90 lines
3.3 KiB
BibTeX
90 lines
3.3 KiB
BibTeX
@online{pythonplearson,
|
|
author = {Jason Brownlee},
|
|
title = {How to Calculate Correlation Between Variables in Python},
|
|
date = {Aug 14, 2019},
|
|
url = {https://machinelearningmastery.com/how-to-use-correlation-to-understand-the-relationship-between-variables/},
|
|
}
|
|
@book{cuanti1,
|
|
author = {McMillan, James and Schumacher, Sally},
|
|
title = {Investigación Educativa},
|
|
year = {2005},
|
|
edition = {5},
|
|
publisher = {Pearson Educacación},
|
|
}
|
|
@book{cuanti2,
|
|
author = {Campbell, Donald and Stanley, Julian},
|
|
title = {Diseños experimentales y cuasiexperimentales en la investigación social},
|
|
year = {1995},
|
|
edition = {1},
|
|
publisher = {Amorrotou editores},
|
|
}
|
|
@TechReport{diagmate18,
|
|
author = {Sonia Zamora},
|
|
title = {Resultados Diagnósticos Asignatura de Matemática I},
|
|
institution = {Universidad Técnica Federico Santa María - Sede Concepción},
|
|
year = {2018},
|
|
}
|
|
@TechReport{diagmate19,
|
|
author = {Sonia Zamora},
|
|
title = {Resultados Diagnósticos Asignatura de Matemática I},
|
|
institution = {Universidad Técnica Federico Santa María - Sede Concepción},
|
|
year = {2019},
|
|
}
|
|
@TechReport{diagchaea18,
|
|
author = {Nicole Morales},
|
|
title = {Resultados De La Aplicación Del Cuestionario Honey-Alonso de Estilos De Aprendizaje},
|
|
institution = {Universidad Técnica Federico Santa María - Sede Concepción},
|
|
year = {2018},
|
|
}
|
|
|
|
@TechReport{diagchaea19,
|
|
author = {Nicole Morales},
|
|
title = {Resultados De La Aplicación Del Cuestionario Honey-Alonso de Estilos De Aprendizaje},
|
|
institution = {Universidad Técnica Federico Santa María - Sede Concepción},
|
|
year = {2019},
|
|
}
|
|
|
|
@article{cuantitativax,
|
|
author = {Fernández, Pita and Díaz, Pértega},
|
|
title ={Relación entre variables cuantitativas},
|
|
journal = {Complexo Hospitalario Juan Canalejo. A Coruña.Cad Aten Primaria 1997},
|
|
volume = {4},
|
|
pages = {141-144},
|
|
year = {2001},
|
|
|
|
|
|
URL = {
|
|
https://www.fisterra.com/mbe/investiga/var_cuantitativas/var_cuantitativas2.pdf
|
|
|
|
},
|
|
eprint = {
|
|
https://www.fisterra.com/mbe/investiga/var_cuantitativas/var_cuantitativas2.pdf
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
@article{doi:10.1177/070674370304801108,
|
|
author = {David L Streiner},
|
|
title ={Unicorns Do Exist: A Tutorial on “Proving” the Null Hypothesis},
|
|
journal = {The Canadian Journal of Psychiatry},
|
|
volume = {48},
|
|
number = {11},
|
|
pages = {756-761},
|
|
year = {2003},
|
|
doi = {10.1177/070674370304801108},
|
|
note ={PMID: 14733457},
|
|
|
|
URL = {
|
|
https://doi.org/10.1177/070674370304801108
|
|
|
|
},
|
|
eprint = {
|
|
https://doi.org/10.1177/070674370304801108
|
|
|
|
}
|
|
,
|
|
abstract = { Introductory statistics classes teach us that we can never prove the null hypothesis; all we can do is reject or fail to reject it. However, there are times when it is necessary to try to prove the nonexistence of a difference between groups. This most often happens within the context of comparing a new treatment against an established one and showing that the new intervention is not inferior to the standard. This article first outlines the logic of “noninferiority” testing by differentiating between the null hypothesis (that which we are trying to nullify) and the “nill” hypothesis (there is no difference), reversing the role of the null and alternate hypotheses, and defining an interval within which groups are said to be equivalent. We then work through an example and show how to calculate sample sizes for noninferiority studies. }
|
|
}
|