Gallery — Math

Gallery Items tagged Math

Show all Gallery Items Si \(AB=I\) entonces \(A\) es invertible y \(A^{-1}=B\)
Vamos a demostrar el notable teorema que dice que, dadas dos matrices cuadradras \(A\) y \(B\) del mismo tamaño, si \(AB=I\), donde \(I\) es la matriz identidad del mismo tamaño que la matrices \(A\) y \(B\), entonces \(A\) es invertible y \(B^{-1}=A\). La prueba será directa y sólo usaremos el hecho de que si \(|A|\ne0\) entonces \(A\) es invertible. La pregunta es si puedes tú, estimado estudiante, ofrecer otra prueba de la que aquí se sugiere. Sirva además este texto como un ejemplo de escritura con LaTeX.
Memo Garro Materials 1 Lab Report
The aim of this laboratory work is to design a strut/bracket assembly for aircrafts. Experiments are carried out to determine mechanical properties of certain materials.The material chosen is Mild Steel. Given the possible condition experienced by the material and the safety factor, the dimensions for the designs of the strut/bracket assembly for aircrafts are obtained to avoid failure by yield or fracture. The diameter of the pin, d ,which is subjected to shear stress should be larger than 14.56mm. The diameter of the rod, D, should be larger than 12.74mm. The thickness of the rod would be 10mm.
Chen Zhi Shen MDA HW2: Principial components analysis and Canonical correlation analysis
Principal Components Analysis (PCA) and Canonical Correlation Analysis (CCA) are among the methods used in Multivariate Data Analysis. PCA is concerned with explaining the variance-covariance structure of a set of variables through a few linear combinations of these variables. Its general objectives are data reduction and interpretation. CCA seeks to identify and quantify the associations between two sets of variables i.e Pulp fibres and Paper variables.PCA shows that the first PC already exceeds 90% of the total variability. According to the proportion of variability explained by each canonical variable , the results suggest that the first two canonical correlations seem to be sufficient to explain the structure between Pulp and Paper characteristics with 98.86%. Despite the fact that the first the two canonical variables keep 98% of common variability, 78% was kept in the pulp fiber set and about 94% of the paper set as a whole. In the proportion of opposite canonical variable,there were approximately 64% for the paper set of variables and 78% for the pulp fiber set of variables kept for the two respectively. 