scholarly journals The Scoring Mechanism of Players after Game Based on Cluster Regression Analysis Model

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jin Xu ◽  
Chao Yi
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Regression Models ◽  
Effective Theory ◽  
Standard Errors ◽  
Limited Data ◽  
Analysis Model ◽  
Multiple Objects ◽  
Model Categories ◽  
Model Expression

Cluster regression analysis model is an effective theory for a reasonable and fair player scoring game. It can roughly predict and evaluate the performance of athletes after the game with limited data and provide scientific predictions for the performance of athletes. The purpose of this research is to achieve the player’s postmatch scoring through the cluster regression model. Through the research and analysis of past ball games, the comparison and experiment of multiple objects based on different regression analysis theories, the following conclusions are drawn. Different regression models have different standard errors, but if the data in other model categories are put into the centroid model expression, the standard error and the error of the original model are within 0.3, which can replace other models for calculation. In the player’s postmatch scoring, although the expert’s prediction of the result is very accurate, within the error range of 1 copy, the player’s postmatch scoring mechanism based on the cluster regression analysis model is more accurate, and the error formula is in the 0.5 range. It is best to switch the data of the regression model twice to compare the scoring mechanism using different regression experiments.

A Review of Regression Models in Machine Learning

2021 ◽  
Vol 2 (2) ◽  
pp. 40-47
Author(s):  
Sunil Kumar ◽  
Vaibhav Bhatnagar
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Regression Model ◽  
Regression Models ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Analysis Model ◽  
Data Set ◽  
Data Regression ◽  
Different Types

Machine learning is one of the active fields and technologies to realize artificial intelligence (AI). The complexity of machine learning algorithms creates problems to predict the best algorithm. There are many complex algorithms in machine learning (ML) to determine the appropriate method for finding regression trends, thereby establishing the correlation association in the middle of variables is very difficult, we are going to review different types of regressions used in Machine Learning. There are mainly six types of regression model Linear, Logistic, Polynomial, Ridge, Bayesian Linear and Lasso. This paper overview the above-mentioned regression model and will try to find the comparison and suitability for Machine Learning. A data analysis prerequisite to launch an association amongst the innumerable considerations in a data set, association is essential for forecast and exploration of data. Regression Analysis is such a procedure to establish association among the datasets. The effort on this paper predominantly emphases on the diverse regression analysis model, how they binning to custom in context of different data sets in machine learning. Selection the accurate model for exploration is the most challenging assignment and hence, these models considered thoroughly in this study. In machine learning by these models in the perfect way and thru accurate data set, data exploration and forecast can provide the maximum exact outcomes.

scholarly journals Bimodal Regression Model

2017 ◽  
Vol 40 (1) ◽  
pp. 65-83 ◽  
Author(s):  
Guillermo Domingo Martinez ◽  
Heleno Bolfarine ◽  
Hugo Salinas
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Regression Models ◽  
Error Term ◽  
Bimodal Distribution ◽  
New Model ◽  
Special Cases ◽  
Error Distributions ◽  
New Distribution ◽  
Skew Normal

Regression analysis is a technique widely used in different areas ofhuman knowledge, with distinct distributions for the error term. Itis the case, however, that regression models with bimodal responsesor, equivalently, with the error term following a bimodal distribution are notcommon in the literature, perhaps due to the lack of simple to dealwith bimodal error distributions. In this paper we propose a simpleto deal with bimodal regression model with a symmetric-asymmetricdistribution for the error term for which for some values of theshape parameter it can be bimodal. This new distribution containsthe normal and skew-normal as special cases. A realdata application reveals that the new model can be extremely usefulin such situations.

scholarly journals SPATIAL RESOLUTION ENHANCEMENT OF OVERSAMPLED IMAGES USING REGRESSION DECOMPOSITION AND SYNTHESIS

2022 ◽  
Vol XLVI-4/W3-2021 ◽  
pp. 71-77
Author(s):  
H.-W. Chen
Keyword(s):  
Regression Analysis ◽  
Statistical Model ◽  
Regression Model ◽  
Spatial Resolution ◽  
Regression Models ◽  
Resolution Enhancement ◽  
Design Matrix ◽  
Statistical Inferences ◽  
Proposed Model ◽  
Regression Decomposition

Abstract. A new statistical model designed for regression analysis with a sparse design matrix is proposed. This new model utilizes the positions of the limited non-zero elements in the design matrix to decompose the regression model into sub-regression models. Statistical inferences are further made on the values of these limited non-zero elements to provide a reference for synthesizing these sub-regression models. With this concept of the regression decomposition and synthesis, the information on the structure of the design matrix can be incorporated into the regression analysis to provide a more reliable estimation. The proposed model is then applied to resolve the spatial resolution enhancement problem for spatially oversampled images. To systematically evaluate the performance of the proposed model in enhancing the spatial resolution, the proposed approach is applied to the oversampled images that are reproduced via random field simulations. These application results based on different generated scenarios then conclude the effectiveness and the feasibility of the proposed approach in enhancing the spatial resolution of spatially oversampled images.

Temperature Character Analysis of RCC Based on Stepwise Regression Model

2012 ◽  
Vol 238 ◽  
pp. 268-271
Author(s):  
Yu Qing Zhao
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Stepwise Regression ◽  
Stepwise Regression Analysis ◽  
Gravity Dam ◽  
Practical Calculation ◽  
Analysis Model ◽  
Related Factors ◽  
Reservoir Water ◽  
Basic Principles

The basic principles and ways of stepwise regression analysis is explained, taking the case of Jiangya gravity dam. On the basis of the temperature monitoring data, the optimal regression equation of the dam temperature is established gradually by the dam bedrock temperature, air temperature and reservoir water temperature and other related factors. It is proved that stepwise regression analysis model is reasonable and the simulation is fairly well with high precision. The stepwise regression model can be used to analyze the concrete temperature. The work provides the practical calculation basis for the monitoring of dam safety running.

scholarly journals PENERAPAN REGRESI ZERO INFLATED POISSON UNTUK MENGATASI OVERDISPERSI PADA REGRESI POISSON (Studi Kasus: Ketidaklulusan Siswa SMA/MA dalam Ujian Nasional di Buleleng)

2013 ◽  
Vol 2 (3) ◽  
pp. 23
Author(s):  
LUH KOMANG MARDIANI ◽  
KOMANG GDE SUKARSA ◽  
I GUSTI AYU MADE SRINADI
Keyword(s):  
High School ◽  
Regression Analysis ◽  
Count Data ◽  
Poisson Regression ◽  
Regression Models ◽  
Senior High School ◽  
Standard Errors ◽  
Poisson Regression Analysis ◽  
Independent Variables ◽  
Regression Methods

The Poisson regression analysis is one of the regression methods used for count data and has the assumption of equidispersion. However, it is the overdispersion and then underestimate standard errors will be obtained. If the data are overdispersed and more data is zero then ZIP (Zero Inflated Regression) regression is used. ZIP regression model is more appropriate to be used to analyze the amount of Senior High School/Madrasah Aliyah who do not pass the exam with five independent variables, because a lot of data failure is zero. In this paper, data are overdispersed on Poisson regression, so ZIP regression are used. ZIP regression models obtained are only influenced by the proportion of Senior High School/Madrasah Aliyah classroom were damaged (X3), is and .

scholarly journals The research of regression model in machine learning field

2018 ◽  
Vol 176 ◽  
pp. 01033 ◽  
Author(s):  
Shen Rong ◽  
Zhang Bao-wen
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Reference Value ◽  
The Other ◽  
Analysis Model ◽  
Model Based ◽  
Set Up ◽  
Library Function

The paper herein will analyze the sale of iced products affected by variation of temperature. Firstly, we will collect the data of the forecast temperature last year and the sale of iced products and then conduct data compilation and cleansing. Finally, we will set up the mathematical regression analysis model based on the cleansed data by means of data mining theory. Regression analysis refers to the method of studying the relationship between independent variable and dependent variable. Linear regression model that corresponds to the practical situation is proposed in the paper, which is to set up simple linear regression model based on practical problem and then to implement the following with the help of the latest and most popular Python3.6. Python3.6 boasts the features of pure object-oriented, platform independence and concise and elegant language. So we will call the corresponding library function to predict the sale of iced products according to the variation of temperature, which will provide the foundation for the company to adjust its production each month, or even each week and each day. As a result, the situation of overproduction can be avoided. Moreover, the other situation as the profit will be affected by the lack of production since the rise of temperature will also be avoided. So the regression model also has reference value for the other fields of marketing.

scholarly journals Türkiye’de Organik Bal Üretiminin Yıllara Göre Değişiminin Regresyon Analizi ile İncelenmesi Üzerine Bir Çalışma

2018 ◽  
Vol 6 (11) ◽  
pp. 1507 ◽  
Author(s):  
Şenol Çelik ◽  
Turgay Şengül ◽  
Bünyamin Söğüt ◽  
A. Yusuf Şengül
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Regression Models ◽  
Mean Squared Error ◽  
Parameter Estimates ◽  
Suitable Model ◽  
Inverse Regression ◽  
Linear Quadratic ◽  
Honey Production ◽  
Organic Honey

In this study, changes in organic honey production in Turkey between 2004 and 2016 were examined by regression analysis. In regression analysis, linear, quadratic, cubic, logarithmic and inverse regression models have been studied comparatively. The R2 values obtained with these models are; 0.155, 0.616, 0.699, 0.366, 0.522, R ̅^2 values were found as 0.079, 0.539, 0.599, 0.308, 0.479 and MSE (Mean Squared Error) values were 48743.013, 24376.605, 21228.605, 36580.476, 27563.473, respectively. The quadratic regression model, in which the parameter estimates are significant, R ̅^2 is the highest and MSE is the lowest, is the most appropriate model. According to this regression model, estimated organic honey production yields in 2017 and 2018 are going to be 693 and 891 tons, respectively. In addition, regression analysis of non-organic honey production was done in the same period and linear regression model was determined as the most suitable model. For this model, R2= 0.772 and R ̅^2 = 0.750 were calculated. As a result, it has been concluded that organic and non-organic honey production yields can be estimated with different regression models.

A Regression Analysis Model of Ordinal Variable to Psychological Data

2015 ◽  
Vol 47 (12) ◽  
pp. 1520
Author(s):  
Peng XU ◽  
Lu QI ◽  
Jian XIONG ◽  
Haosheng YE
Keyword(s):  
Regression Analysis ◽  
Analysis Model ◽  
Ordinal Variable ◽  
Psychological Data

scholarly journals Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant

2021 ◽  
Vol 11 (4) ◽  
pp. 1776
Author(s):  
Young Seo Kim ◽  
Han Young Joo ◽  
Jae Wook Kim ◽  
So Yun Jeong ◽  
Joo Hyun Moon
Keyword(s):  
Power Generation ◽  
Data Analysis ◽  
Power Plant ◽  
Relative Humidity ◽  
Regression Model ◽  
Regression Models ◽  
Solar Power ◽  
Meteorological Variables ◽  
Solar Power Plant ◽  
Solar Power Generation

This study identified the meteorological variables that significantly impact the power generation of a solar power plant in Samcheonpo, Korea. To this end, multiple regression models were developed to estimate the power generation of the solar power plant with changing weather conditions. The meteorological data for the regression models were the daily data from January 2011 to December 2019. The dependent variable was the daily power generation of the solar power plant in kWh, and the independent variables were the insolation intensity during daylight hours (MJ/m2), daylight time (h), average relative humidity (%), minimum relative humidity (%), and quantity of evaporation (mm). A regression model for the entire data and 12 monthly regression models for the monthly data were constructed using R, a large data analysis software. The 12 monthly regression models estimated the solar power generation better than the entire regression model. The variables with the highest influence on solar power generation were the insolation intensity variables during daylight hours and daylight time.

scholarly journals Regression Models in Complex Survey Sampling for Sensitive Quantitative Variables

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 609
Author(s):  
María del Mar Rueda ◽  
Beatriz Cobo ◽  
Antonio Arcos
Keyword(s):  
Regression Analysis ◽  
Regression Models ◽  
Regression Coefficients ◽  
Randomized Response ◽  
Statistical Tool ◽  
Complex Survey ◽  
Complex Sample ◽  
Qualitative Variables ◽  
World Survey ◽  
Sensitive Variable

Randomized response (RR) techniques are widely used in research involving sensitive variables, such as drugs, violence or crime, especially when a population mean or prevalence must be estimated. However, they are not generally applied to examine relationships between a sensitive variable and other characteristics. This type of technique was initially applied to qualitative variables, and studies later showed that a logistic regression may be performed with RR data. Since many of the variables considered in this context are quantitative, RR techniques were extended to these cases to estimate the values required. Regression analysis is a valuable statistical tool for exploring relationships among variables and for establishing associations between responses and covariates. In this article, we propose a design-based regression analysis for complex sample designs based on the unified RR approach. We present estimators of the regression coefficients, study their theoretical properties and consider different ways to estimate their variance. The properties of these estimation techniques were simulated using various quantitative randomized models. The method proposed was also used to analyse the findings from a real-world survey.

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