a good research question for each
explanatory variable?
Q1:
Q2:
Who and what would be the individual sampling
unit? (What will the observations be(not variable)?
Who would be the population of interest?
Use this dataset for the questions(data was
extracted from a larger dataset):
Occupatio Monthly Income Credit Sco Years of Employme Finance Status
Teacher
40000
750
10 Good
Engineer
60000
800
8 Good
Nurse
25000
5 Fair
Doctor
80000
12 Excellent
100000
15 Good
50000
35000
20000
120000
90000
Businessm
Engineer
Teacher
Nurse
Businessm
Doctor
Engineer
Salespersc
Teacher
Numeric Explanatory Variable, Categorical
Explanatory Variable, Binary Categorical
Explanatory variable, Response variable
Entreprene
Lawyer
Doctor
Accountan
Software D
Nurse
Electrician
Architect
Marketing
Chef
Police Offi
Real Estate
AUL
6000
3000
4000
8000
9000
12000
5000
7000
3500
2500
5500
4500
3000
4000
6000
10000
650
900
700
750
700
600
850
800
700
600
750
800
820
900
720
780
650
550
740
690
620
680
760
6 Fair
8 Fair
3 Poor
20 Excellent
10 Good
3 Good
1 Fair
5 Excellent
7 Good
10 Excellent
12 Excellent
4 Good
6 Excellent
2 Fair
1 Poor
8 Good
4 Fair
2 Fair
3 Fair
5 Good
Finance History
No issues
Car
Yes
No issues
Yes
Late payment 2 months ag No
No issues
Yes
Late payment 6 months ag Yes
No issues
No
Late payment 3 months ag Yes
Late payment 1 month agc No
No issues
Yes
No issues
Yes
Excellent
Poor
Excellent
Excellent
Good
Excellent
Fair
Good
Poor
Poor
Excellent
Fair
Poor
Fair
Good
Yes
No
Yes
Yes
Yes
Yes
No
Yes
No
No
Yes
No
No
No
Yes
Number of Childre
2
1
0
3
2
1
Transcribed Image Text:Identify: What would be a good research question for each explanatory variable? Q1: Q2: Who and what would be the individual sampling unit? (What will the observations be(not variable)? Who would be the population of interest? Use this dataset for the questions(data was extracted from a larger dataset): Occupatio Monthly Income Credit Sco Years of Employme Finance Status Teacher 40000 750 10 Good Engineer 60000 800 8 Good Nurse 25000 5 Fair Doctor 80000 12 Excellent 100000 15 Good 50000 35000 20000 120000 90000 Businessm Engineer Teacher Nurse Businessm Doctor Engineer Salespersc Teacher Numeric Explanatory Variable, Categorical Explanatory Variable, Binary Categorical Explanatory variable, Response variable Entreprene Lawyer Doctor Accountan Software D Nurse Electrician Architect Marketing Chef Police Offi Real Estate AUL 6000 3000 4000 8000 9000 12000 5000 7000 3500 2500 5500 4500 3000 4000 6000 10000 650 900 700 750 700 600 850 800 700 600 750 800 820 900 720 780 650 550 740 690 620 680 760 6 Fair 8 Fair 3 Poor 20 Excellent 10 Good 3 Good 1 Fair 5 Excellent 7 Good 10 Excellent 12 Excellent 4 Good 6 Excellent 2 Fair 1 Poor 8 Good 4 Fair 2 Fair 3 Fair 5 Good Finance History No issues Car Yes No issues Yes Late payment 2 months ag No No issues Yes Late payment 6 months ag Yes No issues No Late payment 3 months ag Yes Late payment 1 month agc No No issues Yes No issues Yes Excellent Poor Excellent Excellent Good Excellent Fair Good Poor Poor Excellent Fair Poor Fair Good Yes No Yes Yes Yes Yes No Yes No No Yes No No No Yes Number of Childre 2 1 0 3 2 1
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