Results obtained for latency of SEFs using multiple linear regression analysis.

Results of the excluded variables according to the multiple linear regression analysis for burnout predictors.

Othman A. Alfuqaha (6499352)
Mahmoud Y. Alkawareek (309198)
Hussein S. Alsharah (6499355)

March 2019

Results of the excluded variables according to the multiple linear regression analysis for burnout p...

Results of the multi-variate regression analysis with quasi-least squares, adjusted for multiple comparisons.

Eleonora M. Lad (1889089)
Dibyendu Mukherjee (4840374)
Sandra S. Stinnett (4840368)
Scott W. Cousins (324993)
Guy G. Potter (320987)
James R. Burke (258157)
Sina Farsiu (3951851)
Heather E. Whitson (4840371)

February 2018

Results of the multi-variate regression analysis with quasi-least squares, adjusted for multiple ...

Results of generalized linear regression model analyses for factors significantly associated with the number of previous MDEs.

Margo de Jonge (5925116)
Claudi L. H. Bockting (130818)
Patricia van Oppen (94161)
Henricus L. Van (5925119)
Jaap Peen (83470)
Martijn J. Kikkert (5925122)
Jack J. M. Dekker (764011)

November 2018

Results of generalized linear regression model analyses for factors significantly associated with th...

Results obtained for intensity of SEFs using multiple linear regression analysis.

Chihiro Kitayama (13844178)
Eriya Shimada (13844181)
Hiroki Hihara (4112329)
Akitake Kanno (393126)
Nobukazu Nakasato (401401)
Yoshimichi Imai (503312)
Akimitsu Sato (13844184)
Ryuta Kawashima (146994)
Kaoru Igarashi (8164452)
Hiroyasu Kanetaka (724955)

September 2022

Results obtained for intensity of SEFs using multiple linear regression analysis.</p

Results of the multiple linear regression analysis of environmental factors affecting the EE.

Luis Carus (10125582)
Isabel Castillo (5740634)

August 2021

Results of the multiple linear regression analysis of environmental factors affecting the EE.</p

Results of the multiple linear regression analysis of environmental factors affectation of measured speed.

Luis Carus (10125582)
Isabel Castillo (5740634)

August 2021

Results of the multiple linear regression analysis of environmental factors affectation of measured ...

Results of the multiple stepwise linear regression using hyperspectral metrics.

Haiming Qin (782099)
Cheng Wang (102692)
Kaiguang Zhao (197563)
Xiaohuan Xi (5309420)

May 2018

Results of the multiple stepwise linear regression using hyperspectral metrics.</p

Results of the multiple time regression analysis experiment of the five CPP using the linear model.

Sofie Stalmans (443949)
Nathalie Bracke (443951)
Evelien Wynendaele (443950)
Bert Gevaert (443952)
Kathelijne Peremans (296815)
Christian Burvenich (443954)
Ingeborgh Polis (296823)
Bart De Spiegeleer (443955)

October 2015

Results of the multiple time regression analysis experiment of the five CPP using the linear mode...

Results of multiple linear regression for predicting number of non-doubt answers.

Jef Vanderoost (5108072)
Rianne Janssen (5811212)
Jan Eggermont (29168)
Riet Callens (5811215)
Tinne De Laet (1369203)

October 2018

Results of multiple linear regression for predicting number of non-doubt answers.</p

Result of multiple linear regression analysis using the maximum IOP as a dependent variable.

Fumihiko Mabuchi (94451)
Nakako Mabuchi (4386163)
Yoichi Sakurada (94452)
Seigo Yoneyama (2503747)
Kenji Kashiwagi (94453)
Hiroyuki Iijima (94455)
Zentaro Yamagata (94454)
Mitsuko Takamoto (153461)
Makoto Aihara (153475)
Takeshi Iwata (230562)
Kazuhide Kawase (4386166)
Yukihiro Shiga (724428)
Koji M. Nishiguchi (147632)
Toru Nakazawa (287792)
Mineo Ozaki (531871)
Makoto Araie (88258)

August 2017

Result of multiple linear regression analysis using the maximum IOP as a dependent variable.</p

Results of the multiple regression analysis, with naming latencies as the criterion variable and H statistic, age of acquisition, visual complexity, manipulability and lexical frequency as predictor variables.

Eduardo Navarrete (5517809)
Giorgio Arcara (182500)
Sara Mondini (202286)
Barbara Penolazzi (231910)

February 2019

Results of the multiple regression analysis, with naming latencies as the criterion variable and H s...

Multiple linear regression was used to determine the ratio of similar materials.

Zhengguo Zhu (2676715)
Chaoyi Ma (14259558)
Zhichun Fang (14259561)
Weige Han (14259564)
Renyuan Wang (10921218)
Zhiming Han (151601)
Shaohu Chu (14259567)
Yuanbo Gao (14259570)

December 2022

Multiple linear regression was used to determine the ratio of similar materials.</p

Standard deviation of the estimated latency produced by the different methods.

Anna Balkenius (326760)
Anders J. Johansson (550822)
Christian Balkenius (750467)

June 2015

Standard deviation of the estimated latency produced by the different methods.</p

Results of multiple linear regression analysis (stepwise regression) for burnout predictors.

Othman A. Alfuqaha (6499352)
Mahmoud Y. Alkawareek (309198)
Hussein S. Alsharah (6499355)

March 2019

Results of multiple linear regression analysis (stepwise regression) for burnout predictors.</p

Performance of the multiple linear regression models for the summary cognitive indexes.

Tania Aznielle-Rodríguez (13253196)
Lídice Galán-García (13253202)
Marlis Ontivero-Ortega (13253199)
Karen Aguilar-Mateu (17020639)
Ana M. Castro-Laguardia (17020642)
Ana Fernández-Nin (17020645)
Daysi García-Agustín (17020648)
Mitchell Valdés-Sosa (5656780)

September 2023

Performance of the multiple linear regression models for the summary cognitive indexes.</p

Results of the excluded variables according to the multiple linear regression analysis for burnout predictors.

Othman A. Alfuqaha (6499352)
Mahmoud Y. Alkawareek (309198)
Hussein S. Alsharah (6499355)

March 2019

Results of the excluded variables according to the multiple linear regression analysis for burnout p...

Results of the multi-variate regression analysis with quasi-least squares, adjusted for multiple comparisons.

Eleonora M. Lad (1889089)
Dibyendu Mukherjee (4840374)
Sandra S. Stinnett (4840368)
Scott W. Cousins (324993)
Guy G. Potter (320987)
James R. Burke (258157)
Sina Farsiu (3951851)
Heather E. Whitson (4840371)

February 2018

Results of the multi-variate regression analysis with quasi-least squares, adjusted for multiple ...

Results of generalized linear regression model analyses for factors significantly associated with the number of previous MDEs.

Margo de Jonge (5925116)
Claudi L. H. Bockting (130818)
Patricia van Oppen (94161)
Henricus L. Van (5925119)
Jaap Peen (83470)
Martijn J. Kikkert (5925122)
Jack J. M. Dekker (764011)

November 2018

Results of generalized linear regression model analyses for factors significantly associated with th...

Results obtained for intensity of SEFs using multiple linear regression analysis.

Chihiro Kitayama (13844178)
Eriya Shimada (13844181)
Hiroki Hihara (4112329)
Akitake Kanno (393126)
Nobukazu Nakasato (401401)
Yoshimichi Imai (503312)
Akimitsu Sato (13844184)
Ryuta Kawashima (146994)
Kaoru Igarashi (8164452)
Hiroyasu Kanetaka (724955)

September 2022

Results obtained for intensity of SEFs using multiple linear regression analysis.</p

Results of the multiple linear regression analysis of environmental factors affecting the EE.

Luis Carus (10125582)
Isabel Castillo (5740634)

August 2021

Results of the multiple linear regression analysis of environmental factors affecting the EE.</p

Results of the multiple linear regression analysis of environmental factors affectation of measured speed.

Luis Carus (10125582)
Isabel Castillo (5740634)

August 2021

Results of the multiple linear regression analysis of environmental factors affectation of measured ...

Results of the multiple stepwise linear regression using hyperspectral metrics.

Haiming Qin (782099)
Cheng Wang (102692)
Kaiguang Zhao (197563)
Xiaohuan Xi (5309420)

May 2018

Results of the multiple stepwise linear regression using hyperspectral metrics.</p

Results of the multiple time regression analysis experiment of the five CPP using the linear model.

Sofie Stalmans (443949)
Nathalie Bracke (443951)
Evelien Wynendaele (443950)
Bert Gevaert (443952)
Kathelijne Peremans (296815)
Christian Burvenich (443954)
Ingeborgh Polis (296823)
Bart De Spiegeleer (443955)

October 2015

Results of the multiple time regression analysis experiment of the five CPP using the linear mode...

Results of multiple linear regression for predicting number of non-doubt answers.

Jef Vanderoost (5108072)
Rianne Janssen (5811212)
Jan Eggermont (29168)
Riet Callens (5811215)
Tinne De Laet (1369203)

October 2018

Results of multiple linear regression for predicting number of non-doubt answers.</p

Result of multiple linear regression analysis using the maximum IOP as a dependent variable.

Fumihiko Mabuchi (94451)
Nakako Mabuchi (4386163)
Yoichi Sakurada (94452)
Seigo Yoneyama (2503747)
Kenji Kashiwagi (94453)
Hiroyuki Iijima (94455)
Zentaro Yamagata (94454)
Mitsuko Takamoto (153461)
Makoto Aihara (153475)
Takeshi Iwata (230562)
Kazuhide Kawase (4386166)
Yukihiro Shiga (724428)
Koji M. Nishiguchi (147632)
Toru Nakazawa (287792)
Mineo Ozaki (531871)
Makoto Araie (88258)

August 2017

Result of multiple linear regression analysis using the maximum IOP as a dependent variable.</p

Results of the multiple regression analysis, with naming latencies as the criterion variable and H statistic, age of acquisition, visual complexity, manipulability and lexical frequency as predictor variables.

Eduardo Navarrete (5517809)
Giorgio Arcara (182500)
Sara Mondini (202286)
Barbara Penolazzi (231910)

February 2019

Results of the multiple regression analysis, with naming latencies as the criterion variable and H s...

Multiple linear regression was used to determine the ratio of similar materials.

Zhengguo Zhu (2676715)
Chaoyi Ma (14259558)
Zhichun Fang (14259561)
Weige Han (14259564)
Renyuan Wang (10921218)
Zhiming Han (151601)
Shaohu Chu (14259567)
Yuanbo Gao (14259570)

December 2022

Multiple linear regression was used to determine the ratio of similar materials.</p

Standard deviation of the estimated latency produced by the different methods.

Anna Balkenius (326760)
Anders J. Johansson (550822)
Christian Balkenius (750467)

June 2015

Standard deviation of the estimated latency produced by the different methods.</p

Results of multiple linear regression analysis (stepwise regression) for burnout predictors.

Othman A. Alfuqaha (6499352)
Mahmoud Y. Alkawareek (309198)
Hussein S. Alsharah (6499355)

March 2019

Results of multiple linear regression analysis (stepwise regression) for burnout predictors.</p

Performance of the multiple linear regression models for the summary cognitive indexes.

Tania Aznielle-Rodríguez (13253196)
Lídice Galán-García (13253202)
Marlis Ontivero-Ortega (13253199)
Karen Aguilar-Mateu (17020639)
Ana M. Castro-Laguardia (17020642)
Ana Fernández-Nin (17020645)
Daysi García-Agustín (17020648)
Mitchell Valdés-Sosa (5656780)

September 2023

Performance of the multiple linear regression models for the summary cognitive indexes.</p

Results of the excluded variables according to the multiple linear regression analysis for burnout predictors.

Othman A. Alfuqaha (6499352)
Mahmoud Y. Alkawareek (309198)
Hussein S. Alsharah (6499355)

March 2019

Results of the excluded variables according to the multiple linear regression analysis for burnout p...

Results of the multi-variate regression analysis with quasi-least squares, adjusted for multiple comparisons.

Eleonora M. Lad (1889089)
Dibyendu Mukherjee (4840374)
Sandra S. Stinnett (4840368)
Scott W. Cousins (324993)
Guy G. Potter (320987)
James R. Burke (258157)
Sina Farsiu (3951851)
Heather E. Whitson (4840371)

February 2018

Results of the multi-variate regression analysis with quasi-least squares, adjusted for multiple ...

Results of generalized linear regression model analyses for factors significantly associated with the number of previous MDEs.

Margo de Jonge (5925116)
Claudi L. H. Bockting (130818)
Patricia van Oppen (94161)
Henricus L. Van (5925119)
Jaap Peen (83470)
Martijn J. Kikkert (5925122)
Jack J. M. Dekker (764011)

November 2018

Results of generalized linear regression model analyses for factors significantly associated with th...

Results obtained for latency of SEFs using multiple linear regression analysis.

Abstract

Extracted data

Results obtained for latency of SEFs using multiple linear regression analysis.

Abstract

Extracted data

Related items

Related items