Communicate findings The steps listed in Table 6.
Therefore, we try to find a possibility to assess all countries in terms of their extreme values in certain dimensions. Furthermore, we want to get a table with the numbers of "boxplot outliers" in each dimension. Therefore we computed a x 14 matrix containing logical values 0 or 1, where 1 means observation is an outlier.
Simple computations using this matrix lead among others to the number of outliers in each dimension shown in table 3.
Graphic 6, Have a look on the program code and a description of its purpose by clicking on the picture. Graphic 6 shows a barchart, where countries are classified according to the number of univariate extreme values or "boxplot outliers".
This charts yields the information that only a few countries are outliers in the boxplot sense in four dimensions, and no country has more than four extreme values. The "outlier program" that generates graphic 6 furthermore provides the option to decide in how many dimensions an observation has to be a univariate outlier to be considered as "multidimensional outlier".
These "multidimensional outliers" are then colored blue and shown together with the other observations in a stardiagram alternativlely one could also choose so called Chernoff-Flurry faces.
This facilitates the decision whether those observations are actually really different from the rest of the data. This process can be repeated several times until one finds a satisfying set of outliers that shall be excluded from the further analysis saving option available via "outlier program".
For the further analysis however, we decided not to exclude any observations, but to proceed with the whole dataset, since even four exceptionally high or low values per observation are still relatively few in comparison to a total of 13 relevant dimensions. Furthermore, the outlier starplot shows that there seem to be different groups of countries in the data that have similar characteristics reflected by the respective shape of the stars.
In case we choose to color all observations with one or more "boxplot outliers", the remaining green observations seem to have quite similar characteristics. But the number of remaining countries is very limited and does not seem to be a representative group of countries in the world.
Nevertheless, we could check the influence of excluding certain "multidimensional outliers" in the further course of our analysis. Bivariate Analysis[ edit ] Graphic 7, All variables plotted against Tuberculosis prevalence y-axis Now we would like to get a better impression of the relations between our variable of interest, namely the tuberculosis prevalence, and the other variables, which are according to our goal considered to be explanatory variables.
One possibility to visualize the relations between all variables within a dataset would be a scatterplot.
In such a graphic all variables would be plotted against each other. Since we have 13 variables of interest, this would give us a 13 x 13 display of 2-dimensional plots, which hardly allows for proper displaying on a standard computer monitor.
Additionally, we have empty spaces on the diagonal as well as doubling of the same information in the upper and lower triangular. Thus, this graphic can only be used in a proper way for up to eight variables.
Graphic 8, Have a look on the program code by clicking on the picture. Instead, we just plottet all explanatory variables against tuberculosis and display them in one window that is shown in graphic 7.
This graphic provides the required informations to derive basic assumptions about the relations within the data. First, one can see that the majority of observations seem to be distributed in a very small area, in most cases one corner of the plot, whereas only a relatively small proportion is scattered over the whole range of the diagrams.
To better visualize this, we added one dimension to plots and computed a two-dimensional density estimate of the plots. This can be seen in graphic 8which exemplary shows such a two-dimensional density estimate for "tuberculosis" and "sanitation". This further strengthens the idea mentioned in the preceding steps of our analysis.
Furthermore, there seem to be different relations between the explanatory variables and tuberculosis. These relationships are to be considered in the following steps of our analysis.
Since they may differ from subgroup to subgroup, we proceed with trying to find homogeneous groups within the countries and turn to developing hypotheses on the relationships in the section on multivariate analysis. Finding Groups[ edit ] Since we have seen many indicators for the existence of different groups, we will now try to find and interpret the groups that can be found in the data.Despite treatment advances that have transformed HIV/AIDS into a less deadly, more manageable, chronic disease, it remains highly stigmatizing and contributes to the social marginalization of those infected, thus undermining their mental and physical well-being (Logie & Gadalla, ; Ware, Wyatt & Tugenberg, ).
Discrepancies between team members’ understandings or gaps in the chain of events are fine – these point to what they need to find out in person. Next, to really nail down what happened, team members must collect more information on the event.
Determining the origin of a deadly disease is an important part of understanding and treating the disease properly.
While the origins of many diseases remain matters for debate, the following deadly outbreaks have compelling evidence of an animal origin. Based on the known impact of depression on physical and emotional well-being for HIV sufferers, the findings have important implications for those working to support those living with HIV.
The prevalence rate of depression within the sample was %, reflecting previous research (Olley, Seedat, & Stein, Olley, B. O., Seedat, S., & Stein, D. J. (). Just imagine finding out that you are positive.
How will society accept you. often a deadly sexually transmitted disease. Currently, there are different kinds of antiretroviral treatments available for patients who are tested to be HIV positive as well as patients with AIDS. - Introduction In Acquired Immune Deficiency Syndrome.
In medicine, the most deadly disease i have come across in a patient is Neurofibromatosis, its a condition where small tumor like nodules formed all over body, More than anything, the patient suffers from psychological stress due to people avoiding him /her.