Confidence Interval Estimates
Critical Thinking Assignment
Using the
Framingham Heart Study dataset
provided, perform the ANOVA multivariable linear regression analysis using BMI as a continuous variable. Before conducting the analysis, be sure that all participants have complete data on all analysis variables.
Describe how each characteristic is related to BMI. Are crude and multivariable effects similar? What might explain or account for any differences?
H0 The BMI is not related to the patient characteristics in the Framingham Heart Study. (Null Hypothesis)
H1 The BMI is related to the patient characteristics in the Framingham Heart Study. (Alternative Hypothesis)
Upload both Excel sheet into R Studio. (Refer to Chapters 7 & 12 in Introductory Statistics with R or pages 111–122 in EXCEL Statistics A Quick Guide). Exclude participants with missing data on analysis variables (age, sex, systolic blood pressure, total serum cholesterol, current smoker, and diabetes = cleaning the data). Conduct the simple linear regression (ANOVA) by using the Excel Regression tool in the Data Analysis Toolpak.
Remember SEX is coded 1=male and 2=female.
Present your findings in a Word document by copying and pasting the ANOVA table into the document. Your paper must be written with a title page, an introduction, a discussion where you interpret the meaning of the ANOVA test, and a conclusion should be included. Your submission should be 2–3 pages to discuss and display your findings.
Provide support for your statements with in–text citations from a minimum of two scholarly, peer–reviewed articles. One of these sources may be from the class readings, textbook, or lectures, but the others must be external. The Saudi Digital Library is a good place to find these sources and should be your primary resource for conducting research.
Follow APA and Saudi Electronic University writing standards.
Review the grading rubric to see how you will be graded for this assignment.
You are strongly encouraged to submit all assignments to the TurnItIn Originality Check prior to submitting them to your instructor for grading.
HCM-506-24410-202020 – (Current Semester – الفصل الحالي)HCM-506: Applied Biostatistics in Healt 24410-Riyadh-Males
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MASAUD ALYAMI
on Fri, Feb 26 2021, 7:42 PM
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Submission ID: 6802f609-2f75-4432-bb82-345a7758ad6a
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MultivariateAnalysisofBodyMassIndex. x
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Running header:
1
MULTIVARIATE ANALYSIS 1
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MULTIVARIATE ANALYSIS 1
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MULTIVARIATE ANALYSIS 1
MULTIVARIATE ANALYSIS 7
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MULTIVARIATE ANALYSIS 7
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MULTIVARIATE ANALYSIS 7
2
Applied Biostatistics in Health
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Applied Biostatistics in Health
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Applied Biostatistics in Health
Multivariate Analysis of Body Mass Index HCM-506 Masaud Alyami S199632635 February, 2021
Introduction
1
Body Mass Index (BMI) is an attribute calculated through the aspects of mass and height of a person.
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Body Mass Index (BMI) is an attribute calculated through the aspects of mass and height of a person
Source – Another student’s paper
Body Mass Index (BMI) is an attribute calculated through the aspects of mass and height of a person
The body mass is usually divided by the body height square, and its unit is expressed in kg/m2.
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
The body mass is usually divided by the body height square, and its unit is expressed in kg/m2
Source – Another student’s paper
The body mass is usually divided by the body height square, and its unit is expressed in kg/m2
BMI values of below 20 and above 25 are attributed to high cases of mortality.
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BMI values of below 20 and above 25 are attributed to high cases of mortality
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BMI values of below 20 and above 25 are attributed to high cases of mortality
Thereby, the optimal range of Body Mass Index ranges from 20-25.
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Thereby, the optimal range of Body Mass Index ranges from 20-25
Source – Another student’s paper
Thereby, the optimal range of Body Mass Index ranges from 20-25
The tissue mass is a factor that is used to determine the overweight and underweight of a person.
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
The tissue mass is a factor that is used to determine the overweight and underweight of a person
Source – Another student’s paper
The tissue mass is a factor that is used to determine the overweight and underweight of a person
There are a variety of factors that are used to determine the bodyweight of individuals.
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
There are a variety of factors that are used to determine the bodyweight of individuals
Source – Another student’s paper
There are a variety of factors that are used to determine the bodyweight of individuals
These factors include age, diabetes, sex, and ethnicity (Emel Önal, 2019).
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
These factors include age, diabetes, sex, and ethnicity (Emel Önal, 2019)
Source – Another student’s paper
These factors include age, diabetes, sex, and ethnicity (Emel Önal, 2019)
However, in this paper, we are going to use the Framingham Heart Study dataset to perform an ANOVA multivariable regression analysis with the use of BMI as a continuous variable.
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
However, in this paper, we are going to use the Framingham Heart Study dataset to perform an ANOVA multivariable regression analysis with the use of BMI as a continuous variable
Source – Another student’s paper
However, in this paper, we are going to use the Framingham Heart Study dataset to perform an ANOVA multivariable regression analysis with the use of BMI as a continuous variable
The results that will be obtained will determine the factors that affect BMI in an individual.
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
The results that will be obtained will determine the factors that affect BMI in an individual
Source – Another student’s paper
The results that will be obtained will determine the factors that affect BMI in an individual
The null and alternate hypothesis of the study is described below.
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
The null and alternate hypothesis of the study is described below
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The null and alternate hypothesis of the study is described below
· H0 the BMI is not related to the patient characteristics in the Framingham Heart Study.
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
· H0 the BMI is not related to the patient characteristics in the Framingham Heart Study
Source – Another student’s paper
· H0 the BMI is not related to the patient characteristics in the Framingham Heart Study
(Null Hypothesis) · H1 the BMI is related to the patient characteristics in the Framingham Heart Study.
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
(Null Hypothesis) · H1 the BMI is related to the patient characteristics in the Framingham Heart Study
Source – Another student’s paper
(Null Hypothesis) · H1 the BMI is related to the patient characteristics in the Framingham Heart Study
(Alternative Hypothesis) Findings of the Study After analyzing how different concepts relate to Body Mass Index using multivariate regression, the following results were obtained:
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
(Alternative Hypothesis) Findings of the Study After analyzing how different concepts relate to Body Mass Index using multivariate regression, the following results were obtained
Source – Another student’s paper
(Alternative Hypothesis) Findings of the Study After analyzing how different concepts relate to Body Mass Index using multivariate regression, the following results were obtained
Regression Statistics
1
Multiple R 0.05810995
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Multiple R 0.05810995
Source – Another student’s paper
Multiple R 0.05810995
R Square 0.00337677
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
R Square 0.00337677
Source – Another student’s paper
R Square 0.00337677
Adjusted R Square 0.00327301
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Adjusted R Square 0.00327301
Source – Another student’s paper
Adjusted R Square 0.00327301
Standard Error 4.0303178
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Standard Error 4.0303178
Source – Another student’s paper
Standard Error 4.0303178
Observations 9607
ANOVA
1
Df SS MS F Significance F
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Df SS MS F Significance F
Source – Another student’s paper
df SS MS F Significance F
Regression 1 528.62284 528.62284 32.5437307 1.1999E-08
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Regression 1 528.62284 528.62284 32.5437307 1.1999E-08
Source – Another student’s paper
Regression 1 528.62284 528.62284 32.5437307 1.1999E-08
Residual 9605 156018.449 16.2434616
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Residual 9605 156018.449 16.2434616
Source – Another student’s paper
Residual 9605 156018.449 16.2434616
Total 9606 156547.071
Suspected Entry: 100% match
Uploaded – MultivariateAnalysisofBodyMassIndex. x
Total 9606 156547.071
Source – Another student’s paper
Total 9606 156547.071
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Source – Another student’s paper
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 24.490722 0.23904879 102.450726 0 24.022136 24.9593081 24.022136 24.9593081
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Intercept 24.490722 0.23904879 102.450726 0 24.022136 24.9593081 24.022136 24.9593081
Source – Another student’s paper
Intercept 24.490722 0.23904879 102.450726 0 24.022136 24.9593081 24.022136 24.9593081
AGE 0.02472912 0.00433486 5.70471128 1.1999E-08 0.01623188 0.03322636 0.01623188 0.03322636
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AGE 0.02472912 0.00433486 5.70471128 1.1999E-08 0.01623188 0.03322636 0.01623188 0.03322636
Source – Another student’s paper
AGE 0.02472912 0.00433486 5.70471128 1.1999E-08 0.01623188 0.03322636 0.01623188 0.03322636
The p-value of the analysis between Body Mass Index and age is 1.1999E-08.
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
The p-value of the analysis between Body Mass Index and age is 1.1999E-08
Source – Another student’s paper
The p-value of the analysis between Body Mass Index and age is 1.1999E-08
The p-value is less than 0.05, thereby the null hypothesis is rejected, and the alternate hypothesis is accepted.
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
The p-value is less than 0.05, thereby the null hypothesis is rejected, and the alternate hypothesis is accepted
Source – Another student’s paper
The p-value is less than 0.05, thereby the null hypothesis is rejected, and the alternate hypothesis is accepted
From this analysis, the BMI is related to the age of patients in the Framingham Heart Study dataset.
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
From this analysis, the BMI is related to the age of patients in the Framingham Heart Study dataset
Source – Another student’s paper
From this analysis, the BMI is related to the age of patients in the Framingham Heart Study dataset
Older people tend to have an increased BMI in comparison to younger individuals.
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Older people tend to have an increased BMI in comparison to younger individuals
Source – Another student’s paper
Older people tend to have an increased BMI in comparison to younger individuals
At old age, people tend to be less active, making them increase their body muscles.
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
At old age, people tend to be less active, making them increase their body muscles
Source – Another student’s paper
At old age, people tend to be less active, making them increase their body muscles
Regression Statistics
1
Multiple R 0.26630323
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Multiple R 0.26630323
Source – Another student’s paper
Multiple R 0.26630323
R Square 0.07091741
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
R Square 0.07091741
Source – Another student’s paper
R Square 0.07091741
Adjusted R Square 0.07082068
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Adjusted R Square 0.07082068
Source – Another student’s paper
Adjusted R Square 0.07082068
Standard Error 3.89135587
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Standard Error 3.89135587
Source – Another student’s paper
Standard Error 3.89135587
Observations 9607
ANOVA
1
Df SS MS F Significance F
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Df SS MS F Significance F
Source – Another student’s paper
df SS MS F Significance F
Regression 1 11101.9133 11101.9133 733.155219 1.163E-155
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Regression 1 11101.9133 11101.9133 733.155219 1.163E-155
Source – Another student’s paper
Regression 1 11101.9133 11101.9133 733.155219 1.163E-155
Residual 9605 145445.158 15.1426505
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Residual 9605 145445.158 15.1426505
Source – Another student’s paper
Residual 9605 145445.158 15.1426505
Total 9606 156547.071
Suspected Entry: 100% match
Uploaded – MultivariateAnalysisofBodyMassIndex. x
Total 9606 156547.071
Source – Another student’s paper
Total 9606 156547.071
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Source – Another student’s paper
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 19.3693733 0.24203325 80.0277387 0 18.8949371 19.8438095 18.8949371 19.8438095
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Intercept 19.3693733 0.24203325 80.0277387 0 18.8949371 19.8438095 18.8949371 19.8438095
Source – Another student’s paper
Intercept 19.3693733 0.24203325 80.0277387 0 18.8949371 19.8438095 18.8949371 19.8438095
SYSBP 0.04761088 0.00175836 27.0768392 1.163E-155 0.04416412 0.05105764 0.04416412 0.05105764
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SYSBP 0.04761088 0.00175836 27.0768392 1.163E-155 0.04416412 0.05105764 0.04416412 0.05105764
Source – Another student’s paper
SYSBP 0.04761088 0.00175836 27.0768392 1.163E-155 0.04416412 0.05105764 0.04416412 0.05105764
In the second regression analysis, BMI is compared to systolic blood pressure, and the p-value is ascertained to be 1.163E-155, which is below 0.05.
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
In the second regression analysis, BMI is compared to systolic blood pressure, and the p-value is ascertained to be 1.163E-155, which is below 0.05
Source – Another student’s paper
In the second regression analysis, BMI is compared to systolic blood pressure, and the p-value is ascertained to be 1.163E-155, which is below 0.05
Thus, the null hypothesis is rejected, and the alternate hypothesis is accepted.
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Thus, the null hypothesis is rejected, and the alternate hypothesis is accepted
Source – Another student’s paper
Thus, the null hypothesis is rejected, and the alternate hypothesis is accepted
Systolic blood pressure is related to BMI according to the provided dataset.
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Systolic blood pressure is related to BMI according to the provided dataset
Source – Another student’s paper
Systolic blood pressure is related to BMI according to the provided dataset
An increase in Body Mass Index also has a positive impact on the systolic blood pressure.
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
An increase in Body Mass Index also has a positive impact on the systolic blood pressure
Source – Another student’s paper
An increase in Body Mass Index also has a positive impact on the systolic blood pressure
SUMMARY OUTPUT
Regression Statistics
1
Multiple R 0.16159796
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Multiple R 0.16159796
Source – Another student’s paper
Multiple R 0.16159796
R Square 0.0261139
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
R Square 0.0261139
Source – Another student’s paper
R Square 0.0261139
Adjusted R Square 0.02601251
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Adjusted R Square 0.02601251
Source – Another student’s paper
Adjusted R Square 0.02601251
Standard Error 3.98407837
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Standard Error 3.98407837
Source – Another student’s paper
Standard Error 3.98407837
Observations 9607
ANOVA
1
Df SS MS F Significance F
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Df SS MS F Significance F
Source – Another student’s paper
df SS MS F Significance F
Regression 1 4088.0545 4088.0545 257.54963 3.2438E-57
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Regression 1 4088.0545 4088.0545 257.54963 3.2438E-57
Source – Another student’s paper
Regression 1 4088.0545 4088.0545 257.54963 3.2438E-57
Residual 9605 152459.017 15.8728805
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Residual 9605 152459.017 15.8728805
Source – Another student’s paper
Residual 9605 152459.017 15.8728805
Total 9606 156547.071
Suspected Entry: 100% match
Uploaded – MultivariateAnalysisofBodyMassIndex. x
Total 9606 156547.071
Source – Another student’s paper
Total 9606 156547.071
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Source – Another student’s paper
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 26.4067177 0.05408639 488.232228 0 26.3006969 26.5127384 26.3006969 26.5127384
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Intercept 26.4067177 0.05408639 488.232228 0 26.3006969 26.5127384 26.3006969 26.5127384
Source – Another student’s paper
Intercept 26.4067177 0.05408639 488.232228 0 26.3006969 26.5127384 26.3006969 26.5127384
CURSMOKE -1.3157466 0.08198639 -16.048353 3.2438E-57 -1.4764572 -1.155036 -1.4764572 -1.155036
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CURSMOKE -1.3157466 0.08198639 -16.048353 3.2438E-57 -1.4764572 -1.155036 -1.4764572 -1.155036
Source – Another student’s paper
CURSMOKE -1.3157466 0.08198639 -16.048353 3.2438E-57 -1.4764572 -1.155036 -1.4764572 -1.155036
In the third regression analysis, the Body Mass Index of patients is compared to the smoking patterns in the dataset’s data.
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
In the third regression analysis, the Body Mass Index of patients is compared to the smoking patterns in the dataset’s data
Source – Another student’s paper
In the third regression analysis, the Body Mass Index of patients is compared to the smoking patterns in the dataset’s data
From the analysis, the p-value was determined to be 3.2438E-57, which is below 0.05;
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
From the analysis, the p-value was determined to be 3.2438E-57, which is below 0.05
Source – Another student’s paper
From the analysis, the p-value was determined to be 3.2438E-57, which is below 0.05
thereby, the null hypothesis is rejected.
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
thereby, the null hypothesis is rejected
Source – Another student’s paper
thereby, the null hypothesis is rejected
Therefore, the smoking rate of an individual can be related to their Body Mass Index.
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Therefore, the smoking rate of an individual can be related to their Body Mass Index
Source – Another student’s paper
Therefore, the smoking rate of an individual can be related to their Body Mass Index
Smoking lowers the BMI of individuals by reducing their general body weight.
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Smoking lowers the BMI of individuals by reducing their general body weight
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Smoking lowers the BMI of individuals by reducing their general body weight
Smokers have a decreased appetite making them not improve on their bodies.
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Smokers have a decreased appetite making them not improve on their bodies
Source – Another student’s paper
Smokers have a decreased appetite making them not improve on their bodies
SUMMARY OUTPUT
Regression Statistics
1
Multiple R 0.08598698
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Multiple R 0.08598698
Source – Another student’s paper
Multiple R 0.08598698
R Square 0.00739376
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
R Square 0.00739376
Source – Another student’s paper
R Square 0.00739376
Adjusted R Square 0.00729042
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Adjusted R Square 0.00729042
Source – Another student’s paper
Adjusted R Square 0.00729042
Standard Error 4.02218729
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Standard Error 4.02218729
Source – Another student’s paper
Standard Error 4.02218729
Observations 9607
ANOVA
1
Df SS MS F Significance F
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Df SS MS F Significance F
Source – Another student’s paper
df SS MS F Significance F
Regression 1 1157.47172 1157.47172 71.5460746 3.104E-17
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Regression 1 1157.47172 1157.47172 71.5460746 3.104E-17
Source – Another student’s paper
Regression 1 1157.47172 1157.47172 71.5460746 3.104E-17
Residual 9605 155389.6 16.1779906
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Residual 9605 155389.6 16.1779906
Source – Another student’s paper
Residual 9605 155389.6 16.1779906
Total 9606 156547.071
Suspected Entry: 100% match
Uploaded – MultivariateAnalysisofBodyMassIndex. x
Total 9606 156547.071
Source – Another student’s paper
Total 9606 156547.071
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Source – Another student’s paper
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 25.7611868 0.04193192 614.357399 0 25.6789914 25.8433822 25.6789914 25.8433822
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Intercept 25.7611868 0.04193192 614.357399 0 25.6789914 25.8433822 25.6789914 25.8433822
Source – Another student’s paper
Intercept 25.7611868 0.04193192 614.357399 0 25.6789914 25.8433822 25.6789914 25.8433822
DIABETES 1.72531564 0.20397439 8.45849127 3.104E-17 1.32548278 2.12514849 1.32548278 2.12514849
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DIABETES 1.72531564 0.20397439 8.45849127 3.104E-17 1.32548278 2.12514849 1.32548278 2.12514849
Source – Another student’s paper
DIABETES 1.72531564 0.20397439 8.45849127 3.104E-17 1.32548278 2.12514849 1.32548278 2.12514849
Lastly, diabetes is another aspect that is compared in the multivariate regression analysis.
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Lastly, diabetes is another aspect that is compared in the multivariate regression analysis
Source – Another student’s paper
Lastly, diabetes is another aspect that is compared in the multivariate regression analysis
After analyzing the data, it was established that the p-value was 3.104E-17, which is below 0.05, and hence the alternate hypothesis was accepted.
Suspected Entry: 100% match
Uploaded – MultivariateAnalysisofBodyMassIndex. x
After analyzing the data, it was established that the p-value was 3.104E-17, which is below 0.05, and hence the alternate hypothesis was accepted
Source – Another student’s paper
After analyzing the data, it was established that the p-value was 3.104E-17, which is below 0.05, and hence the alternate hypothesis was accepted
Diabetes is a factor that relates to the Body Mass Index of patients (“Incorrect body mass index range in:
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Diabetes is a factor that relates to the Body Mass Index of patients (“Incorrect body mass index range in
Source – Another student’s paper
Diabetes is a factor that relates to the Body Mass Index of patients (“Incorrect body mass index range in
Does body mass index adequately convey a patient’s mortality risk?”
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
Does body mass index adequately convey a patient’s mortality risk?”
Source – Another student’s paper
Does body mass index adequately convey a patient’s mortality risk?”
2017).
1
An increase in the body mass index tends to make individuals highly susceptible to diabetes.
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Uploaded – MultivariateAnalysisofBodyMassIndex. x
An increase in the body mass index tends to make individuals highly susceptible to diabetes
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An increase in the body mass index tends to make individuals highly susceptible to diabetes
It is attributed to the increasing levels of sugar in the blood attributed to increased body weight.
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It is attributed to the increasing levels of sugar in the blood attributed to increased body weight
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Conclusion In conclusion, the Body Mass Index is an attribute related to the weight and height of an individual.
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Conclusion In conclusion, the Body Mass Index is an attribute related to the weight and height of an individual
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Conclusion In conclusion, the Body Mass Index is an attribute related to the weight and height of an individual
BMI is a factor in human beings that is determined by a variety of factors that are prevalent in the human body.
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BMI is a factor in human beings that is determined by a variety of factors that are prevalent in the human body
According to the multivariate analysis that was done on the Framingham Heart Study dataset, diabetes, systolic blood pressure, age, and smoking rate were determined to be the factors that are related to BMI.
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These factors had a p-value of less than 0.05 in the multivariate regression that was conducted.
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Thereby, the alternate hypothesis is accepted in this analysis, which states that BMI is related to the patient characteristics in the Framingham Heart Study.
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References
1
Emel Önal, A.
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Emel Önal, A
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Emel Önal, A
(2019). .
1
Body-mass Index and Health.
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Body-mass Index and Health
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Body-mass Index and Health
DOI:10.5772/intechopen.82142
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Incorrect body mass index range in:
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Does body mass index adequately convey a patient’s mortality risk?
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Does body mass index adequately convey a patient’s mortality risk
(2017).
1
JAMA, 309(5), 442.
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JAMA, 309(5), 442
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JAMA, 309(5), 442
doi:10.1001/jama.2013.15
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%100Running header: MULTIVARIATE ANALYSIS 1
MULTIVARIATE ANALYSIS 7
Applied Biostatistics in Health
Multivariate Analysis of Body Mass Index HCM-506 Masaud Alyami S199632635 February, 2021
Introduction
Body Mass Index (BMI) is an attribute calculated through the aspects of mass and height of a person.
The body mass is usually divided by the body height square, and its unit is expressed in kg/m2. BMI values
of below 20 and above 25 are attributed to high cases of mortality. Thereby, the optimal range of Body
Mass Index ranges from 20-25. The tissue mass is a factor that is used to determine the overweight and un-
derweight of a person. There are a variety of factors that are used to determine the bodyweight of
individuals. These factors include age, diabetes, sex, and ethnicity (Emel Önal, 2019). However, in this paper,
we are going to use the Framingham Heart Study dataset to perform an ANOVA multivariable regression
analysis with the use of BMI as a continuous variable. The results that will be obtained will determine the
factors that affect BMI in an individual. The null and alternate hypothesis of the study is described below. ·
H0 the BMI is not related to the patient characteristics in the Framingham Heart Study. (Null Hypothesis) ·
H1 the BMI is related to the patient characteristics in the Framingham Heart Study. (Alternative Hypothesis)
Findings of the Study After analyzing how different concepts relate to Body Mass Index using multivariate
regression, the following results were obtained:
Regression Statistics
Multiple R 0.05810995
R Square 0.00337677
Adjusted R Square 0.00327301
Standard Error 4.0303178
Observations 9607
ANOVA
Df SS MS F Significance F
Regression 1 528.62284 528.62284 32.5437307 1.1999E-08
Residual 9605 156018.449 16.2434616
Total 9606 156547.071
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 24.490722 0.23904879 102.450726 0 24.022136 24.9593081 24.022136 24.9593081
AGE 0.02472912 0.00433486 5.70471128 1.1999E-08 0.01623188 0.03322636 0.01623188 0.03322636
The p-value of the analysis between Body Mass Index and age is 1.1999E-08. The p-value is less than 0.05,
thereby the null hypothesis is rejected, and the alternate hypothesis is accepted. From this analysis, the BMI
is related to the age of patients in the Framingham Heart Study dataset. Older people tend to have an in-
creased BMI in comparison to younger individuals. At old age, people tend to be less active, making them
increase their body muscles. Regression Statistics
Multiple R 0.26630323
R Square 0.07091741
Adjusted R Square 0.07082068
Standard Error 3.89135587
Observations 9607
ANOVA
1
2
1
1
1
1
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Df SS MS F Significance F
Regression 1 11101.9133 11101.9133 733.155219 1.163E-155
Residual 9605 145445.158 15.1426505
Total 9606 156547.071
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 19.3693733 0.24203325 80.0277387 0 18.8949371 19.8438095 18.8949371 19.8438095
SYSBP 0.04761088 0.00175836 27.0768392 1.163E-155 0.04416412 0.05105764 0.04416412 0.05105764
In the second regression analysis, BMI is compared to systolic blood pressure, and the p-value is ascer-
tained to be 1.163E-155, which is below 0.05. Thus, the null hypothesis is rejected, and the alternate hypo-
thesis is accepted. Systolic blood pressure is related to BMI according to the provided dataset. An increase
in Body Mass Index also has a positive impact on the systolic blood pressure. SUMMARY OUTPUT
Regression Statistics
Multiple R 0.16159796
R Square 0.0261139
Adjusted R Square 0.02601251
Standard Error 3.98407837
Observations 9607
ANOVA
Df SS MS F Significance F
Regression 1 4088.0545 4088.0545 257.54963 3.2438E-57
Residual 9605 152459.017 15.8728805
Total 9606 156547.071
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 26.4067177 0.05408639 488.232228 0 26.3006969 26.5127384 26.3006969 26.5127384
CURSMOKE -1.3157466 0.08198639 -16.048353 3.2438E-57 -1.4764572 -1.155036 -1.4764572 -1.155036
In the third regression analysis, the Body Mass Index of patients is compared to the smoking patterns in the
dataset’s data. From the analysis, the p-value was determined to be 3.2438E-57, which is below 0.05;
thereby, the null hypothesis is rejected. Therefore, the smoking rate of an individual can be related to their
Body Mass Index. Smoking lowers the BMI of individuals by reducing their general body weight. Smokers
have a decreased appetite making them not improve on their bodies. SUMMARY OUTPUT
Regression Statistics
Multiple R 0.08598698
R Square 0.00739376
Adjusted R Square 0.00729042
Standard Error 4.02218729
Observations 9607
ANOVA
Df SS MS F Significance F
Regression 1 1157.47172 1157.47172 71.5460746 3.104E-17
Residual 9605 155389.6 16.1779906
Total 9606 156547.071
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 25.7611868 0.04193192 614.357399 0 25.6789914 25.8433822 25.6789914 25.8433822
1
1
1
1
1
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DIABETES 1.72531564 0.20397439 8.45849127 3.104E-17 1.32548278 2.12514849 1.32548278 2.12514849
Lastly, diabetes is another aspect that is compared in the multivariate regression analysis.
After analyzing the data, it was established that the p-value was 3.104E-17, which is below 0.05, and
hence the alternate hypothesis was accepted. Diabetes is a factor that relates to the Body Mass Index of pa-
tients (“Incorrect body mass index range in: Does body mass index adequately convey a patient’s mortality
risk?” 2017). An increase in the body mass index tends to make individuals highly susceptible to
diabetes. It is attributed to the increasing levels of sugar in the blood attributed to increased body weight.
Conclusion In conclusion, the Body Mass Index is an attribute related to the weight and height of an
individual. BMI is a factor in human beings that is determined by a variety of factors that are prevalent in
the human body. According to the multivariate analysis that was done on the Framingham Heart Study
dataset, diabetes, systolic blood pressure, age, and smoking rate were determined to be the factors that are
related to BMI. These factors had a p-value of less than 0.05 in the multivariate regression that was
conducted. Thereby, the alternate hypothesis is accepted in this analysis, which states that BMI is related to
the patient characteristics in the Framingham Heart Study.
References
Emel Önal, A. (2019). . Body-mass Index and Health. DOI:10.5772/intechopen.82142
Incorrect body mass index range in: Does body mass index adequately convey a patient’s mortality risk?
(2017). JAMA, 309(5), 442. doi:10.1001/jama.2013.15
1
1
1 1
1
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1,146 02/26/21 0d508fb0-9d68-f236-abf6-d144bbdf6f6b 57284055-607e-4b8e-7369-e8217ba2d0e8
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HCM-506-24410-202020 – (CURRENT SEMESTER – الفصل الحالي)HCM-506: APPLIED BIOSTATISTICS IN HEALT 24410-RIYADH-
MALES
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Citations (3/3)
Running header: 1 FRAMINGHAM HEART STUDY DATASET 1
FRAMINGHAM HEART STUDY DATASET 3
Applied Biostatistics in Health Framingham Heart Study dataset HCM-506 Masaud
Alyami S199632635 February, 2021
1 FRAMINGHAM HEART STUDY DATASET
INTRODUCTION BODY MASS INDEX (BMI) IS A CHARACTERISTIC THAT
IS CALCULATED FROM THE MASS AND HEIGHT UNITS OF AN
INDIVIDUAL. THE BMI CAN ALSO BE DEFINED AS MASS OF THE BODY
DIVIDED BY ITS SQUARE ROOT HEIGHT AND IS USUALLY EXPRESSED IN
Word Count: ٧٣٩
Attachment ID: 4061286277
FraminghamHeartStudydatase…
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2 Another student’s paper
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UNITS OF KG/M2. THROUGH THE BMI RESULTS INDIVIDUALS ARE
CATEGORIZED AS OF NORMAL WEIGHT, NORMAL WEIGHT,
OVERWEIGHT, AND OBESE. With the rapid evolution of lifestyle diseases BMI is an
issue of concern in the current society. 1 THE BMIS LIMITS OF OVER 25 AND
UNDER 20 ARE CHARACTERIZED TO BE UNHEALTHY WHICH LEADS TO
HIGH MORTALITY IN THE CURRENT SOCIETY (MARYCLAIRE, 2020).
Different factors in our lifestyles contribute to the low or high recordings of BMI. There i
a need to understand the factors affecting BMI for us not to be victims of the extreme in
the society. 1 THEREFORE, THIS PAPER WILL CRITICALLY ANALYZE
RELATIONSHIP OF BMI TO PATIENTS’ CHARACTERISTICS ACCORDING
TO FRAMINGHAM HEART STUDY DATASET.
Hypothesis To analyze the dataset effectively the following hypothesis will be tested.
H0: 2 THE BMI IS NOT RELATED TO THE PATIENT CHARACTERISTICS IN
THE FRAMINGHAM HEART STUDY. 3 (NULL HYPOTHESIS) H1: 2 THE
BMI IS RELATED TO THE PATIENT CHARACTERISTICS IN THE
FRAMINGHAM HEART STUDY. (Alternative Hypothesis)
1 RESULTS OF ANOVA MULTIVARIATE ANALYSIS COLUMN1
COEFFICIENTS P-VALUE
INTERCEPT 17.2916618 2.39372E-98
MALE 0.812985908 3.21703E-09
AGE -0.002452255 0.773380806
EDUCATION -0.439252556 3.21113E-13
CURRENTSMOKER -1.270311854 5.88893E-11
CIGSPERDAY 0.009764184 0.244255549
BPMEDS 0.445921597 0.249309362
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PREVALENTSTROKE -2.093615903 0.010727051
PREVALENTHYP 0.829803384 1.58106E-05
DIABETES 0.878390358 0.04649781
TOTCHOL 0.003514526 0.005420187
SYSBP -0.00422398 0.439165117
DIABP 0.104998289 4.20067E-32
HEARTRATE 0.002887996 0.590457538
GLUCOSE 0.001872462 0.382194082
TENYEARCHD -0.344122152 0.058854909
Discussion
1 THE FIRST ASPECT WAS THE GENDER OF THE PATIENTS THAT
RECORDED A P-VALUE OF 3.21703 E-09. COMPARING THE P-VALUE TO
THE REQUIRED STANDARDS IT IS LESS THAN 0.05 THEREBY WE ACCEPT
THE NULL HYPOTHESIS AND REJECT THE NULL HYPOTHESIS.
THEREBY, BMI I NOT RELATED TO THE GENDER CHARACTERISTIC OF
THE PATIENTS.
AGE IS ALSO ATTRIBUTE THAT HAD A P-VALUE OF 0.773380806 WHICH
IS MORE THAN 0.05. FROM THESE RESULTS WE REJECT THE NULL
HYPOTHESIS WAS REJECTED AND ALTERNATE HYPOTHESIS ACCEPTED
THEREBY, THE BMI OF THE PATIENTS IS RELATED TO THEIR AGE.
EDUCATION RECORDED A P-VALUE OF 3.21113E-13 WHICH IS LESS THAN
0.05. FROM THE ANALYSIS OF THE P-VALUE THERE IS NO
RELATIONSHIP OF THE BMI WITH THE EDUCATION OF THE PATIENTS.
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MOREOVER, THE RATE OF SMOKING, STROKE, HYPERTENSION,
DIABETES, CHOLESTEROL, AND TEN YEAR CHILD ARE ATTRIBUTES
THAT HAVE A LOWER P-VALUE AS COMPARED TO 0.05. FROM THE
ABOVE STATISTICS THE ABOVE CHARACTERISTICS DO NOT HAVE A
RELATIONSHIP WITH BMI OF PATIENTS.
HOWEVER, THERE ARE SOME ASPECTS THAT INCLUDE CIGARETTES
PER DAY, BLOOD PRESSURE, SYSTOLIC BLOOD PRESSURE, DIASTOLIC
BLOOD PRESSURE, HEART RATE, GLUCOSE LEVEL, AND TEN YEAR
CHILD ARE ATTRIBUTES THAT HAVE A P-VALUE GREATER THAN 0.05.
THEREBY, PATIENT WITH THESE ATTRIBUTES HAVE A RELATIONSHIP
WITH THEIR BMI.
MULTIVARIATE AND CRUDE IMPLICATIONS ARE NOT RELATED
ACCORDING TO THE DATASET PROVIDED. ONE OF THE CONTRIBUTING
FACTORS TO THE DIFFERENCES IS THE STATISTICAL TOOL THAT WAS
ADOPTED IN THE ANALYSIS. THE TOOL BRINGS OUT THE DIFFERENCE
BETWEEN THE TWO EFFECTS (HEINRICH ET AL., 2016). Additionally, there
are some as that are not related with the BMI and the aspects making the variations to be
evident. 1 THEREFORE, THESE ARE SOME OF THE REASONS THAT ARE
LEADING TO THE VARIATION.
Conclusion In conclusion, it has been noted that not all characteristics are related to the
BMI. The following attributes were found to have a relationship with the BMI: age,
cigarettes per day, blood pressure, heart rate, and glucose levels. However, the aspects
that are not related with BMI are gender, education, smoking, stroke, hypertension, and
cholesterol levels.
References
Heinrich, S., Kowalsky, U., & Dinkler, D. (2016). Multiscale damage analyis for steel
structures. PAMM, 16(1), 133-134. https://doi.org/10.1002/pamm.201610055
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Maryclaire, N. (2020). WHO categories of manual BMI, standard scan BMI, and straight
scan BMI. https://doi.org/10.7717/peerj.8095/table-4