scholarly journals Construction a map of geochemical anomalies for the predicted value of gold in the Lidin ore field of Urup Island

2021 ◽  
Vol 22 (2) ◽  
pp. 234-240
Author(s):  
Yuri M. Panasenko ◽  
Vladimir E. Markov ◽  
Elena V. Karelina
Keyword(s):  
Multiple Regression ◽  
Regression Equation ◽  
Kuril Islands ◽  
Uneven Distribution ◽  
Multiple Regression Equation ◽  
Sampling Point ◽  
Quaternary Sediments ◽  
Geostatistical Methods ◽  
Geochemical Anomalies

The purpose of this article is to study the methods of constructing maps of geochemical anomalies using geostatistical methods. The example presented in the article shows the possibility of probabilistic determination of gold in strongly overlapped areas (Urup Island, Kuril Islands). On the site of the Kolenchaty Lidinskoye ore field, the gold ore occurrence of the Kolenchaty is known. It is timed to the incision of the river and disappears outside of it. This is due to the strong overlap of the area by Quaternary sediments. Together with the uneven distribution of gold, these circumstances complicate the construction of maps of geochemical anomalies and the identification of promising areas. To solve this problem, the Statistica program was used. With its help, a matrix of correlations was built, elements of the satellites of gold were selected, and a multiple regression equation for gold was drawn up. This equation was used to probabilistically calculate gold values at each sampling point. In ArcGis, Geostatistical Analyst, geochemical anomalies were mapped from the predicted gold value by kriging, and promising areas were highlighted.

Carcass length in the bacon pig; its association with vertebrae numbers and prediction from radiographs of the young pig

1960 ◽  
Vol 2 (01) ◽  
pp. 59-65 ◽  
Author(s):  
J. W. B. King ◽  
R. C. Roberts
Keyword(s):  
Multiple Regression ◽  
Cervical Vertebrae ◽  
Lumbar Vertebrae ◽  
Regression Equation ◽  
Multiple Regression Equation ◽  
Large White ◽  
Independent Variables ◽  
Accuracy Of Prediction ◽  
Improved Accuracy

Vertebrae counts made on 504 Large White and 550 (Wessex × Large White) pigs showed an association with carcass length but not with other carcass traits. For each additional vertebra there was an increase in length of approximately 15 mm.Another group of 313 piglets from 36 litters were X-rayed at around 9 days of age in order to predict their ultimate carcass length. After correction for sex and variations in weight at slaughter it was found that the number of thoracic and lumbar vertebrae together accounted for 14% of the remaining variation in carcass length. Improved accuracy of prediction, accounting for 29% of the variation was obtained with a multiple regression equation using as independent variables: the skeletal length of the young pig as measured on its radiograph, its weight at birth and X-raying and the number of lumbar vertebrae. From measurements made on the carcass it i s suggested that X-raying of the bacon pig to measure the length of the first four lumbar vertebrae, would, in conjunction with an earlier determination of vertebrae number, enable a much improved prediction of carcass length to be made.The incidence of various vertebral variations is noted and in particular the occurrence of one pig with only six cervical vertebrae.

scholarly journals Correlation of 3-Dimensional Shoulder Kinematics to Function in Subjects With Idiopathic Loss of Shoulder Range of Motion

2005 ◽  
Vol 85 (7) ◽  
pp. 636-647 ◽  
Author(s):  
Peter J Rundquist ◽  
Paula M Ludewig
Keyword(s):  
Range Of Motion ◽  
Multiple Regression ◽  
External Rotation ◽  
Regression Equation ◽  
Multiple Regression Equation ◽  
3 Dimensional ◽  
Shoulder Range Of Motion ◽  
Shoulder Kinematics ◽  
And Function ◽  
Shoulder Range

AbstractBackground and Purpose. People with idiopathic loss of shoulder range of motion (ROM) have difficulty completing activities of daily living. This investigation was performed to determine the association between active glenohumeral ROM and function and to develop a multiple regression equation to explain variation in function in people with idiopathic loss of shoulder motion. Subjects and Methods. This was a comparative study of 21 subjects (18 female, 3 male), using measurements of shoulder kinematics and administration of the Shoulder Rating Questionnaire (SRQ). Electromagnetic tracking sensors monitored the 3-dimensional position of the scapula and humerus throughout active shoulder motions. Correlations were performed between the active ROMs of interest and various demographic factors and the SRQ. A multiple regression equation was generated. Results. A multiple regression equation including scapular-plane abduction, external rotation at the side, external rotation at 90 degrees of abduction, and weight explained 69% of the variation in the SRQ scores. Discussion and Conclusion. The results suggest that active ROM can be used to predict function in people with idiopathic loss of shoulder ROM.

Analysis of Cloud Seeding Efforts in the Tri-County Area,* Oregon, 1950–54

1957 ◽  
Vol 38 (3) ◽  
pp. 134-137 ◽  
Author(s):  
Fred W. Decker ◽  
Russell L. Lincoln ◽  
John A. Day
Keyword(s):  
Multiple Regression ◽  
Regression Equation ◽  
Multiple Regression Equation ◽  
Cloud Seeding ◽  
North Central ◽  
Natural Rainfall ◽  
Cent Level

Using an 11-variable multiple regression equation to compute the “natural” rainfall, it is concluded that commercial cloud seeding during 1950–54 in three north central Oregon counties failed to produce departures in precipitation which are statistically significant at the 5 per cent level of acceptance.

The Use of Econometric Analysis in Forecasting Macroeconomic Processes

2021 ◽  
pp. 28-33
Author(s):  
Aleksandr Kalyanov ◽  
Andrey Shishkin
Keyword(s):  
Multiple Regression ◽  
Econometric Model ◽  
Econometric Analysis ◽  
Regression Equation ◽  
Multiple Regression Equation ◽  
Linear Multiple Regression ◽  
Consumer Prices ◽  
Macroeconomic Indicators ◽  
Primary Impact ◽  
Selection Of

The article discusses the issues related to the influence of various price categories of goods on the level of inflation. The possibility of using econometric analysis for predictive purposes is considered. An econometric model of multiple regression of the influence of consumer prices on the level of inflation is formed. A linear multiple regression equation is constructed. The selection of factors for the construction of an econometric model is made. The main groups of goods are identified, the prices of which form the level of inflation and can have a primary impact. The viability of the model and the possibility of forecasting macroeconomic indicators based on econometric analysis are proved.

Variation in nutrient content of feedingstuffs rich in protein and reassessment of the chemical method for metabolizable energy estimation for poultry

1976 ◽  
Vol 86 (2) ◽  
pp. 293-303 ◽  
Author(s):  
G. N. Lodhi ◽  
Daulat Singh ◽  
J. S. Ichhponani
Keyword(s):  
Multiple Regression ◽  
Crude Protein ◽  
Regression Equation ◽  
Metabolizable Energy ◽  
Multiple Regression Equation ◽  
Regression Equations ◽  
Ether Extract ◽  
Crude Fibre ◽  
Energy Estimation ◽  
Short Period

SummaryA series of five metabolism trials was made to determine apparent nitrogen digestibility and metabolizable energy (ME) contents of protein rich feedingstuffs. The mean nitrogen digestibilities of fish meal, groundnut, mustard, sesame and cottonseed cakes were 66, 69, 68, 57 and 40%, respectively. Corresponding values for metabolizable energy values were 1820, 2460, 2330, 1870 and 1530 kcal/kg, respectively. The metabolizable energy contents of coconut cake, niger cake and blood meal were 1190, 2360 and 2190 kcal/kg, respectively. The quantity of protein, its digestibility and crude fibre content in the cakes are the prime factors for this trend in MB. Simple and multiple regression equations were derived from biologically assayed metabolizable energy and chemically analysed energy-yielding nutrient contents of the feedingstuffs. The simple regression equation is:ME kcal/kg = 32·95 (% crude protein + % ether extract × 2·25+ % available carbohydrate)–29·20.The multiple regression equation is:ME kcal/kg = 370·29 + (24·47 × % crude protein)+ (65·77 × % ether extract)+ (44·07 × % available carbohydrate)- (8·15 × % crude fibre).The correlation coefficients of simple and multiple regression equations were 0·72 and 0·73, respectively, indicating that there is very little advantage for prediction in using the multiple regression equation. The usefulness of the equation for routine checking of poultry feeds for ME is apparent since the nutrients required to predict metabolizable energy can be analysed within a short period of time.

scholarly journals A Multiple Regression Equation for Prediction of Posthepatectomy Liver Failure

1984 ◽  
Vol 200 (5) ◽  
pp. 658-663 ◽  
Author(s):  
NAOKI YAMANAKA ◽  
EIZO OKAMOTO ◽  
KEIJI KUWATA ◽  
NOBUTAKA TANAKA
Keyword(s):  
Liver Failure ◽  
Multiple Regression ◽  
Regression Equation ◽  
Multiple Regression Equation ◽  
Posthepatectomy Liver Failure
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