The Experts below are selected from a list of 40209 Experts worldwide ranked by ideXlab platform
Michael D Larsen - One of the best experts on this subject based on the ideXlab platform.
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a simple method of sample size calculation for linear and logistic regression
Statistics in Medicine, 1998Co-Authors: F Y Hsieh, Daniel A Bloch, Michael D LarsenAbstract:A sample size calculation for logistic regression involves complicated formulae. This paper suggests use of sample size formulae for comparing means or for comparing proportions in order to calculate the required sample size for a simple logistic regression model. One can then adjust the required sample size for a multiple logistic regression model by a Variance Inflation Factor. This method requires no assumption of low response probability in the logistic model as in a previous publication. One can similarly calculate the sample size for linear regression models. This paper also compares the accuracy of some existing sample-size software for logistic regression with computer power simulations. An example illustrates the methods.
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a simple method of sample size calculation for linear and logistic regression
Statistics in Medicine, 1998Co-Authors: F Y Hsieh, Daniel A Bloch, Michael D LarsenAbstract:SUMMARY A sample size calculation for logistic regression involves complicated formulae. This paper suggests use of sample size formulae for comparing means or for comparing proportions in order to calculate the required sample size for a simple logistic regression model. One can then adjust the required sample size for a multiple logistic regression model by a Variance Inflation Factor. This method requires no assumption of low response probability in the logistic model as in a previous publication. One can similarly calculate the sample size for linear regression models. This paper also compares the accuracy of some existing sample-size software for logistic regression with computer power simulations. An example illustrates the methods. ( 1998 John Wiley & Sons, Ltd.
Axel Moehrenschlager - One of the best experts on this subject based on the ideXlab platform.
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Annual Northern leopard frog occupancy trends from 2009–2013 in spring (20 April–02 June) and summer (21 July–30 August) in southern Alberta.
2015Co-Authors: Lea A. Randall, Des H. V. Smith, Breana L. Jones, David R. C. Prescott, Axel MoehrenschlagerAbstract:-2 LL = -2 log likelihood; K = number of parameters; AIC = Akaike Information Criterion; wi = Akaike weight; ψ = occupancy; ɣ = colonization; ε = extinction. Variance Inflation Factor ĉ = 1.Annual Northern leopard frog occupancy trends from 2009–2013 in spring (20 April–02 June) and summer (21 July–30 August) in southern Alberta.
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Seasonal and annual Northern leopard frog occupancy dynamics in southern Alberta from 2009–2013 spring (20 April–02 June) and summer (21 July–30 August) visual surveys.
2015Co-Authors: Lea A. Randall, Des H. V. Smith, Breana L. Jones, David R. C. Prescott, Axel MoehrenschlagerAbstract:-2 LL = -2 log likelihood; K = number of parameters; AIC = Akaike Information Criterion; wi = Akaike weight; ψ = occupancy; ɣ = colonization. Variance Inflation Factor ĉ = 1.Seasonal and annual Northern leopard frog occupancy dynamics in southern Alberta from 2009–2013 spring (20 April–02 June) and summer (21 July–30 August) visual surveys.
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Annual Northern leopard frog occupancy trends with site selection criteria (SSC) from 2009–2013 in spring (20 April–02 June) and summer (21 July–30 August) in southern Alberta.
2015Co-Authors: Lea A. Randall, Des H. V. Smith, Breana L. Jones, David R. C. Prescott, Axel MoehrenschlagerAbstract:-2 LL = -2 log likelihood; K = number of parameters; AIC = Akaike Information Criterion; wi = Akaike weight; ψ = occupancy; ɣ = colonization; ε = extinction. Variance Inflation Factor ĉ = 1.Annual Northern leopard frog occupancy trends with site selection criteria (SSC) from 2009–2013 in spring (20 April–02 June) and summer (21 July–30 August) in southern Alberta.
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Top five Northern leopard frog detection probability models for 2009–2013 spring (20 April–02 June) or summer (21 July–30 August) visual surveys in southern Alberta.
2015Co-Authors: Lea A. Randall, Des H. V. Smith, Breana L. Jones, David R. C. Prescott, Axel MoehrenschlagerAbstract:-2 LL = -2 log likelihood; K = number of parameters; AIC = Akaike information criterion; wi = Akaike weights; p = probability of detection; wind chill (Wchill); air temperature (AT); maximum (Wmax) and average (Wavg) wind speed; water surface temperature (WT); humidity (HM); Julian date of survey (JD). Variance Inflation Factor ĉ = 1.Top five Northern leopard frog detection probability models for 2009–2013 spring (20 April–02 June) or summer (21 July–30 August) visual surveys in southern Alberta.
F Y Hsieh - One of the best experts on this subject based on the ideXlab platform.
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a simple method of sample size calculation for linear and logistic regression
Statistics in Medicine, 1998Co-Authors: F Y Hsieh, Daniel A Bloch, Michael D LarsenAbstract:A sample size calculation for logistic regression involves complicated formulae. This paper suggests use of sample size formulae for comparing means or for comparing proportions in order to calculate the required sample size for a simple logistic regression model. One can then adjust the required sample size for a multiple logistic regression model by a Variance Inflation Factor. This method requires no assumption of low response probability in the logistic model as in a previous publication. One can similarly calculate the sample size for linear regression models. This paper also compares the accuracy of some existing sample-size software for logistic regression with computer power simulations. An example illustrates the methods.
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a simple method of sample size calculation for linear and logistic regression
Statistics in Medicine, 1998Co-Authors: F Y Hsieh, Daniel A Bloch, Michael D LarsenAbstract:SUMMARY A sample size calculation for logistic regression involves complicated formulae. This paper suggests use of sample size formulae for comparing means or for comparing proportions in order to calculate the required sample size for a simple logistic regression model. One can then adjust the required sample size for a multiple logistic regression model by a Variance Inflation Factor. This method requires no assumption of low response probability in the logistic model as in a previous publication. One can similarly calculate the sample size for linear regression models. This paper also compares the accuracy of some existing sample-size software for logistic regression with computer power simulations. An example illustrates the methods. ( 1998 John Wiley & Sons, Ltd.
Lea A. Randall - One of the best experts on this subject based on the ideXlab platform.
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Annual Northern leopard frog occupancy trends from 2009–2013 in spring (20 April–02 June) and summer (21 July–30 August) in southern Alberta.
2015Co-Authors: Lea A. Randall, Des H. V. Smith, Breana L. Jones, David R. C. Prescott, Axel MoehrenschlagerAbstract:-2 LL = -2 log likelihood; K = number of parameters; AIC = Akaike Information Criterion; wi = Akaike weight; ψ = occupancy; ɣ = colonization; ε = extinction. Variance Inflation Factor ĉ = 1.Annual Northern leopard frog occupancy trends from 2009–2013 in spring (20 April–02 June) and summer (21 July–30 August) in southern Alberta.
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Seasonal and annual Northern leopard frog occupancy dynamics in southern Alberta from 2009–2013 spring (20 April–02 June) and summer (21 July–30 August) visual surveys.
2015Co-Authors: Lea A. Randall, Des H. V. Smith, Breana L. Jones, David R. C. Prescott, Axel MoehrenschlagerAbstract:-2 LL = -2 log likelihood; K = number of parameters; AIC = Akaike Information Criterion; wi = Akaike weight; ψ = occupancy; ɣ = colonization. Variance Inflation Factor ĉ = 1.Seasonal and annual Northern leopard frog occupancy dynamics in southern Alberta from 2009–2013 spring (20 April–02 June) and summer (21 July–30 August) visual surveys.
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Annual Northern leopard frog occupancy trends with site selection criteria (SSC) from 2009–2013 in spring (20 April–02 June) and summer (21 July–30 August) in southern Alberta.
2015Co-Authors: Lea A. Randall, Des H. V. Smith, Breana L. Jones, David R. C. Prescott, Axel MoehrenschlagerAbstract:-2 LL = -2 log likelihood; K = number of parameters; AIC = Akaike Information Criterion; wi = Akaike weight; ψ = occupancy; ɣ = colonization; ε = extinction. Variance Inflation Factor ĉ = 1.Annual Northern leopard frog occupancy trends with site selection criteria (SSC) from 2009–2013 in spring (20 April–02 June) and summer (21 July–30 August) in southern Alberta.
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Top five Northern leopard frog detection probability models for 2009–2013 spring (20 April–02 June) or summer (21 July–30 August) visual surveys in southern Alberta.
2015Co-Authors: Lea A. Randall, Des H. V. Smith, Breana L. Jones, David R. C. Prescott, Axel MoehrenschlagerAbstract:-2 LL = -2 log likelihood; K = number of parameters; AIC = Akaike information criterion; wi = Akaike weights; p = probability of detection; wind chill (Wchill); air temperature (AT); maximum (Wmax) and average (Wavg) wind speed; water surface temperature (WT); humidity (HM); Julian date of survey (JD). Variance Inflation Factor ĉ = 1.Top five Northern leopard frog detection probability models for 2009–2013 spring (20 April–02 June) or summer (21 July–30 August) visual surveys in southern Alberta.
Daniel A Bloch - One of the best experts on this subject based on the ideXlab platform.
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a simple method of sample size calculation for linear and logistic regression
Statistics in Medicine, 1998Co-Authors: F Y Hsieh, Daniel A Bloch, Michael D LarsenAbstract:A sample size calculation for logistic regression involves complicated formulae. This paper suggests use of sample size formulae for comparing means or for comparing proportions in order to calculate the required sample size for a simple logistic regression model. One can then adjust the required sample size for a multiple logistic regression model by a Variance Inflation Factor. This method requires no assumption of low response probability in the logistic model as in a previous publication. One can similarly calculate the sample size for linear regression models. This paper also compares the accuracy of some existing sample-size software for logistic regression with computer power simulations. An example illustrates the methods.
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a simple method of sample size calculation for linear and logistic regression
Statistics in Medicine, 1998Co-Authors: F Y Hsieh, Daniel A Bloch, Michael D LarsenAbstract:SUMMARY A sample size calculation for logistic regression involves complicated formulae. This paper suggests use of sample size formulae for comparing means or for comparing proportions in order to calculate the required sample size for a simple logistic regression model. One can then adjust the required sample size for a multiple logistic regression model by a Variance Inflation Factor. This method requires no assumption of low response probability in the logistic model as in a previous publication. One can similarly calculate the sample size for linear regression models. This paper also compares the accuracy of some existing sample-size software for logistic regression with computer power simulations. An example illustrates the methods. ( 1998 John Wiley & Sons, Ltd.
