The Estimation of a Lagged Regression Relation

Biometrika ◽  
1967 ◽  
Vol 54 (3/4) ◽  
pp. 409 ◽  
Author(s):  
E. J. Hannan
Keyword(s):  
Regression Relation

Calculations of rock matrix modulus based on a linear regression relation

2011 ◽  
Vol 8 (3) ◽  
pp. 155-162 ◽  
Author(s):  
Xi-Lei He ◽  
Zhen-Hua He ◽  
Rui-Liang Wang ◽  
Xu-Ben Wang ◽  
Lian Jiang
Keyword(s):  
Linear Regression ◽  
Rock Matrix ◽  
Regression Relation ◽  
Matrix Modulus

scholarly journals Estimation of Bearing Capacity of Foundation of Transmission Tower Using Cokriging with Regression Relation.

1997 ◽  
pp. 47-58
Author(s):  
Wataru Shiraki ◽  
Mitsuyoshi Tsunekuni ◽  
Manabu Matsushima ◽  
Noboru Yasuda
Keyword(s):  
Bearing Capacity ◽  
Transmission Tower ◽  
Regression Relation

Binary Regression Investigate on Composition Tempering Temperature and Hardness（HRC）of Common Steel

2011 ◽  
Vol 204-210 ◽  
pp. 1362-1365 ◽  
Author(s):  
Qing Hua Zou
Keyword(s):  
Mechanical Property ◽  
Regression Analysis ◽  
Thermal Treatment ◽  
Process Parameters ◽  
Treatment Process ◽  
Steel Grade ◽  
Binary Regression ◽  
Tempering Temperature ◽  
Regression Relation ◽  
Binary Regression Analysis

Establish the uniform regression relation expression for these three parameters including composition of over ten kinds of common steels, composition tempering temperature and hardness (HRC) of steel, which can accurately calculate the relative values of these three parameters of over ten kinds of common steels within the discussed scope. Binary regression analysis can be expanded to any Tempering temperature and each kind of steel grade, and the work can be fulfilled with computer. Furthermore express other thermal treatment process parameters and its corresponding mechanical property of each kind of steel with the uniform mathematic relation, so that related uniform regression relation expression can be used into the actual production process, and this is what the author will complete further.

Accumulated temperature and maize development in England

1976 ◽  
Vol 87 (3) ◽  
pp. 577-583 ◽  
Author(s):  
E. S. Bunting
Keyword(s):  
Effective Temperature ◽  
Dry Matter ◽  
Matter Content ◽  
Base Temperature ◽  
Dry Matter Content ◽  
Regression Relation ◽  
Accumulated Temperature

SummaryInformation is given on dates of sowing and mid-silk for Inra 200 in 40 experiments (two each year at Oxford, 1962–75, and at Cambridge, 1970–5), together with cumulative temperatures between these dates. Two methods of defining effective temperature for maize development have been considered, namely the method advocated by U.K. Meteorological Office with 10 °C as the base temperature (the Ag. Met. method) and that defined in Ontario units. The regression relation between accumulated temperature and dry-matter content of the grain during ripening has also been calculated, from data obtained in the years 1966–74, and assessments made of cumulative totals in Ag. Met. day °C and in Ontario units from the date of silking until the grain D.M. content is 40%, when the crop is considered suitable for ensiling, and from silking until grain D.M. content is 60%, when the crop may be considered ripe.Cumulative temperature totals were significantly more stable over experiments than time expressed in calendar days. Totals of Ontario units were less variable than totals of Ag. Met. day °C. Estimated requirements for Inra 200 were 405 day °C or 1380 Ontario units from sowing to mid-silk, 255 day °C or 800 Ontario units from silking to 40% D.M. in the grain (for silage crops), and 380 day °C or 1235 Ontario units from silking to 60% D.M. in the grain.

The estimation of a lagged regression relation

Biometrika ◽  
1967 ◽  
Vol 54 (3-4) ◽  
pp. 409-418 ◽  
Author(s):  
E. J. HANNAN
Keyword(s):  
Regression Relation
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