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In the interest of clarity of the cross table, it is recommended to write the factor in the cells and the target parameter in the columns. Thus, cough is the (dependent) target parameter and smoking the (independent) possible factor.
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In our example, it was important to know whether smoking influences the occurrence of cough. To simplify the interpretation, it should be made clear which variable is the target parameter. The four field table helps the reader to understand the reference values and the results. The cumulative percentage for all lines is 100% and the cumulative percentage for all columns is also 100%. You can see from this presentation that 21/29 = 72% of non-smokers do not cough but 8/29 = 28% cough. Figure 1 gives a review of types of parameters, as well as graphs to be used and statistical measures. Good basic portrayals of the descriptive statistics of medical data can be found in text books ( 4– 9). A further classification of a categorical parameter is into nominal characteristics (unordered) and ordinal characteristics (ordered according to rank). Parameters which can be classified into two or more categories are described as categorical parameters (= qualitative data).
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The gender of man cannot be measured, but is classified into two categories. One example for a metric discrete parameter is the number of erythrocytes per microliter of blood. Examples for metric continuous parameters are body height in cm, blood pressure in mmHg or the creatinine concentration in mg/L. In contrast to discrete variables, continuous variables can take any value. Data with a metric scale of measure can be further classified into continuous and discrete variables. A variable has a metric level (= quantitative data) if it can be counted, measured or weighed in a physical unit (as in cm or kg) or at least can be recorded in whole numbers. Generally two types of parameters are distinguished. The property of a parameter is specified by its so-called scale of measure. The aim of descriptive statistics is to summarize the data, so that they can be clearly illustrated ( 1– 3). Each of these parameters, also called variables, has a specific parameter value (gender = male, age = 30 years, weight = 70 kg) for each observation unit (for example the patient). Every case, for example every study participant, patient, every experimental animal, every tooth or every cell shows comparable parameters (such as body weight, gender, erosion, pH). A set of medical data is based on a collection of the data of individual cases or objects, also called observation units or statistical units.