scholarly journals IDENTIFICATION OF AN AUTOREGRESSIVE PROCESS USING MATLAB

T-Comm ◽  
2021 ◽  
Vol 15 (7) ◽  
pp. 34-38
Author(s):  
Alina A. Sherstneva ◽  
◽  
Olga G. Sherstneva ◽  
Keyword(s):  
Regression Analysis ◽  
Least Squares Method ◽  
Theoretical Calculations ◽  
Autoregressive Process ◽  
Reliability Indicators ◽  
Estimation Problems ◽  
Trend Forecasting ◽  
Technical Issues ◽  
Theory Of Filtration ◽  
Parameters Calculation

The article aims to apply the methods of regression analysis in estimation task the reliability indicators of an infocommunication system and data trend forecasting. The task of assessment the reliability indicators of infocommunication systems is based, traditionally, on statistical data, the collection and processing is carried out by monitoring system. To obtain the calculated indicators as close as possible to the real practical results it is necessary to process a large number of measurements. In this sense, the theory of filtration is widely used in various estimation problems. It allows to support the possibility of effective solution of technical issues and implementation through mathematical modeling programs. The article is aimed to consider the issues of data trend forecasting for parameters calculation of infocommunication systems. One of the most effective solutions is the use of regression analysis methods. The research gives development and analysis of mathematical and program models. Based on theoretical calculations, methods for solving the problem are determined and experimental research are carried out. The article solves the problem of identifying an autoregressive process through the Yule-Walker equations. In addition to theoretical calculations, a program has been developed that allows handle the process of calculations in the Matlab environment. As a result, it is proposed to compare the results of identification time series of variables through the Yule-Walker system with the classical parameter estimation by the least squares method. The results are shown graphically.

scholarly journals REGRESSION ANALYSIS APPLICATION FOR TELETRAFFIC THEORY TASKS

T-Comm ◽  
2020 ◽  
Vol 14 (12) ◽  
pp. 18-25
Author(s):  
Alina A. Sherstneva ◽  
Keyword(s):  
Regression Analysis ◽  
Least Squares ◽  
Experimental Analysis ◽  
Random Sequence ◽  
Measurement Data ◽  
Real Data ◽  
Polynomial Model ◽  
Theoretical Research ◽  
Graphical Form ◽  
Trend Forecasting

The article aims to consider least squares approach for solving problems of queuing systems theory. The opportunity of predicting the behavior of infocommunication system is shown. Choosing the optimal model of its functioning is proposed. On base monitoring system metrics, statistical data were formed. The article proposes to make data trend forecasting, to estimate parameters of random processes over time. To obtain the results of functioning data in infocommunication systems that are as close as possible to the real values, polynomial and sine models are considered. The method of regression analysis is proposed to determine the parameter values for a model from a set of observational data. In theoretical research, the linear and nonlinear least squares methods are used in terms of a circle. The task of experimental analysis is to obtain an estimated parameter of sine, polynomial models and the center of circle. Experimental analysis was performed using the mathematical modeling program Matlab. A uniformly distributed random sequence and a random sequence with normal distribution are generated. The sequence with experimental data for polynomial and sine models, respectively, are calculated. The correspondence each model for generated data is shown in graphical form. The measurement data obeys observations. The estimated parameters are summarized in the tables. The polynomial order is estimated. The estimated dispersion curve of the polynomial model is obtained. The calculated variance values of the polynomial model are presented. Data trend forecasting for measurement data is made. The estimated values are extremally close to real data. The results are shown in graphs. Finally, an approximate model of the circumference of measurement data is presented in graphical form. After some iterations with estimated center from the arithmetic mean the new circle center is given. And quite close values for center and radius of circle are obtained.

A nonlinear regression analysis method based on Stakeholder Theory Model for performance evaluation of poverty reduction of agricultural operators

2019 ◽  
pp. 002072091989420
Author(s):  
Zhenmin Wang
Keyword(s):  
Regression Analysis ◽  
Performance Evaluation ◽  
Stakeholder Theory ◽  
Nonlinear Regression ◽  
Poverty Reduction ◽  
Least Squares Method ◽  
Theory Model ◽  
Analysis Method ◽  
Nonlinear Regression Analysis ◽  
Policy Supply

A nonlinear regression analysis method for performance evaluation of poverty reduction of agricultural operators based on Stakeholder Theory Model analysis was proposed to improve the effectiveness of performance evaluation of poverty reduction of a new type agricultural operator in poverty-stricken areas. Firstly, it can find out which interest demands of different stakeholders and which interest realization paths have significant impact on the task performance and peripheral performance of the policy supply of new agricultural operators through factor analysis and regression analysis of the questionnaire data of stakeholders to provide sufficient basis for the appropriate revision and adjustment of policy supply; secondly, the nonlinear regression model is established by using the nonlinear least squares method to fit, control, and forecast the performance evaluation of poverty reduction behavior of agricultural operators, which is more accurate than linear regression in both object and method. Finally, the effectiveness of the proposed algorithm is verified by empirical analysis.

scholarly journals Necessary Sample Size for Method Comparison Studies Based on Regression Analysis

1999 ◽  
Vol 45 (6) ◽  
pp. 882-894 ◽  
Author(s):  
Kristian Linnet
Keyword(s):  
Regression Analysis ◽  
Sample Size ◽  
Statistical Power ◽  
Theoretical Calculations ◽  
Method Comparison ◽  
Sample Sizes ◽  
Analytical Error ◽  
Deming Regression ◽  
Standard Deviations ◽  
Range Of Values

Abstract Background: In method comparison studies, it is of importance to assure that the presence of a difference of medical importance is detected. For a given difference, the necessary number of samples depends on the range of values and the analytical standard deviations of the methods involved. For typical examples, the present study evaluates the statistical power of least-squares and Deming regression analyses applied to the method comparison data. Methods: Theoretical calculations and simulations were used to consider the statistical power for detection of slope deviations from unity and intercept deviations from zero. For situations with proportional analytical standard deviations, weighted forms of regression analysis were evaluated. Results: In general, sample sizes of 40–100 samples conventionally used in method comparison studies often must be reconsidered. A main factor is the range of values, which should be as wide as possible for the given analyte. For a range ratio (maximum value divided by minimum value) of 2, 544 samples are required to detect one standardized slope deviation; the number of required samples decreases to 64 at a range ratio of 10 (proportional analytical error). For electrolytes having very narrow ranges of values, very large sample sizes usually are necessary. In case of proportional analytical error, application of a weighted approach is important to assure an efficient analysis; e.g., for a range ratio of 10, the weighted approach reduces the requirement of samples by >50%. Conclusions: Estimation of the necessary sample size for a method comparison study assures a valid result; either no difference is found or the existence of a relevant difference is confirmed.

A Fuzzy Linear Regression Analysis for Fuzzy Input-Output Data Using the Least Squares Method under Linear Constraints and Its Application to Fuzzy Rating Data

1999 ◽  
Vol 3 (1) ◽  
pp. 36-41 ◽  
Author(s):  
Kazuhisa Takemura ◽  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Least Squares ◽  
Least Squares Method ◽  
Linear Regression Analysis ◽  
Linear Constraints ◽  
Fuzzy Linear Regression ◽  
Output Data ◽  
Rating Data ◽  
Fuzzy Input

Fuzzy linear regression analysis using the least squares method under linear constraint, where input data, output data, and coefficients are represented by triangular fuzzy numbers, was proposed and compared to possibilistic linear regression analysis proposed by Sakawa and Yano (1992) using fuzzy rating data in a psychological study. Major findings of the comparison were as follows: (1) Under the proposed analysis, the width between the maximum and minimum of the predicted model was nearer to the width of the dependent variable than that of possibilistic linear regression analysis, (2) the representative prediction by the proposed analysis was also nearer to that of the dependent variable, compared to that of possibilistic linear regression analysis.

scholarly journals Evaluation of carcass quality of live pigs at weights of 60 kg and 90 kg

1971 ◽  
Vol 43 (2) ◽  
pp. 111-127
Author(s):  
Unto Uusisalmi
Keyword(s):  
Regression Analysis ◽  
Least Squares Method ◽  
Live Weight ◽  
Stepwise Multiple Regression ◽  
Carcass Quality ◽  
Progeny Testing ◽  
Stepwise Multiple Regression Analysis ◽  
Explanatory Variables ◽  
Initial Correlation

The possibility of predicting the carcass quality from live pigs at weights of c. 60 kg and c. 90 kg was investigated on progeny testing pigs (n = 236) of Landrace and Yorkshire breeds. In both weight classes the thickness of the fat was measured with an ultrasonic instrument on the back, hams and shoulders, as well as the depth of the m. long, dorsi. Exterior measurements and points evalutations were also made. The pigs were slaughtered at a weight of 90 kg, after which a conventional carcass evaluation was made. The least squares method (HARVEY 1966) was used to analyse the results in order to establish the effect of the live weight, the group, breed and sex. After calculating an initial correlation matric (179 x 179), 20 characteristics obtained from the carcass evaluation were taken as functions, and 30 characteristics of both the live weights taken as optional explanatory variables, for further processing by stepwise multiple regression analysis. The back fat can be estimated from measurements at live weights of 60 kg (R2 = 0.53) and 90 kg (R2 = 0.75). The fat estimations obtained from the two weight classes showed similarities. The average back fat measured at a live weight of 90 kg correlated significantly with the average back fat measured from the carcass (r = 0.82***, r2 = 0.67), and the side fat at a live weight of 90 kg with the s.o.l. from the carcass (r = 0.79***, r2 = 0.62). Prediction of the area of the m. long. dorsi was poor in both the weights (R2 = 0.27—0.29).

scholarly journals Testing the methodology for analyzing the impact of wages on the reproduction of cadres in rural areas by the example of urban districts of the Sverdlovsk region

2020 ◽  
Vol 194 (3) ◽  
pp. 91-100
Author(s):  
Valentina Sharapova ◽  
Ivan Borisov ◽  
N. Sharapova
Keyword(s):  
Regression Analysis ◽  
Rural Areas ◽  
Least Squares Method ◽  
Statistical Significance ◽  
Economic Effect ◽  
Practical Significance ◽  
Advantages And Disadvantages ◽  
Urban Districts ◽  
Labor Resources ◽  
The Impact

Abstract. The purpose of this article is to develop and test a methodology for assessing the impact of wages on the reproduction of personnel in rural areas. Urban districts of the Sverdlovsk region were selected as the object for the pilot project. As a research method, the authors used multiple regression analysis of spatial data using the least squares method. As the data sources for the sample, data from the database of the municipalities of Rosstat were used. As a result of the behavioral research, key factors were identified that affect various indicators of the reproduction of the labor resources of the territory, the statistical significance of the influencing factors is evaluated, and the explanatory ability of the obtained models of their possibilities of their use for forecasting is evaluated. It was found that indicators of wages have a significant impact on most indicators of the reproduction of labor resources, indicators of the state of public health in the territory also have a significant impact. Based on the results of the study, recommendations are given for further refinement of the methodology, as well as recommendations for improving the system of statistical reporting of municipalities. The scientific novelty of the study is the use of regression analysis as a method for assessing the reproductive processes of rural territories using data from urban districts of the Sverdlovsk region. The practical significance of the results obtained is a quantitative assessment of the dependence of the reproduction parameters on the factors under study, which makes it possible to quantify the economic effect of the predicted measures. Also, the practical significance of the article lies in identifying the advantages and disadvantages of the proposed methodology that emerged during the pilot study, and the corresponding directions for its improvement.

Propylene Glycol Plasma Level

PEDIATRICS ◽  
1985 ◽  
Vol 76 (4) ◽  
pp. 654-654
Author(s):  
ALLEN M. GLASGOW ◽  
ROGER L. BOECKX ◽  
MARILEA K. MILLER ◽  
MHAIRI G. MACDONALD ◽  
GILBERT P. AUGUST ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Plasma Level ◽  
Propylene Glycol ◽  
Theoretical Calculations ◽  
Linear Regression Analysis ◽  
Chemical Properties ◽  
Typographical Error ◽  
Correct Formula ◽  
Derived Data ◽  
Osmolal Gap

In Reply.— We thank Dr Hall and his associates for pointing out the typographical error in our formula for estimating propylene glycol concentration from osmolal gap.1 The correct formula is, as presumed by Hall et al, propylene glycol (mg/dL) = 47.5 + (osmolal gap x 9.2). It is important to remember that both our formula and that developed by Fligner et al2 are determined from linear regression analysis of experimentally derived data and are not theoretical calculations based on chemical properties of propylene glycol.

Regression Analysis of Dynamic Parameters Based on Experimental Data of a New Seismic Isolation Layer

2011 ◽  
Vol 105-107 ◽  
pp. 523-526
Author(s):  
Fei Yao ◽  
Ke Liu ◽  
Shou Ping Shang
Keyword(s):  
Regression Analysis ◽  
Seismic Isolation ◽  
Least Squares Method ◽  
Lateral Displacement ◽  
Damping Ratio ◽  
Shake Table Tests ◽  
Isolation Layer ◽  
Lower Beam ◽  
Calculation Results ◽  
Input Acceleration

A new steel-asphalt composite seismic isolation layer is introduced briefly. Based on data of shake table tests, regression analysis by least squares method is adopted to analyze the natural frequency and the damping ratio of a new isolation layer. The calculation results show that the damping increases as the input acceleration increases because when PGA is relatively large, the lateral displacement of the isolation layer is large, the distance between the upper beam and the lower beam become smaller. The upper beam squeezes the asphalt ointment between it and brick piers. Part of the vertical load begins to transfer to the brick piers and friction occurs between brick piers and upper beam.. It is good for displacement limit of the structure.

scholarly journals Using Least squares methods and nonlinear regression Methods to Calculate the Approximate Value of Ionicity in Terms of the Energy Gap

2019 ◽  
Vol 24 (5) ◽  
pp. 97
Author(s):  
Ghassan E. Arif ◽  
Sura Y. Jaafar ◽  
, Shymaa M. Abdullah
Keyword(s):  
Regression Analysis ◽  
Numerical Analysis ◽  
Least Squares ◽  
Energy Gap ◽  
Least Squares Method ◽  
Regression Method ◽  
Quadratic Regression ◽  
Nonlinear Method ◽  
Regression Methods ◽  
Analysis Methods

The objective of the current study is to find the best mathematical models to calculate the estimated value of the ionization for the physical compounds of semiconductors based on the energy gap throughout using some numerical analysis methods as the least squares method. The best of its branches obtained is a nonlinear method of the second degree, we compare the new result with other methods and we obtained our new method is more accurate and efficiency. Another side we using some regression analysis methods as the regression method. The best of its branches obtained is a nonlinear method of the quadratic regression model.   http://dx.doi.org/10.25130/tjps.24.2019.097

scholarly journals ESTIMASI NILAI KERUGIAN DAN PREMI ASURANSI BANGUNAN AKIBAT BANJIR SUNGAI CITARUM DI KELURAHAN BALEENDAH BANDUNG

2017 ◽  
Vol 9 (1) ◽  
pp. 79
Author(s):  
Iin Irianingsih ◽  
Sukono Sukono ◽  
Forman Ivana S. S. S.
Keyword(s):  
Regression Analysis ◽  
Moisture Absorption ◽  
Least Squares Method ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Insurance Premium ◽  
Model Parameters ◽  
Loss Of Life ◽  
Insurance Product ◽  
Premium Calculation

High rainfall and a lack of soil moisture absorption, often causing over flowing rivers which leads to floods. Flooding due to over flow of Citarum river is a frequent disasters in the district of Bandung which cause losses ranging from loss of property, damage to homes, diseases to loss of life. In this paper discussed about how to estimate loss cost and insurance due to flooding of the Citarum river. In this case alleged that the losses of Citarum floods affected by heads of family suffering, submerged homes, household damages and field damages. Multiple linear regression analysis was used to estimate the loss cost, which used least squares method for estimating model parameters. Mixture of analysis was used to calculate the insurance premium which considered the zone region, social class, loading factor and regulations set by  OJK. The results indicate the loss cost is affected by household damage, whileother factors are not significant. Whereas, average premium is 0.5 % of insured amount by calculating the loss cost due to flooding of Citarum river. Based on estimates of loss cost and premium calculation, obtained insurance product in accordance of Baleendah condition.

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