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Introduction:

BMI and a way to find out whether your weight is appropriate for your height, which is your weight in kilograms divided by the square of your height in meters, increases the risk of severe health problems associated with excess weight, such as type 2 diabetes, heart disease and some types of cancers. In children, the BMI percentages indicate whether the child is at an average weight or not. (Önal, 2019).

Take into account BMI that people are of different shapes and sizes. This is why there is a range for index values ​​ considered normal for an adult of a specified height. A BMI value above the normal range indicates that a person weighs more than average for height.

· BMI under 18: If the index is lower than this limit, this means that the person may be underweight.

· BMI between 18-24: This is the normal range. And it indicates that the weight is appropriate for the length.

· BMI greater or equal to 25: In this case, the index values ​​exceed the ideal range and indicate that the person may be overweight.

· BMI greater or equal to 30: index esteems higher than 30 are named hefty. Being large expands the danger of medical conditions, for example, coronary illness, stroke, and type 2 diabetes. (Yanik & Memis, 2016)

Multivariable linear regression analysis:

It is a mathematical condition that conveys the association between two factors and is used to evaluate past characteristics and to envision future characteristics. it is used to foresee the alterations in a penniless variable affected by a couple of free factors. Various direct backslide is used to explain the association between a consistent ward variable and in any event two free factors. The independent variables can be straight or discontinuous, as its idea depends on semantic relationships that use what is known as dispersion or propagation. (Harrell, 2015)

We have been provided with some characteristics that affect BMI, and linear regression analysis will be made for them, and then the following hypotheses will be tested:

– HO The BMI is not related to the affected person traits in the Framingham Heart Study. (Null Hypothesis).

– HI The BMI is associated with the affected person characteristics in the Framingham Heart Study. (Alternative Hypothesis).

Utilizing the Excel program, the speculations were tried, and the accompanying outcomes were drawn up:

Interpretation of previous results:

The table shows the association coefficients, the direct relationship coefficient R, which was 0.3742, the coefficient of R2 approaches 0.1400.The outcomes in the variable BMI and the rest (0.891) are ascribed to different components. The vast majority of the impacts originate from other elements, and the effect of the eight factors joined is little.

The investigation of the different segments can be characterized by the logical intensity of the model all in all by the F measurement, as can be seen from the table of low critical fluctuation examination of F’s trial (P <0.00). This affirms the great informative intensity of the straight relapse model measurably.

Conclusion:

We find from the above that there is an effect of the characteristics of the body mass index. Still, it is not considered a significant impact, as there are those who have a good body mass index but smoke heavily, which is indicated by the study (Jacobs, 2018) while the study (C.g, 2019) showed a slight link to the effect of these characteristics on the body mass index, as confirmed A study that smoking and fat affect BMI, and thus does not contradict our study.

References

– Jacobs, M. (2018). Adolescent smoking: The relationship between cigarette smoking, E-cigarette smoking, and Biqa. Journal of Nutrition and Human Health. (Jacobs, 2018).

– Onal, A. E. (2019). Introductory Chapter: Life, Health, and Body Mass Index. Body-mass Index and Health. (Onal, 2019).

– Yanik, H. B., & Memis, Y. (2016). What is your body mass index? Teaching Children Mathematics. (Yanik & Memis, 2016)

– C.g, D. K. (2019). Room-air Pulse Oximetry: Effects of Smoking, Age, Gender, Blood pressure, Respiratory rate, and Body mass index. Journal of Medical Science and Clinical Research. (C.g, 2019).

– Harrell, F. E. (2015). Multivariable Modeling Strategies. Regression Modeling Strategies Springer Series in Statistics. (Harrell, 2015)