Introduction

Descriptive statistics constitute a fundamental pillar of scientific data analysis, as they provide the necessary tools for understanding and presenting the essential characteristics of a sample or a population. Through descriptive statistics, large volumes of data can be summarized into simple and comprehensible forms, such as measures of central tendency and dispersion, frequency tables, and graphical representations. In the case under examination, the file “patient_los.sav” from the SPSS software was used, which includes information about patients hospitalized with a possible myocardial infarction. Each row of data corresponds to a single patient and contains information related to age, gender, smoking habits, blood pressure, cost of hospitalization, and other characteristics. By analyzing this dataset, it becomes possible to draw useful conclusions about the sample of patients.

Analysis of Continuous Quantitative Variables

In the first phase of the analysis, the continuous quantitative variables were examined, specifically age and hospitalization cost. For these variables, several descriptive measures were calculated, including mean, median, standard deviation, range, and outliers. The results were presented both in tabular form and as histograms. Histograms with an overlaid normal curve were generated, which makes it easier to assess the distribution of the data and identify deviations. For example, if the patients’ age follows a normal distribution, the curve will appear symmetrical around the mean, whereas significant deviations may indicate heterogeneity or the presence of outliers. In the same way, the analysis of hospitalization cost can reveal whether there are large variations linked to factors such as length of stay or severity of the illness.

Analysis of Qualitative Variables

The next step involved the qualitative variables, such as age category, gender, blood pressure, and smoking status. For these variables, frequency tables were generated, which show the distribution of patients across categories. This allows one to easily determine, for example, how many patients are male and how many are female, how the sample is distributed across different age groups, what proportion presents with high blood pressure, and what percentage declare themselves as smokers. The analysis was enhanced by bar charts, which make interpretation more intuitive since visual representations help highlight the distribution and underscore important differences between groups.

Crosstabulation Tables

To better understand the relationship between two qualitative variables, crosstabulation tables (crosstabs) were created. A representative example is the analysis of the relationship between age category and gender. In this way, the distribution of males and females across different age groups became evident, making it possible to detect potential differences between genders depending on age. These tables were accompanied by clustered bar charts, which provide a more direct and comprehensible visualization of the relationship, illustrating whether men or women appear more frequently in specific age categories.

Graphs and Data Visualization

Data visualization plays an important role in descriptive analysis, as it enhances the understanding of numerical results. In this particular analysis, boxplots were created for the variable age in relation to gender. These diagrams allow the identification of the median, the range, and outliers. In this way, differences between groups can be highlighted, providing a more complete picture of the distribution. Additionally, error bar charts were created for the same variable, showing the mean age by gender along with error margins. Such visualizations contribute to a clearer understanding of data variability and allow safer comparisons between groups.

Conclusions

The analysis of data using descriptive statistics highlights the usefulness of this methodology in research. Through description and visualization, researchers are able to obtain a clear picture of the sample under study. In the case of patients with a possible myocardial infarction, the analysis of age, gender, habits, and hospitalization cost provides valuable information that can be used both for understanding the characteristics of the population and for designing further studies. The results of descriptive statistics can serve as a starting point for more advanced statistical techniques, such as inferential statistics, hypothesis testing, or regression models. Ultimately, descriptive analysis is not limited to presenting numbers but constitutes a necessary step for interpreting data, drawing conclusions, and making evidence-based decisions in practice.