Analysis of influencing factors of grain yield based on multiple linear regression

2021 ◽  
Vol 15 (3) ◽  
pp. 337
Author(s):  
Victor Chang ◽  
Qianwen Xu
Keyword(s):  
Linear Regression ◽  
Grain Yield ◽  
Multiple Linear Regression ◽  
Influencing Factors

Application of combined model with DGM(1,1) and linear regression in grain yield prediction

2018 ◽  
Vol 8 (1) ◽  
pp. 25-34 ◽  
Author(s):  
Bingjun Li ◽  
Weiming Yang ◽  
Xiaolu Li
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Grain Yield ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Entropy Weight ◽  
Combined Model ◽  
Content Type ◽  
Combination Model

Purpose The purpose of this paper is to address and overcome the problem that a single prediction model cannot accurately fit a data sequence with large fluctuations. Design/methodology/approach Initially, the grey linear regression combination model was put forward. The Discrete Grey Model (DGM)(1,1) model and the multiple linear regression model were then combined using the entropy weight method. The grain yield from 2010 to 2015 was forecasted using DGM(1,1), a multiple linear regression model, the combined model and a GM(1,N) model. The predicted values were then compared against the actual values. Findings The results reveal that the combination model used in this paper offers greater simulation precision. The combination model can be applied to the series with fluctuations and the weights of influencing factors in the model can be objectively evaluated. The simulation accuracy of GM(1,N) model fluctuates greatly in this prediction. Practical implications The combined model adopted in this paper can be applied to grain forecasting to improve the accuracy of grain prediction. This is important as data on grain yield are typically characterised by large fluctuation and some information is often missed. Originality/value This paper puts the grey linear regression combination model which combines the DGM(1,1) model and the multiple linear regression model using the entropy weight method to determine the results weighting of the two models. It is intended that prediction accuracy can be improved through the combination of models used within this paper.

scholarly journals Post-traumatic growth and influencing factors among frontline nurses fighting against COVID-19

2020 ◽  
pp. oemed-2020-106540
Author(s):  
Pan pan Cui ◽  
Pan pan Wang ◽  
Kun Wang ◽  
Zhiguang Ping ◽  
Peng Wang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Influencing Factors ◽  
Psychological Intervention ◽  
Linear Regression Analysis ◽  
Bivariate Analysis ◽  
Related Factors ◽  
Self Confidence ◽  
Post Traumatic Growth ◽  
Post Traumatic

ObjectiveTo explore the level and influencing factors of frontline nurses’ post-traumatic growth (PTG) during COVID-19 epidemic.MethodsA cross-sectional survey was conducted in February 2020 in three hospitals in China. The Post-traumatic Growth Inventory (PTGI) was used to investigate the PTG of frontline nurses. Data on related factors, including demographic characteristics and subjective variables, were collected. The Event-Related Rumination Inventory was used to assess rumination. Pearson’s or Spearman’s correlation was calculated for bivariate analysis. Independent sample t-tests or one-way analysis of variance and multiple linear regression analysis were used to examine the related factors.ResultsA total of 179 frontline nurses were recruited, and 167 were included in the analyses. The mean PTG score was 70.53±17.26. The bivariate analyses showed that deliberate rumination was modestly positively correlated with PTG (r=0.557, p<0.01), while intrusive rumination had a modest negative correlation with PTG (r=−0.413, p<0.01). Multiple linear regression demonstrated that working years, self-confidence in frontline work, awareness of risk, psychological intervention or training during the epidemic and deliberate rumination were the main influencing factors of PTG among frontline nurses and accounted for 42.5% of the variance (F=31.626, p<0.001).ConclusionsThe PTG of frontline nurses was at a medium to high level and was influenced by working years, self-confidence in frontline work, awareness of risk, psychological intervention or training and deliberate rumination. It is necessary to strengthen psychological guidance and training for frontline nurses and promote their deliberate rumination on epidemic events to improve their PTG.

scholarly journals Research and application of constructing football training linear programming based on multiple linear regression equation

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
YinZhuang Bai ◽  
Aizhen Ren ◽  
Adil Omar Khadidos ◽  
Mohammed Abdulrazzqa
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Influencing Factors ◽  
Linear Regression Analysis ◽  
Quantitative Relationship ◽  
Broad Perspective ◽  
The Core ◽  
Football Game ◽  
Football Match ◽  
Football Training

Abstract Based on the factors affecting sports performance, from a more comprehensive and broad perspective, after consulting the literature, 52 factors that affect the outcome of football matches are selected, including technology, tactics, physical fitness and referees’ penalties. By watching the video of the game, 52 influencing factors of 200 games and 400 teams were counted. The original data was statistically processed with correlation analysis and multiple linear regression analysis, and the statistics of the 26 European Cup games were substituted into the winning formula. To verify the scientific nature and objectivity of the formula, we aim to ascertain the core factors in the winning factors of a football game and the quantitative relationship between these factors and the result of the game, so as to provide a certain reference for football training, game analysis and scientific research. The technical and tactical ability of individuals and teams is the core competitive ability factor that affects the result of the game; from a single factor, 15 factor indicators have a significant impact on the result of a football match; on the whole, 10 factor indicators have a significant effect on the result of a football match. In addition, there is a certain quantitative relationship between these influencing factors and the results of the game; empirical evidence shows that the football game winning formula has a certain degree of science and objectivity.

scholarly journals Investigation on the Stress of Chinese Pediatricians Under the Outbreak of COVID-19

2021 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Lan Yang ◽  
Bingbing Zhang ◽  
Xiaoxing Kong ◽  
Weifang Zhou ◽  
Jianmei Tian ◽  
...  
Keyword(s):  
Linear Regression ◽  
Correlation Analysis ◽  
Sleep Quality ◽  
Multiple Linear Regression ◽  
Influencing Factors ◽  
Perceived Stress ◽  
Physical Condition ◽  
Work Experience ◽  
Mental Stress ◽  
Professional Title

Background: The Coronavirus disease 2019 (COVID-19) pandemic has posed a severe threat to international health and the economy. Clinicians, the main staff involved in fighting against the pandemic, are under great pressure. However, relevant mental stress studies are lacking at present. Objectives: This study aimed to explore the mental stress level and its influencing factors among Chinese pediatricians under the outbreak of COVID-19, aiming to provide a certain theoretical basis for relevant psychological intervention among medical staff. Methods: In this cross-sectional study, 352 in-service pediatricians were selected from nine hospitals in Jiangsu province, China, in February 2020. The online survey was performed to collect general information. Meanwhile, the Perceived Stress Scale (PSS-10), Self-rating Anxiety Scale (SAS), and Pittsburgh Sleep Quality Index (PSQI) scale were employed for assessment. Afterward, the stress level and influencing factors among pediatricians were analyzed through descriptive analysis, one-way analysis of variance (ANOVA), correlation analysis, and multiple linear regression analysis. Results: The mean score of PSS-10 showed moderate stress on the whole. Pediatricians from outpatient and emergency departments had significantly higher perceived stress than those from other departments. The perceived stress was significantly associated with age, educational level, professional title, work experience, and physical condition. Stepwise multiple linear regression showed that age, professional title, and physical condition had a linear relationship with the individual’s perceived stress. Besides, Pearson correlation analysis indicated that perceived stress was associated with anxiety level and sleep quality. Conclusions: Under the outbreak of COVID-19, pediatricians suffer from relatively high levels of mental stress. The influencing factors include education, age, professional title, work experience, and physical condition. Typically, the anxiety level and sleep quality are correlated with the mental stress among pediatricians.

scholarly journals Analysis of the Influencing Factors of the Medical Service Capacity in Key Clinical Specialties at the County Level Based on TOPSIS

2020 ◽  
Author(s):  
Kaixuan Wang ◽  
Jin-Ling Guo ◽  
Wei Wang ◽  
Shui-Chang Zhao ◽  
Miao-Jun Li ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Influencing Factors ◽  
Medical Service ◽  
Compliance Rate ◽  
Care Quality ◽  
Appropriate Technology ◽  
County Level ◽  
Service Capacity ◽  
Personnel Training

Abstract To acknowledge the medical service capacity of the first batch of county-level key clinical specialties in Henan province, analyze the influencing factors, and make targeted suggestions. TOPSIS method was used to evaluate the medical service capacity comprehensively, and the influencing factors were analyzed by simple linear regression and multiple linear regression. In the multiple linear regression model, the basic conditions, including the total investment funds during the construction period, net usable area of beds and the ratio of bed to nurse, the quality of care—the compliance rate of major clinical diagnosis and pathological diagnosis, and the personnel training—the number of technical promotion trainees, of the first county-level clinical specialties, have a significant influence upon the medical service capacity; while, the actual number of beds, the ratio of doctors and nurses, the proportion of annual expert visits, the number of technical promotion training and sending of learners, percentage of drug expenditure and core journal articles have limited effect. On the basis of the ensured allocation of resources and capital investment, more attention should be paid to personnel training and appropriate technology promotion, and the medical care quality is supposed to be always insisted on, to promote the overall improvement of medical service capacity in county hospitals.

scholarly journals Workload and influencing factors in non-emergency medical transfers: a multiple linear regression analysis of a cross-sectional questionnaire study

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Johann Georg Keunecke ◽  
Christine Gall ◽  
Torsten Birkholz ◽  
Andreas Moritz ◽  
Christian Eiche ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Influencing Factors ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Task Demands ◽  
Measurement Tool ◽  
Online Questionnaire ◽  
Cross Sectional ◽  
Emergency Medical

Abstract Background Human workload is a key factor for system performance, but data on emergency medical services (EMS) are scarce. We investigated paramedics’ workload and the influencing factors for non-emergency medical transfers. These missions make up a major part of EMS activities in Germany and are growing steadily in number. Methods Paramedics rated missions retrospectively through an online questionnaire. We used the NASA-Task Load Index (TLX) to quantify workload and asked about a variety of medical and procedural aspects for each mission. Teamwork was assessed by the Weller teamwork measurement tool (TMT). With a multiple linear regression model, we identified a set of factors leading to relevant increases or decreases in workload. Results A total of 194 non-emergency missions were analysed. Global workload was rated low (Mean = 27/100). In summary, 42.8% of missions were rated with a TLX under 20/100. TLX subscales revealed low task demands but a very positive self-perception of performance (Mean = 15/100). Teamwork gained high ratings (Mean TMT = 5.8/7), and good teamwork led to decreases in workload. Aggression events originating from patients and bystanders occurred frequently (n = 25, 12.9%) and increased workload significantly. Other factors affecting workload were the patient’s body weight and the transfer of patients with transmittable pathogens. Conclusion The workload during non-emergency medical transfers was low to very low, but performance perception was very positive, and no indicators of task underload were found. We identified several factors that led to workload increases. Future measures should attempt to better train paramedics for aggression incidents, to explore the usefulness of further technical aids in the transfer of obese patients and to reconsider standard operating procedures for missions with transmittable pathogens.

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