Continued review of article on hypertension

Continued Review of Article on Hypertension Continued Review of Article on Hypertension In the given statistical discussion, the most notable statistics used is experimental study because of measures the variables that have a large impact on hypertension. Therefore, according to Creswell’s 2014 table, the prevalence of the disease at 43. 3 percent, thus, formulates the experimental manipulation that is important in understanding descriptive statistics. The data illustrated in 8. 3, for example, is a clear indication of summarized data through the use of sampling variation and mean to derive critical information such as 23. 1 percent from the survey conducted of 1, 239 respondents above 30 years (Creswell, 2013). Alternatively, in the blood pressure measures, the total prevalence reflected that 287 cases were a part of the socio-demographic correlates in the tackling of dependent variables such as central obesity and cardiovascular disease. It means the 250 respondents who are equivalent to 20. 2 percent are calculated using the multivariate logistic regression supported by Creswell in his tables.
On the other hand, the descriptive statistics applied amongst 45, 587 individuals spread in 11 villages and found among 7, 164 families indicate the disparity in correlation especially for testing true and false hypotheses. Additionally, in the data sets as observed in the statistical tables, analysing random variables is crucial because it will reject null data acquired during the sampling method. In the case study, for instance, an allowable error of between 15 and 95 percent was permitted because the hypothesis could not cover all the socio-demographic risk factors and variables. Overall, the rationale for using the experimental study is to help in comparing the various data sets.
Reference
Creswell, J. (2013). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Mason, OH: SAGE Publications.