scholarly journals Prediction of winter minimum temperature of Kolkata using statistical model

MAUSAM ◽  
2021 ◽  
Vol 57 (3) ◽  
pp. 451-458
Author(s):  
TAPAN KANTI CHAKRABORTY
Keyword(s):  
Linear Regression ◽  
Minimum Temperature ◽  
Cloud Cover ◽  
Multiple Linear Regression Model ◽  
Skill Score ◽  
Temperature Data ◽  
Wind Flow ◽  
Surface Data ◽  
Winter Minimum Temperature ◽  
Stationarity Test

Lkkj & dksydkrk ¼vfyiqj½ ds U;wure rkieku dk okLrfod iwokZuqeku 12 ?kaVs iwoZ tkjh djus ds mn~ns’; ls ik¡p izkpyksa ij vk/kkfjr cgq jSf[kd lekJ;.k ekWMy fodflr fd;k x;k gS A blds iwoZ lwpdksa dk p;u vfyiqj os/k’kkyk ls izkIr lrg vk¡dM+ksa rFkk ekSle dk;kZy; dksydkrk ds fuEu Lrj ds iou vk¡dM+ksa ds vk/kkj ij fd;k x;k gS A ;g ekWMy 237 fnuksa ds ¼o"kZ 1997&2000 dh vof/k ds tuojh ,oa Qjojh ekg ds½ vk¡dM+ksa ds uewuksa rFkk dkQh yach vof/k ¼o"kZ 1988&2004½ ds U;wure rkieku ds vk¡dM+ksa es fLFkjrk dh tk¡p ds vk/kkj ij fodflr fd;k x;k gS A bl ekWMy dh tk¡p 178 fnuksa ds vk¡dM+ksa ds Lora= uewus ds vk/kkj ij dh xbZ gS A bl ekWMy dh {kerk dh tk¡p lkaf[;dh; vk¡dM+ksa ds vk/kkj ij dh xbZ gS vkSj bls ldkjkRed ik;k x;k gSA bl ekWMy dk mi;ksx ekSle iwokZuqekudRrkZ }kjk U;wure rkieku ds iwokZuqeku dk vkdyu djus ds fy, fd;k tk ldrk gS vkSj ;fn ckny rFkk iou dh xfr ds :[k esa ckn esa ifjorZu laHkkfor gks rks mlesa lq/kkj fd;k tk ldrk gS A  Five parameter multiple linear regression model for objective forecasting of minimum temperature of Kolkata (Alipore) with 12 hours lead period has been developed. The predictors are chosen from the available surface data of Alipore observatory and low level wind data of M. O. Kolkata. Model has been developed from data sample comprising of 237 days (in January and February, period: 1997 – 2000) after stationarity test of minimum temperature data of much longer period (1988–2004). The model is tested with independent sample of 178 days. Efficiencies of the model have been tested with statistical skill score and found to be positive. The model can be used by the forecaster for assessing prediction minimum temperature and modify if cloud cover and wind flow pattern are expected to change subsequently.  

scholarly journals The Use of Multiple Linear Regression Model for Predicting The Influence of Weather to The Prevalence of Newcastle Disease in Chicken in North Aceh

2008 ◽  
Vol 2 (1) ◽  
Author(s):  
Mahdi Abrar
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Newcastle Disease ◽  
Linear Regression Model ◽  
Minimum Temperature ◽  
Multiple Linear Regression Model ◽  
Maximum Temperature ◽  
Independent Variables ◽  
Dependent Variables

The objective of this research is to see the influence of weather on the prevalence of Newcastle Disease (ND) in chicken in Kabupaten Aceh Utara (North Aceh). Data used in this research were obtained from Dinas Peternakan North Aceh for the number of chicken suffered ND and from Badan Meteorologi dan Geofisika Lhokseumawe, North Aceh for the form of weather. Multiple Linear Regression Model with five independent variables (the average of rainfall per month, the average of maximum temperature, the average of minimum temperature, the velocity of the wind, and the average of humidity per month) was used to see the influence of wheather to the prevalence of Newcastle Disease. Proportion the number of chicken suffered from ND which is the ratio of the number of chicken suffered from ND to the total number of chicken was used as dependent variables. The result shows that the best model is Ŷ= 120.529278 – 1.33 x wind humidity + 1.907 x wind velocity.

Folate Nutritional Status among Psoriasis Patients not Exposed to Antifolate Drug

2020 ◽  
Vol 16 (4) ◽  
pp. 543-553
Author(s):  
Luciana Y. Tomita ◽  
Andréia C. da Costa ◽  
Solange Andreoni ◽  
Luiza K.M. Oyafuso ◽  
Vânia D’Almeida ◽  
...  
Keyword(s):  
Folic Acid ◽  
Linear Regression ◽  
Multiple Linear Regression Model ◽  
Folate Intake ◽  
Serum Folate ◽  
Linear Regression Models ◽  
Cross Sectional ◽  
Folic Acid Fortification ◽  
Plasma Folate ◽  
Psoriasis Severity

Background: Folic acid fortification program has been established to prevent tube defects. However, concern has been raised among patients using anti-folate drug, i.e. psoriatic patients, a common, chronic, autoimmune inflammatory skin disease associated with obesity and smoking. Objective: To investigate dietary and circulating folate, vitamin B12 (B12) and homocysteine (hcy) in psoriatic subjects exposed to the national mandatory folic acid fortification program. Methods: Cross-sectional study using the Food Frequency Questionnaire, plasma folate, B12, hcy and psoriasis severity using the Psoriasis Area and Severity Index score. Median, interquartile ranges (IQRs) and linear regression models were conducted to investigate factors associated with plasma folate, B12 and hcy. Results: 82 (73%) mild psoriasis, 18 (16%) moderate and 12 (11%) severe psoriasis. 58% female, 61% non-white, 31% former smokers, and 20% current smokers. Median (IQRs) were 51 (40, 60) years. Only 32% reached the Estimated Average Requirement of folate intake. Folate and B12 deficiencies were observed in 9% and 6% of the blood sample respectively, but hyperhomocysteinaemia in 21%. Severity of psoriasis was negatively correlated with folate and B12 concentrations. In a multiple linear regression model, folate intake contributed positively to 14% of serum folate, and negative predictors were psoriasis severity, smoking habits and saturated fatty acid explaining 29% of circulating folate. Conclusion: Only one third reached dietary intake of folate, but deficiencies of folate and B12 were low. Psoriasis severity was negatively correlated with circulating folate and B12. Stopping smoking and a folate rich diet may be important targets for managing psoriasis.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

Predictive Factors for Hamstring Autograft Diameter in Anterior Cruciate Ligament Reconstruction

2019 ◽  
Author(s):  
Willem M.P. Heijboer ◽  
Mathijs A.M. Suijkerbuijk ◽  
Belle L. van Meer ◽  
Eric W.P. Bakker ◽  
Duncan E. Meuffels
Keyword(s):  
Anterior Cruciate Ligament ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Acl Reconstruction ◽  
Cruciate Ligament ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Gracilis Tendon ◽  
Thigh Circumference ◽  
Anterior Cruciate

AbstractMultiple studies found hamstring tendon (HT) autograft diameter to be a risk factor for anterior cruciate ligament (ACL) reconstruction failure. This study aimed to determine which preoperative measurements are associated with HT autograft diameter in ACL reconstruction by directly comparing patient characteristics and cross-sectional area (CSA) measurement of the semitendinosus and gracilis tendon on magnetic resonance imaging (MRI). Fifty-three patients with a primary ACL reconstruction with a four-stranded HT autograft were included in this study. Preoperatively we recorded length, weight, thigh circumference, gender, age, preinjury Tegner activity score, and CSA of the semitendinosus and gracilis tendon on MRI. Total CSA on MRI, weight, height, gender, and thigh circumference were all significantly correlated with HT autograft diameter (p < 0.05). A multiple linear regression model with CSA measurement of the HTs on MRI, weight, and height showed the most explained variance of HT autograft diameter (adjusted R 2 = 44%). A regression equation was derived for an estimation of the expected intraoperative HT autograft diameter: 1.2508 + 0.0400 × total CSA (mm2) + 0.0100 × weight (kg) + 0.0296 × length (cm). The Bland and Altman analysis indicated a 95% limit of agreement of ± 1.14 mm and an error correlation of r = 0.47. Smaller CSA of the semitendinosus and gracilis tendon on MRI, shorter stature, lower weight, smaller thigh circumference, and female gender are associated with a smaller four-stranded HT autograft diameter in ACL reconstruction. Multiple linear regression analysis indicated that the combination of MRI CSA measurement, weight, and height is the strongest predictor.

scholarly journals Magnetization Transfer Ratio of Peripheral Nerve and Skeletal Muscle

2021 ◽  
Author(s):  
Olivia Fösleitner ◽  
Véronique Schwehr ◽  
Tim Godel ◽  
Fabian Preisner ◽  
Philipp Bäumer ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Body Weight ◽  
Sciatic Nerve ◽  
Linear Regression ◽  
Peripheral Nerve ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Magnetization Transfer ◽  
Multiple Linear Regression Model

Abstract Purpose To assess the correlation of peripheral nerve and skeletal muscle magnetization transfer ratio (MTR) with demographic variables. Methods In this study 59 healthy adults evenly distributed across 6 decades (mean age 50.5 years ±17.1, 29 women) underwent magnetization transfer imaging and high-resolution T2-weighted imaging of the sciatic nerve at 3 T. Mean sciatic nerve MTR as well as MTR of biceps femoris and vastus lateralis muscles were calculated based on manual segmentation on six representative slices. Correlations of MTR with age, body height, body weight, and body mass index (BMI) were expressed by Pearson coefficients. Best predictors for nerve and muscle MTR were determined using a multiple linear regression model with forward variable selection and fivefold cross-validation. Results Sciatic nerve MTR showed significant negative correlations with age (r = −0.47, p < 0.001), BMI (r = −0.44, p < 0.001), and body weight (r = −0.36, p = 0.006) but not with body height (p = 0.55). The multiple linear regression model determined age and BMI as best predictors for nerve MTR (R2 = 0.40). The MTR values were different between nerve and muscle tissue (p < 0.0001), but similar between muscles. Muscle MTR was associated with BMI (r = −0.46, p < 0.001 and r = −0.40, p = 0.002) and body weight (r = −0.36, p = 0.005 and r = −0.28, p = 0.035). The BMI was selected as best predictor for mean muscle MTR in the multiple linear regression model (R2 = 0.26). Conclusion Peripheral nerve MTR decreases with higher age and BMI. Studies that assess peripheral nerve MTR should consider age and BMI effects. Skeletal muscle MTR is primarily associated with BMI but overall less dependent on demographic variables.

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