scholarly journals Research on the Coupling Relationship between Teaching Style and Classroom Management Based on Grey Correlation Analysis and Multivariate Linear Regression Method

2013 ◽  
Author(s):  
Lihua Yang
Keyword(s):  
Linear Regression ◽  
Correlation Analysis ◽  
Classroom Management ◽  
Teaching Style ◽  
Regression Method ◽  
Multivariate Linear Regression ◽  
Linear Regression Method ◽  
Grey Correlation ◽  
Coupling Relationship ◽  
Grey Correlation Analysis

scholarly journals Predict the amount of Cu using the four Ca, Al, P, S Elements by Multiple Linear Regression Method

2021 ◽  
Vol 9 (9) ◽  
pp. 1088-1092
Author(s):  
Hamed Nazerian
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Method ◽  
Multivariate Linear Regression ◽  
Linear Regression Method ◽  
Multiple Linear Regression Method ◽  
Spss Software ◽  
Exploration Area ◽  
The Relationship ◽  
South Khorasan

Abstract: The study area is located in Sarbisheh city in South Khorasan province, Iran. Copper estimation was performed by multivariate linear regression method to facilitate the use of previous analyses to predict this element in other areas, reduce costs and also reduce the number of samples. For this purpose, by obtaining a basic formula from estimating the amount of Cu with one of the promising points samples, the amount of copper in other parts of the exploration area was investigated. Several analyses were taken from the exploratory area after calculations to validate the regression. The regression results of new and old data were compared and estimation acceptable. These calculations were performed by SPSS software, according to the four elements Ca, Al, P, S, the results obtained and the relationship presented has acceptable validity. Keywords: Multivariate linear regression, Cu estimation, SPSS, Iran.

scholarly journals Evaluating Four Remote Sensing Methods for Estimating Surface Air Temperature on a Regional Scale

2017 ◽  
Vol 56 (3) ◽  
pp. 803-814 ◽  
Author(s):  
Suhua Liu ◽  
Hongbo Su ◽  
Jing Tian ◽  
Renhua Zhang ◽  
Weizhen Wang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Air Temperature ◽  
Regional Scale ◽  
Surface Air Temperature ◽  
Regression Method ◽  
Multivariate Linear Regression ◽  
The Other ◽  
Linear Regression Method ◽  
Estimation Errors ◽  
Remote Sensing Methods

AbstractSurface air temperature is a basic meteorological variable to monitor the environment and assess climate change. Four remote sensing methods—the temperature–vegetation index (TVX), the univariate linear regression method, the multivariate linear regression method, and the advection-energy balance for surface air temperature (ADEBAT)—have been developed to acquire surface air temperature on a regional scale. To evaluate their utilities, they were applied to estimate the surface air temperature in northwestern China and were compared with each other through regressive analyses, t tests, estimation errors, and analyses on estimations of different underlying surfaces. Results can be summarized into three aspects: 1) The regressive analyses and t tests indicate that the multivariate linear regression method and the ADEBAT provide better accuracy than the other two methods. 2) Frequency histograms on estimation errors show that the multivariate linear regression method produces the minimum error range, and the univariate linear regression method produces the maximum error range. Errors of the multivariate linear regression method exhibit a nearly normal distribution and that of the ADEBAT exhibit a bimodal distribution, whereas the other two methods display negative skewness distributions. 3) Estimates on different underlying surfaces show that the TVX and the univariate linear regression method are significantly limited in regions with sparse vegetation cover. The multivariate linear regression method has estimation errors within 1°C and without high levels of errors, and the ADEBAT also produces high estimation errors on bare ground.

scholarly journals Monitoring of PM2.5 Concentrations by Learning from Multi-Weather Sensors

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6086
Author(s):  
Yuexia Wang ◽  
Zhihuo Xu
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Correlation Coefficient ◽  
Nonlinear Regression ◽  
Regression Method ◽  
Multivariate Linear Regression ◽  
Series Data ◽  
Linear Regression Method ◽  
Multivariate Nonlinear Regression ◽  
Weather Stations

This paper aims to monitor the ambient level of particulate matter less than 2.5 μm (PM2.5) by learning from multi-weather sensors. Over the past decade, China has established a high-density network of automatic weather stations. In contrast, the number of PM monitors is much smaller than the number of weather stations. Since the haze process is closely related to the variation of meteorological parameters, it is possible and promising to calculate the concentration of PM2.5 by studying the data from weather sensors. Here, we use three machine learning methods, namely multivariate linear regression, multivariate nonlinear regression, and neural network, in order to monitor PM2.5 by exploring the data of multi-weather sensors. The results show that the multivariate linear regression method has the root mean square error (RMSE) of 24.6756 μg/m3 with a correlation coefficient of 0.6281, by referring to the ground truth of PM2.5 time series data; and the multivariate nonlinear regression method has the RMSE of 24.9191 μg/m3 with a correlation coefficient of 0.6184, while the neural network based method has the best performance, of which the RMSE of PM2.5 estimates is 15.6391 μg/m3 with the correlation coefficient of 0.8701.

scholarly journals EVOLUTION OF MULTIVARIATE LINEAR REGRESSION METHOD FOR TRACER STUDIES ANALYSIS

2021 ◽  
pp. 554
Author(s):  
R. R. Fakhreeva ◽  
Yu. A. Pityuk ◽  
G. F. Asalkhuzina ◽  
A. Ya. Davletbaev ◽  
V. P. Miroshnichenko ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Method ◽  
Multivariate Linear Regression ◽  
Linear Regression Method ◽  
Tracer Studies

Rainfall Prediction in South-Eastern Part of Bangladesh by Linear Regression Method

2018 ◽  
Vol 6 (6) ◽  
pp. 119
Author(s):  
Mohammad Shohidul Islam ◽  
Sultana Easmin Siddika ◽  
S M Injamamul Haque Masum
Keyword(s):  
Linear Regression ◽  
Correlation Coefficients ◽  
Rain Gauge ◽  
Regression Method ◽  
Linear Regression Method ◽  
Climate Data ◽  
Rainfall Forecasting ◽  
Statistical Parameters ◽  
Time Duration ◽  
Rainfall Prediction

Rainfall forecasting is very challenging task for the meteorologists. Over the last few decades, several models have been utilized, attempting the successful analysing and forecasting of rainfall. Recorded climate data can play an important role in this regard. Long-time duration of recorded data can be able to provide better advancement of rainfall forecasting. This paper presents the utilization of statistical techniques, particularly linear regression method for modelling the rainfall prediction over Bangladesh. The rainfall data for a period of 11 years was obtained from Bangladesh Meteorological department (BMD), Dhaka i.e. that was surface-based rain gauge rainfall which was acquired from 08 weather stations over Bangladesh for the years of 2001-2011. The monthly and yearly rainfall was determined. In order to assess the accuracy of it some statistical parameters such as average, meridian, correlation coefficients and standard deviation were determined for all stations. The model prediction of rainfall was compared with true rainfall which was collected from rain gauge of different stations and it was found that the model rainfall prediction has given good results.

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