scholarly journals Forecasting the yield of principal crops in India on the basis of weather -Paddy / Rice

MAUSAM ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 47-58
Author(s):  
J. A. DAS ◽  
A. K. MEHRA ◽  
M. L. MADNANI
Keyword(s):  
Regression Analysis ◽  
Meteorological Factors ◽  
Correlation Coefficients ◽  
Paddy Rice ◽  
Average Yield ◽  
Weather Factors ◽  
Mysore State ◽  
Cent Level ◽  
Effect On Yield ◽  
Rainy Days

By the method of regression analysis, forecast; formulae have been evolved for forecasting yield of autumn paddy/rice in Mysore State using meteorological factors. The study reveals that there is increase in average yield per acre due to technology from early fifties. In Coastal Mysore, restricted rainy days during July to 15 September and frequency of occasions of drought and floods during August and September are the principal weather factors having significant effect on yield. The corresponding factor for Interior Mysore North is occasions of droughts during July to September. In the Interior Mysore South, June and September rainfall have significant effect on  yield. By testing the formulae for the yields for 1965 to 1968, it is found that they agree well with the reported yields. All correlation coefficients obtained are significant at 0.1 per cent level.

scholarly journals The association between COVID-19 data and meteorological factors in Indonesia

2020 ◽  
Author(s):  
Supari ◽  
Danang Eko Nuryanto ◽  
Amsari Muzakir Setiawan ◽  
Ardhasena Sopaheluwakan ◽  
Furqon AlFahmi ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Relative Humidity ◽  
Meteorological Factors ◽  
Correlation Coefficients ◽  
Weather Conditions ◽  
Weather Data ◽  
Data Series ◽  
Distribution Analysis ◽  
Cumulative Number ◽  
Daily Data

Abstract On March 2, 2020, the first Coronavirus Disease (COVID-19) case was reported in Jakarta, Indonesia. One and half month later (15/05/2020), the cumulative number of infection cases was 16496 with a total of 1076 mortalities. This study is aimed to investigate the possible role of weather in the early cases of COVID-19 incidence in six selected cities in Indonesia. Daily data of temperature and relative humidity from weather stations nearby each city were collected during the period 3 March - 30 April 2020, together with data of COVID-19 cases. Correlation tests and regression analysis were performed to examine the association of those two data series. In addition, we analysed the distribution of COVID-19 with respect to weather data to estimate the effective range of weather data supporting COVID-19 incidence. Our results reveal that weather data is generally associated with COVID-19 incidence. The daily average temperature (T-ave) and relative humidity (RH) presents significant positive and negative correlation with COVID-19 data, respectively. However, the correlation coefficients are weak with the strongest correlations found at 5 day lag time i.e. 0.37 (-0.41) for T-ave (RH). The regression analysis consistently confirmed this relation. The distribution analysis reveals that the majority of COVID-19 cases in Indonesia occurred in the daily temperature range of 25-31oC and relative humidity of 74-92%. Our findings suggest that COVID-19 incidence in Indonesia has a weak association with weather conditions. Therefore, non-meteorological factors seem to play a larger role and should be given greater consideration in preventing the spread of COVID-19.

scholarly journals Seasonality of Non-SARS, Non-MERS Coronaviruses and the Impact of Meteorological Factors

Pathogens ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 187
Author(s):  
Olympia E. Anastasiou ◽  
Anika Hüsing ◽  
Johannes Korth ◽  
Fotis Theodoropoulos ◽  
Christian Taube ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Relative Humidity ◽  
Cloud Cover ◽  
Detection Rate ◽  
Logistic Regression Model ◽  
Meteorological Factors ◽  
Seasonal Pattern ◽  
Weather Factors ◽  
Immunosuppressed Patients ◽  
Immunocompetent Patients

Background: Seasonality is a characteristic of some respiratory viruses. The aim of our study was to evaluate the seasonality and the potential effects of different meteorological factors on the detection rate of the non-SARS coronavirus detection by PCR. Methods: We performed a retrospective analysis of 12,763 respiratory tract sample results (288 positive and 12,475 negative) for non-SARS, non-MERS coronaviruses (NL63, 229E, OC43, HKU1). The effect of seven single weather factors on the coronavirus detection rate was fitted in a logistic regression model with and without adjusting for other weather factors. Results: Coronavirus infections followed a seasonal pattern peaking from December to March and plunged from July to September. The seasonal effect was less pronounced in immunosuppressed patients compared to immunocompetent patients. Different automatic variable selection processes agreed on selecting the predictors temperature, relative humidity, cloud cover and precipitation as remaining predictors in the multivariable logistic regression model, including all weather factors, with low ambient temperature, low relative humidity, high cloud cover and high precipitation being linked to increased coronavirus detection rates. Conclusions: Coronavirus infections followed a seasonal pattern, which was more pronounced in immunocompetent patients compared to immunosuppressed patients. Several meteorological factors were associated with the coronavirus detection rate. However, when mutually adjusting for all weather factors, only temperature, relative humidity, precipitation and cloud cover contributed independently to predicting the coronavirus detection rate.

scholarly journals Meteorological factors and its association with hand, foot and mouth disease in Southeast and East Asia areas: a meta-analysis

2018 ◽  
Vol 147 ◽  
Author(s):  
Chunxiao Duan ◽  
Xuefeng Zhang ◽  
Hui Jin ◽  
Xiaoqing Cheng ◽  
Donglei Wang ◽  
...  
Keyword(s):  
East Asia ◽  
Meteorological Factors ◽  
Global Climate ◽  
Meta Analysis ◽  
Foot And Mouth Disease ◽  
Correlation Coefficients ◽  
Mouth Disease ◽  
Asia Pacific ◽  
China National Knowledge Infrastructure ◽  
Foot And Mouth

AbstractSince the late 1990s, hand, foot and mouth disease (HFMD) has become a common health problem that mostly affects children and infants in Southeast and East Asia. Global climate change is considered to be one of the major risk factors for HFMD. This study aimed to assess the correlation between meteorological factors and HFMD in the Asia-Pacific region. PubMed, Web of Science, Embase, China National Knowledge Infrastructure, Wanfang Data and Weipu Database were searched to identify relevant articles published before May 2018. Data were collected and analysed using R software. We searched 2397 articles and identified 51 eligible papers in this study. The present study included eight meteorological factors; mean temperature, mean highest temperature, mean lowest temperature, rainfall, relative humidity and hours of sunshine were positively correlated with HFMD, with correlation coefficients (CORs) of 0.52 (95% confidence interval (CI) 0.42–0.60), 0.43 (95% CI 0.23–0.59), 0.43 (95% CI 0.23–0.60), 0.27 (95% CI 0.19–0.35), 0.19 (95% CI 0.02–0.35) and 0.19 (95% CI 0.11–0.27), respectively. There were sufficient data to support a negative correlation between mean pressure and HFMD (COR = −0.51, 95% CI −0.63 to −0.36). There was no notable correlation with wind speed (COR = 0.10, 95% CI −0.03 to 0.23). Our findings suggest that meteorological factors affect the incidence of HFMD to a certain extent.

scholarly journals The Comorbidity of Psychopathy and Depression: Across Different Ethnic and Sex Groups

2017 ◽  
Vol 4 (3) ◽  
Author(s):  
Dr. Zlatko Šram
Keyword(s):  
Regression Analysis ◽  
Adult Population ◽  
Community Sample ◽  
Correlation Coefficients ◽  
Convenience Sample ◽  
Positive Direction ◽  
Demographic Groups ◽  
Secondary Psychopathy ◽  
Linear Relationships ◽  
Primary And Secondary Psychopathy

The aim of this research was to examine if comorbid relationships exist between psychopathy and depression in a community sample of different ethnic and sex groups. Based on some previous research, it was hypothesized that psychopathy and depression would be correlated and that secondary psychopathy would be the strongest predictor of depression regardless of different ethnic and sex belongings. The survey was carried out on the adult population in the region of Croatia populated by citizens of Croatian and Serbian minority ethnicity. The equalized convenience sample of 1100 participants, half of which were Croats and half of males. Pearson-product moment correlation coefficients were calculated as a measure of the strength and direction of linear relationships among primary and secondary and depression. In order to determine how well scores on depression could be predicted by primary and secondary psychopathy across different demographic groups, multiple regression analysis were used. It was found that both primary and secondary psychopathy were significantly correlated in a positive direction with depression in different ethnic and sex groups. However, secondary psychopathy was more correlated with depression across different ethnic and sex subsamples. The results of regression analysis revealed that secondary psychopathy was the strongest predictor of depression in all demographic subsamples. After age and school attainment were introduced into regression models, it was shown that a very small percentage of the variance is explained by the sociodemographic variables. The research suggested a significant role of secondary psychopathy in relation to a higher level of psychopathology.

scholarly journals METHODOLOGICAL PROBLEMS OF THE MATHEMATICAL ESTIMATION OF THE IMPACT OF METEOROLOGICAL FACTORS ON ADAPTIVE FUNCTIONS OF THE HUMAN

2019 ◽  
Vol 96 (3) ◽  
pp. 253-257
Author(s):  
Nurlan K. Smagulov ◽  
A. A. Adilbekova
Keyword(s):  
High School ◽  
Mathematical Analysis ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Correlation Coefficients ◽  
Dose Effect ◽  
Output Factors ◽  
The Central Nervous System ◽  
Methodological Problems ◽  
The Impact

The work is dedicated to methodological problems of the mathematical assessment of the impact of meteorological factors on the adaptive function of the teachers of the High School Institutions. Objects of research. Male teachers of the High School Institution, aged of from 24 to 49 years. Meteorological data were evaluated during the investigation of anthropometric indices of height and weight, individual-typological features and the physiological assessment of the central nervous system, cardiovascular system of the High School teachers. Statistical analysis was performed with the use of Statistica 6.0 package and special statistical software. Paired correlation coefficients obtained as a result of calculation were used to estimate the proportion of the influence of input factors (arguments) on the output factors (functions). A mathematical analysis has allowed to reveal leading meteorological factors that cause a certain level of functional exhaustion during the investigated period. The use of a non-linear correlation analysis allowed to enhance significantly the ability for analytical processing of the results, increase of the effectiveness and the possibility of interpreting the interaction of factors in achieving optimal adaptation of teachers in the working process and to identify the role of meteorological factors in this process. Knowledge of leading factors and the percentage of their contribution to the functional state allowed to give the more accurate assessment of stress to the organism in specific circumstances. The ultimate aim of the mathematical analysis should be not only to find the critical value defined the priority factor characterizing the degree of of information load, but the critical combination of all priority factors causing disruption and the beginning of “start-up” adaptation process in the system “dose-effect. “

scholarly journals SARS-CoV-2 and the Weather: Correlation between COVID-19 and Meteorological Variables in 3 Cities in Mexico

2021 ◽  
pp. 74-91
Author(s):  
Hermes Ulises Ramirez-Sanchez ◽  
Alma Delia Ortiz-Bañuelos ◽  
Aida Lucia Fajardo-Montiel
Keyword(s):  
Wind Speed ◽  
Atmospheric Pressure ◽  
Ecological Study ◽  
Metropolitan Areas ◽  
Meteorological Factors ◽  
Correlation Coefficients ◽  
Weather Conditions ◽  
Dew Point ◽  
Meteorological Variables ◽  
Infected Person

Meteorological factors such as temperature, humidity, atmospheric pressure, wind speed and direction are associated with the dispersion of the SARS-CoV-2 virus through aerosols, particles <5μm are suspended in the air being infective at least three hours and dispersing from eight to ten meters. It has been shown that a 10-minute conversation, an infected person produces up to 6000 aerosol particles, which remain in the air from minutes to hours, depending on the prevailing weather conditions. Objective: To establish the correlation between meteorological variables, confirmed cases and deaths from COVID-19 in the 3 most important cities of Mexico. Methodology: A retrospective ecological study was conducted to evaluate the correlation of meteorological factors with COVID-19 cases and deaths in three Mexican cities. Results: The correlations between health and meteorological variables show that in the CDMX the meteorological variables that best correlate with the health variables are Temperature (T), Dew Point (DP), Wind speed (WS), Atmospheric Pressure (AP) and Relative Humidity (RH) in that order. In the ZMG are T, WS, RH, DP and AP; and in the ZMM are RH, WS, DP, T and AP. Conclusions In the 3 Metropolitan Areas showed that the meteorological factors that best correlate with the confirmed cases and deaths from COVID-19 are the T, RH; however, the correlation coefficients are low, so their association with health variables is less than other factors such as social distancing, hand washing, use of antibacterial gel and use of masks.

DEVELOPMENT OF REGIONAL MODELS OF CREATIVE INDUSTRIES ON THE BASIS OF CORRELATION-REGRESSION ANALYSIS

2021 ◽  
Vol 9 (1) ◽  
pp. 27-34
Author(s):  
Maksym Voichuk ◽  
Yuliia Zavadska
Keyword(s):  
Regression Analysis ◽  
Analysis Of Variance ◽  
Integrated Assessment ◽  
Correlation Coefficients ◽  
Regional Model ◽  
Creative Industries ◽  
Regional Models ◽  
Multiple Correlation ◽  
Economic Mechanisms ◽  
Paired Correlation

The article considers the importance of research and functioning of creative industries and substantiates the stages of integrated assessment of their development. One of the methodologi-cal approaches to the study of creative industries is considered namely correlation-regression analysis. The necessity of application of multiple correlation-regression analysis for estimation of the interdependence of certain creative indicators with their creative parameters is opened and the regional model of development of creative industries is constructed. The study is based on calculations and formulas, the values of which are used in the construction of a matrix of paired correlation coefficients and other important components. Based on the study, regression statistics and analysis of variance. The paper provides all possible indicators for the develop-ment of an organizational and economic mechanisms for building regional models of creative industries.

scholarly journals Citius, Altius, Fortius; Is It Enough to Achieve Success in Basketball?

2020 ◽  
Vol 17 (20) ◽  
pp. 7355
Author(s):  
Javier García-Rubio ◽  
Daniel Carreras ◽  
Sebastian Feu ◽  
Antonio Antunez ◽  
Sergio J. Ibáñez
Keyword(s):  
Regression Analysis ◽  
Performance Indicators ◽  
Vertical Jump ◽  
Correlation Coefficients ◽  
Anthropometric Measurements ◽  
Combine Test ◽  
Future Performance ◽  
Correlation Analyses ◽  
Pearson's Correlation ◽  
Game Performance

The NBA Draft Combine includes a series of standardized measurements and drills that provide NBA teams with an opportunity to evaluate players. The purpose of this research was to identify the Combine tests that explain draft position and future performance in the NBA rookie season. Variables were selected from the previous categories of anthropometric measurements and strength and agility tests. A regression analysis was carried out. Combine variables, anthropometric and agility/strength variables were analyzed to explore their effect on draft position. Moreover, correlation analyses were performed to identify relationships among: (i) Combine anthropometric and strength and agility measures and game performance through game related statistics; and (ii) the draft position and game performance using Pearson’s correlation coefficients. Results show that the Combine test does not predict draft position, with the exception of hand width and height in frontcourt players, and standard vertical jump and running vertical jump. Future performance indicators were explained by several Combine tests in all players.

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