Introduction The importance of statistics in clinical research can be explained by a multitude of factors; in clinical management, it is used for monitoring the patients’ conditions, the quality of health care provided, and other indicators. The correlations between length of hospital stay and costs have been of interest to researchers back in the early 2000s and continue to preoccupy them currently. One such research established the median costs of hospitalization for adult patient with viral pneumonia as $5,942, with similar hospitalization and care pattern used by different hospitals (Fine et al., 2000). If the patients were discharged a day earlier, they could have saved an average amount of $680. Another research was more focused on the methodology and data analysis than on the actual outcomes; at any rate, the researchers came to acknowledge that hospitalization costs constitute a significant part of all patients’ expenses on their health care (Polverejan, Gardiner, Bradley, Holmes-Rovner, & Rovner, 2003). A more recent research established the positive association between shorter length of stay and early readmission, with subsequent cost increase: the patients – or their payers – had to fund the initial hospitalization and the readmission. At that, the cost of hospitalization and readmission taken together was 65% higher than that of longer hospital stay in days (Carey, 2015). Despite the constant development of the field of medical sciences for the sake of common good, hospital readmissions are a persistent issue in contemporary medical care. Of course, the overwhelming majority of the patients do not want to return to the hospitals, and, to boost the medical institutions’ measure-taking activities, Medicare practices the system of penalties for the unplanned readmissions. But even though medical institutions are struggling to prevent their patients from unplanned readmissions, the statistical data for patients experiencing readmissions is rather humbling. The problem of our current interest, that is analyzing the hospitalization costs, is not any less pressing than the prevention of readmission in geriatric patients. In 2015, of the total 25,593 geriatric beneficiaries using the services of FFS Medicare, a mean 4.1% experienced at least one readmission within 30 days after the discharge (Gorina, Pratt, Kramarow, & Elgaddal, 2015). As one can see, the subject of hospital stay costs is critical in clinical research and management. The relevance of the subject is the main reason such research is needed; the findings may serve as a baseline for further investigation to create a more effective management system to reduce hospitalization costs and provide higher quality health care. Variables The variables for this analysis include length of stay in days, number of days in the intensive care unit, patient’s age, sex (male or female), and total charges during hospital stay. They can be described as follows: Table 1 – Variable list. Variable                                   Variable Type           Sex                                        Categorical dichotomous Age                                        Numerical continuous    Length of stay in days                     Numerical discrete      Number of days in the intensive care unit  Numerical discrete      Total charges during hospital stay         Numerical continuous     It is known th