# A Method of Accuracy Increment Using Segmented Regression
## 2 Literature Review and Statement of the Problem  
Mathematical model building aims at decreasing the uncertainty level for the objects being studied [14; 15]. The analysis of the level, location, and nature of uncertainty helps to obtain more reliable information and adequate knowledge [16; 17]. To build mathematical models, researchers use methods from different sciences, such as mathematical analysis, probability theory, data science, regression analysis, mathematical statistics, recognition theory, applied geometry, and others [18]. This article concentrates on the techniques of regression analysis for mathematical model building, so corresponding methods are considered in detail. Regression analysis is used to determine the relationship between two or more variables [19] and is widely used to fit mathematical models to statistical data [20]. Regression analysis is frequently used in various applications due to its approximate ease of calculation, high accuracy, and good predictive properties, depending on the approximating function type usage. Regression analysis is applied to different fields in different capacities, for example, in:  
1. Medicine: to detect [PERSON]'s disease based on the analysis of finger-tapping data [21], to forecast the uptake of oxygen based on genes evaluation and to predict data on patient admission [22], and others;
2. Econometrics: to predict the audit opinion using six financial indicators [23], to determine the dependence of economic growth on the level of environmental pollution [24], to describe the trends of economical parameters in correlation with various factors [25; 26], and others;
3. Transport systems: to determine the optimal periodicity of the implementation of operation processes [27; 28], and to analyze possible routes and traffic intensity [29; 30; 31];
4. Aviation: to identify flight conditions and situations based on diagnostic parameter monitoring [32; 33], and to predict the human state and decision making depending on various environmental factors [34; 35];
5. Radar systems: to estimate the efficiency of signal detection [36], to determine the dependence of weather parameters on radar-received signals [37; 38; 39], and others;* Navigation systems: to build a mathematical model for the optimal selection of the navigation equipment [40; 41; 42], to establish the correlation between navigation equipment failures [43], to approximate operational data trends for the prediction of possible aviation events [44], and others;
* Cybersecurity: to evaluate the efficiency of information web-resources functioning [45], to synthesize data-processing algorithms while detecting cyberattacks [46; 47; 48], to ensure high-level security against cyberattacks [49; 50], and others;
* Engineering and control: to describe nonlinear dynamic object behavior [51; 52], to build the mathematical model for statistical parameters while designing control systems [53], to make decisions based on statistical information processing [54; 55], and others;
* Equipment maintenance: to build the mathematical model for diagnostic variable trends [56], and to determine the uncertainty level while conducting condition monitoring and maintenance preference analysis [57; 58];
* Reliability analysis: to describe the behavior of reliability parameters [59; 60], to simulate statistically nonstationary random processes of failures occurrence [61; 62], to describe the processes of technical condition deterioration in the trend of failure rate [63; 64], and others. Regression analysis usually starts with research on the possibility of using a linear regression model. In the case of an unsatisfactory level of accuracy, more complicated models are used [65]. These models are nonlinear regression models [66]. Nonlinear regression models suggest parabolic, hyperbolic, exponential, segmented, and other approximating functions [65; 67]. Because of the complicated calculations required when using a nonlinear regression model, various software can be utilized [68].