Introduction and Review of literature:
Recent lifestyles have made people less active resulting into obesity. Long sittings, lack of exercise and junk eating are a few of the causes of obesity. Isolated systolic hypertension is a silent killer among modern day community usually following obesity. In this disorder once the systolic blood pressure crosses 140 mark on the scale, the disease happens. In addition to obesity, certain physiological conditions such as artery stiffness, heart disorders, or overactive thyroids may also be considered the culprits of the disease.
Understanding the gradual changes that occur in our life styles, which ultimately are reflected in our physical fitness we can predict the risk of developing systolic hypertension. In this study, an econometric analysis has been used to correlate the body mass index to predict the probability of disease occurrence.
Studies have been conducted in these respects where it a strong correlation between BMI and hypertension has been reported . Similarly, Resnicow et al. have studies the same relationships in school going childern and found that obesity and higher BMI contribute towards high systolic blood pressure in them.
Study Descriptions:
The data in Table 1 represents general health indicatives including age, height and weight and systolic blood pressure of subject population has been collected from a local hospital. Systolic blood pressures of 30 patients of mixed ages have been recorded at the same time.
Analysis of the data:
The data was subjected to multivariable corelation analysis using Gretl Software. Systolic blood pressure has been considered as fixed variable while age, BMI, weight and height were used as dynamic variables. General statistics of the data is presented in Table 2. The data has been tested for normality and a Q-Q plot is presented in Figure 1.
An other model had been tried but the results were insignificant hence not explained here (please refer to appendix A1).
Results:
The outcome from regression analysis is presented in Table 3. The results have represented that BMI is significantly correlated with high systolic blood pressure (F1, 28 = 10.85, p = 0.002). The high value for slope (85.019) depicts that a minor increase in BMI of the patient increases the chances of hypertension, exponentially.
Non-Technical Conclusion:
The exercise has enabled me to find out underlying relationships of major issues using proper statistical models of econometrics. I can correlate various factors and variables to predict future events.
Data on general health indicatives of random patients in a local hospital
Summary statistics of the data
Figure 1: Q-Q Plot showing data normality
Works Cited
F Tesfaye, NG Nawi, H Van Minh, P Byass, Y Berhane, R Bonita, S Wall. "Association between body mass index and blood pressure across three populations in Africa and Asia." Journal of Human Hypertension (2007): 28-37..
Resnicow K, Futterman R, Vaughan RD. "Body mass index as a predictor of systolic blood pressure in a multiracial sample of US schoolchildren." Ethinicity and Disease (1993): 351-361.
Appendix 1
Model 7: TSLS, using observations 1-30
Dependent variable: BP
Instrumented: BMI
Instruments: const Age
Coefficient std. error z p-value
Const 141.408 698.754 0.2024 0.8396
BMI 0.00242718 28.7946 8.429e-05 0.9999
Mean dependent variable 141.4667 S.D. dependent variable 13.91287
Sum squared reside 5610.195 S.E. of regression 14.15500
R-squared 0.279405 Adjusted R-squared 0.253669
F (1, 28) 7.11e-09 P-value (F) 0.999933
Log-likelihood −237.1708 Akaike criterion 478.3416
Schwarz criterion 481.1440 Hannan-Quinn 479.2381
Hausman test -
Null hypothesis: OLS estimates are consistent
Asymptotic test statistic: Chi-square (1) = 0.00968855
With p-value = 0.921591
Weak instrument test -
First-stage F-statistic (1, 28) = 0.0233625
Critical values for desired TSLS maximal size, when running
Tests at a nominal 5% significance level:
Size 10% 15% 20% 25%
Value 16.38 8.96 6.66 5.53
Maximal size may exceed 25%
