Answer the following question based on analysing the data in the program: ID Age Gender Blood Pressure Cholesterol Blood Sugar BMI Diabetes Status 1 45 Male 130/80 210 Normal 25.5 No 2 32 Female 120/75 180 Normal 22.0 No 3 38 Male 140/85 220 High 28.3 Yes 4 27 Female 110/70 160 Normal 20.1 No 5 29 Male 125/78 200 Normal 23.8 No 6 35 Female 135/82 190 Normal 24.9 No 7 41 Male 145/88 240 High 29.7 Yes 8 45 Male 130/80 210 Normal 25.5 No 9 32 Female 120/75 180 Normal 22.0 No 10 38 Male 140/85 220 High 28.3 Yes 11 27 Female 110/70 160 Normal 20.1 No 12 29 Male 125/78 200 Normal 23.8 No 13 35 Female 135/82 190 Normal 24.9 No 14 41 Male 145/88 240 High 29.7 Yes 15 45 Male 130/80 210 Normal 25.5 No 16 32 Female 120/75 180 Normal 22.0 No 17 38 Male 140/85 220 High 28.3 Yes 18 27 Female 110/70 160 Normal 20.1 No 19 29 Male 125/78 200 Normal 23.8 No 20 45 Female 135/82 190 Normal 24.9 No 21 32 Male 145/88 240 High 29.7 Yes 22 38 Male 130/80 210 Normal 25.5 No 23 27 Female 120/75 180 Normal 22.0 No 24 29 Male 140/85 220 High 28.3 Yes 25 35 Female 135/82 190 Normal 20.1 No 1. Descriptive Statistics: -What is the average age of the patients? - What is the average BMI (Body Mass Index)? - How many patients have diabetes? 2. Compute Variable: - Create a new variable "Blood Pressure_Category" with values "Normal" for blood pressure below 120/80, "Prehypertension" for blood pressure between 120/80 and 139/89, and "Hypertension" for blood pressure 140/90 and above. 3. Frequency Analysis: - What is the distribution of gender among the patients? - How many patients have normal blood sugar levels, and how many have high blood sugar levels? 4. Correlation Analysis: - Is there any correlation between age and cholesterol levels? - Is there any correlation between BMI and diabetes status? 5. Logistic Regression: - Can you predict the likelihood of having diabetes based on age, gender, blood pressure, cholesterol levels, blood sugar levels, and BMI? 6. Charts/Graphs: - Create a histogram to visualize the distribution of cholesterol levels. - Create a bar chart to compare the average BMI based on gender. - Create a pie chart to visualize the distribution of diabetes status among the patients.
Answer the following question based on analysing the data in the program: ID Age Gender Blood Pressure Cholesterol Blood Sugar BMI Diabetes Status 1 45 Male 130/80 210 Normal 25.5 No 2 32 Female 120/75 180 Normal 22.0 No 3 38 Male 140/85 220 High 28.3 Yes 4 27 Female 110/70 160 Normal 20.1 No 5 29 Male 125/78 200 Normal 23.8 No 6 35 Female 135/82 190 Normal 24.9 No 7 41 Male 145/88 240 High 29.7 Yes 8 45 Male 130/80 210 Normal 25.5 No 9 32 Female 120/75 180 Normal 22.0 No 10 38 Male 140/85 220 High 28.3 Yes 11 27 Female 110/70 160 Normal 20.1 No 12 29 Male 125/78 200 Normal 23.8 No 13 35 Female 135/82 190 Normal 24.9 No 14 41 Male 145/88 240 High 29.7 Yes 15 45 Male 130/80 210 Normal 25.5 No 16 32 Female 120/75 180 Normal 22.0 No 17 38 Male 140/85 220 High 28.3 Yes 18 27 Female 110/70 160 Normal 20.1 No 19 29 Male 125/78 200 Normal 23.8 No 20 45 Female 135/82 190 Normal 24.9 No 21 32 Male 145/88 240 High 29.7 Yes 22 38 Male 130/80 210 Normal 25.5 No 23 27 Female 120/75 180 Normal 22.0 No 24 29 Male 140/85 220 High 28.3 Yes 25 35 Female 135/82 190 Normal 20.1 No 1. Descriptive Statistics: -What is the average age of the patients? - What is the average BMI (Body Mass Index)? - How many patients have diabetes? 2. Compute Variable: - Create a new variable "Blood Pressure_Category" with values "Normal" for blood pressure below 120/80, "Prehypertension" for blood pressure between 120/80 and 139/89, and "Hypertension" for blood pressure 140/90 and above. 3. Frequency Analysis: - What is the distribution of gender among the patients? - How many patients have normal blood sugar levels, and how many have high blood sugar levels? 4. Correlation Analysis: - Is there any correlation between age and cholesterol levels? - Is there any correlation between BMI and diabetes status? 5. Logistic Regression: - Can you predict the likelihood of having diabetes based on age, gender, blood pressure, cholesterol levels, blood sugar levels, and BMI? 6. Charts/Graphs: - Create a histogram to visualize the distribution of cholesterol levels. - Create a bar chart to compare the average BMI based on gender. - Create a pie chart to visualize the distribution of diabetes status among the patients.
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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Transcribed Image Text:Answer the following question based on analysing the data in the program:
ID
Age
Gender Blood Pressure Cholesterol Blood Sugar BMI
Diabetes
Status
1
45
Male
130/80
210
Normal
25.5 No
2
32
Female 120/75
180
Normal
22.0
No
3
38
Male 140/85
220
High
28.3
Yes
4
27
Female
110/70
160
Normal
20.1 No
5
29
Male
125/78
200
Normal
23.8
No
6
35
Female
135/82
190
Normal
24.9 No
7 41
Male
145/88
240
High
29.7
Yes
8 45
Male
130/80
210
Normal
25.5
No
9
32
Female 120/75
180
Normal
22.0
No
10
38
Male
140/85
220
High
28.3 Yes
11
27
Female 110/70
160
Normal
20.1 No
12
29
Male
125/78
200
Normal
23.8 No
13
35
Female
135/82
190
Normal
24.9 No
14
41
Male
145/88
240
High
29.7 Yes
15
45
Male
130/80
210
Normal
25.5
No
16
32
Female
120/75
180
Normal
22.0 No
17
38
Male
140/85
220
High
28.3 Yes
18
27
Female 110/70
160
Normal
20.1
No
19
29
Male 125/78
200
Normal
23.8
No
20
45
Female 135/82
190
Normal
24.9 No
21
32
Male
145/88
240
High
29.7
Yes
22 38
Male
130/80
210
Normal
25.5
No
23
27
Female 120/75
180
Normal
22.0
No
24 29
Male 140/85
220
High
28.3
Yes
25
35
Female 135/82
190
Normal
20.1
No
1. Descriptive Statistics:
-What is the average age of the patients?
- What is the average BMI (Body Mass Index)?
- How many patients have diabetes?
2. Compute Variable:
- Create a new variable "Blood Pressure_Category" with values "Normal" for blood pressure
below 120/80, "Prehypertension" for blood pressure between 120/80 and 139/89, and
"Hypertension" for blood pressure 140/90 and above.
3. Frequency Analysis:
- What is the distribution of gender among the patients?
- How many patients have normal blood sugar levels, and how many have high blood sugar
levels?
4. Correlation Analysis:
- Is there any correlation between age and cholesterol levels?
- Is there any correlation between BMI and diabetes status?
5. Logistic Regression:
- Can you predict the likelihood of having diabetes based on age, gender, blood pressure,
cholesterol levels, blood sugar levels, and BMI?
6. Charts/Graphs:
- Create a histogram to visualize the distribution of cholesterol levels.
- Create a bar chart to compare the average BMI based on gender.
- Create a pie chart to visualize the distribution of diabetes status among the patients.
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