Niveles de Razonamiento Inferencial para el Estadístico t-Student

Abstract

Resumen En este artículo presentamos una propuesta de niveles progresivos, de lo informal a lo formal, de razonamiento inferencial para el estadístico t-Student, a partir de criterios epistémicos identificados con un estudio de tipo histórico-epistemológico sobre este estadístico y de la investigación desarrollada sobre razonamiento inferencial. Para ello, utilizamos algunas nociones teórico-metodológicas introducidas por el Enfoque Onto-Semiótico del conocimiento y la instrucción matemáticos (EOS), las cuales permitieron tanto identificar y caracterizar diversos significados conferidos al estadístico t-Student, a lo largo de su evolución y desarrollo, como presentar una perspectiva integral de lo que se considera razonamiento inferencial. Los atributos matemáticos de los diversos significados del estadístico t-Student se encuentran fuertemente vinculados a los indicadores de los distintos niveles de razonamiento aquí expuestos. Además, cada nivel se encuentra asociado a un razonamiento inferencial informal, pre-formal o formal. La propuesta de niveles de Razonamiento Inferencial para el estadístico t-Student y sus indicadores, se prevén útiles para el diseño de actividades que promuevan, gradualmente, un razonamiento inferencial formal sobre la base del razonamiento inferencial informal, sobre este estadístico.

Hybrid Improved Grey Wolf Support Vector Regression Algorithm for Modeling Solubilities of APIs in Pure Ionic Liquids: σ-Profile Descriptors

“…Various metrics parameters, available in the literature, are used to evaluate and check the model’s reliability in predicting (or the ability of a model to fit) the outcome. In the present study, correlation coefficient ( R ), coefficient of determination ( R 2 ), their adjusted values ( R adj and R adj 2 , respectively), cross-validated correlation coefficient ( Q 2 ), concordance correlation coefficient ( Q 2 ccc), root-mean-squared error (RMSE), mean-squared error (MSE), mean absolute error (MAE), F -value, student t -test (t-value), and chi-square value ( x 2 ) are introduced − as their formula presented below and utilized to determine the precision and performance of the suggested model R = ∑ i = 1 N ( x i obs − x̅ obs ) · false( x i pred − x̅ pred false) ∑ i = 1 N ( x i obs − x̅ obs ) 2 · ∑ i = 1 N false( x i pred − x̅ pred false) 2 R adj = true| 1 − ( 1 − R 2 ) ( N − 1 N − N normalp − 1 ) true| R 2 <...…”

“…A model is determined as the best model that represented the experimental result if that model shows the smallest value of AIC (or AICs, BIC) , AIC = N 0.25em ln ( SSE N ) AICs = AICs + 2 n p + 2 n p ( n normalp + 1 ) N − ( n normalp + 1 ) SSE = prefix∑ i = 0 N false( x i obs − x i pred false) 2 BIC = N · ln ( MSE ) + n p · ln ( N ) where N is the number of data points in the data set, x i obs is the observed value, while x̅ obs is its average, x i pred is the predicted value, while x̅ pred is its average, N p is the number of independent variables in each model, MS R is the mean square regression, MS E is the mean square residual, s is the pooled standard deviation, and SSE is the error sum of squares. More details can be found elsewhere. ,− , Typically, a single statistic value is meaningless; the principal model studied in this paper was compared with 11 different topologies.…”

“…More details can be found elsewhere. 30,[62][63][64][65][66][67]69 Typically, a single statistic value is meaningless; the principal model studied in this paper was compared with 11 different topologies.…”

Exploring challenges and strategies in teaching hypothesis testing to engineering students from the perspective of educators

“…Furthermore, proposals for levels of inferential reasoning on hypothesis testing have emerged, where it is proposed to solve statistical inference problems initially with aspects of informal inference and progressively move towards formal notions and methods of statistical inference (Lugo-Armenta & Pino-Fan, 2021b). The acquisition of statistical concepts must be gradual for its correct application in problem solving.…”

“…Recently developed studies sought to promote the logic of hypothesis testing in order to promote inferential reasoning (Harradine et al, 2011;Liu & Thompson, 2009;Lugo-Armenta & Pino-Fan, 2021b). The following item was applied only in the teachers' questionnaire and deals with students' difficulties in the study of the hypothesis tests.…”