Information Technology and Data Driven Decision Making

Q1: Descriptive Statistics

            Descriptive statistics consist of the summary and organization of the data in order to understand them. They endeavor to characterize the data but do not try to draw conclusions from the selection to the overall population, unlike deductive statistical data. Descriptive statistics usually include a sample description of the data divided into two - central tendency measures and variability measurements (Kaur et al., 2018).

Q2: Demographic Data Points – Mean, Range, and Standard Deviation

           Mean, range, and standard of deviation are known as central tendency and spread measurements. They assume a quasi-numeric value and can be split into more acceptable, fractional, or nominal increments (SAGE Publications, 2015). In reference to the case study, age as a data point is a significant direct consequence to describe a group with a persistent condition like uncontrolled hypertension. On the other hand, the mean has an outstanding analytical quality and is often employed for quantitative modifications and analysis. Therefore, the average/arithmetic mean of the sample age in the case study calculation can help to determine the average age of the majority of patients with uncontrolled hypertension. Some of the advantages of utilizing arithmetic mean are that it is a frequently employed metric since it can be pretty conveniently estimated and interpreted. Further studies could additionally be employed in particular for algebraic measurement and numerical evaluation.  On the contrary, the disadvantages is that it does not impact the mean if data component is omitted and profoundly influenced by abnormal sequence variables (Brinks et al., 2013; Manikandan, 2011; Sykes et al., 2016).

            Range is the disparity between a distribution's maximum and minimum score. The scholar read, for example, in the case study that the patient group analyzed has three and seven years in age, then one can see how the group spreads. The computation of the range is fairly easy. An individual only need to distinguish amongst the biggest value of the data collection and the lowest number of the data The drawback of range is that it is a quite reductive metric of the data disperse since it is exceptionally susceptible to anomalies and consequently, the usefulness of a true range of data is restricted in specific respects to statistics since a solitary data point can have a significant impact on the significance of the range (Sykes et al., 2016; de Sousa Rodrigues et al., 2017).

            The standard deviation (SD) provides understanding into the variability in a set of values. It quantifies the difference from the mean of the sample and has a favorable square root of the difference. In the example study, Blood Pressure Variability (BPV) may require to be understood by uncontrolled hypertension researchers within 24 hours or weeks. Thus, standard deviation (SD) of blood pressure measurements is the most widely utilized index. BPV increases in hypertension with increased ambient stress and is closely correlated with impairment caused to the affected tissue. This has substantial effects on intervention, in which the primary objective has been in the past to reduce mean blood pressure levels. Gender as a data point can be employed in the case study to investigate the variation in blood pressure variability measures amongst men and women for a successful diagnosis (Sykes et al., 2016; Parati et al., 2018; Goh et al., 2016; Paresh et al., 2019).

Q3: Demographic Data Points – Frequencies and Percentages

            The frequency of an occurrence in statistics is the proportion of times that an experiment or research has conducted the event. These frequencies are typically shown in graphs and diagrams. Disease frequency measurements are performed to demonstrate how often a health occurrence relates to demographic size and time measurement. Information on illness prevalence and incidence rates based on demographic parameters might help predict medical costs and illness burdens for medical planning services. As in the case study, the incidence and prevalence rates of hypertension, calculating gender, ethnicity and race helps get the trends of standardized aggregate rate of incidence/prevalence that can be visually shown (Noordzij et al., 2010; Mishra et al., 2019; Liu et al., 2019).

            The percentages are one of the measurements most frequently utilized. In addition to being particularly effective in evaluations, they are beneficial to investigate a deviation from the initial value. The proportional significance of the percentage is a hundredth fraction of every measure. From the case study, age, gender and ethnicity can be used to calculate, age-, ethnicity- and sex-standardized incidence and prevalence rates of hypertension. The trends of overall standardized incidence/prevalence rates can then be calculated and presented as percentages (Lynn, 2020).

Q4: Use of Logistic Regression

            Regression of logistics is a mechanism of categorization. A binary result based on a set of autonomous variables is forecasted. In addition to the fundamental tenets, it enables the assessment of multiple contextual factors.  Logistic regression can be employed to evaluate illness understanding, management and prevention determinants in the study of health events. The advantage of logistic regression is its greater quickness compared with other controlled methods although its accuracy diminishes slightly (Hoffman, 2019). In the case study, age-standardized prevalence rates can be computed using the direct technique when detailed information is supplied. Uncontrolled hypertension understanding, diagnosis and containment can be provided as percentages, while chi square testing can be done for the analysis of demographics disparities in age gender, ethnicity and use of internet. In order to determine the effect of age, gender, ethnicity and the use of Internet information, a logistic regression might be adhered to and carried out to determine if respondents are aware of their disease or not.