Gallery — Essay

Created for Stanford Mathematics Camp 2016. A brief introduction to the Hopf Fibration for introductory topology students.

Here we discuss the path integral formalism for quantization of fields. The basic idea is reviewed and explained. This is completely based on the book ``Quantum Field Theory A Modern Introduction" by Michio Kaku. For calculation natural system of units is taken.

An sample essay for Math 220 assignments

We present a geometric proof of the addition formulas for the hyperbolic sine and cosine functions, using elementary properties of linear transformations.

Linear regression is one of the most widely used statistical methods available today. It is used by data analysts and students in almost every discipline. However, for the standard ordinary least squares method, there are several strong assumptions made about data that is often not true in real world data sets. This can cause numerous problems in the least squares model. One of the most common issues is a model overfitting the data. Ridge Regression and LASSO are two methods used to create a better and more accurate model. I will discuss how overfitting arises in least squares models and the reasoning for using Ridge Regression and LASSO include analysis of real world example data and compare these methods with OLS and each other to further infer the benefits and drawbacks of each method.

Visualization is a descriptive way to ensure the audience attention and to make people better understand the content of a given topic. Nowadays, in the world of science and technology, visualization has become a necessity. However, it is a huge challenge to visualize varying amounts of data in a static or dynamic form. In this paper we describe the role, value and importance of visualization in maths and science. In particular, we are going to explain in details the benefits and shortages of visualization in three main domains: Mathematics, Programming and Big Data. Moreover, we will show the future challenges of visualization and our perspective how to better approach and face with the recent problems through technical solutions.

Teaching and Research Statement example written for 2015 faculty search season.

Es posible diseñar modelos en donde la variable dependiente posea característica cualitativas, ese es el caso que analizaremos en el presente trabajo, enfocándonos únicamente en el modelo LOGIT que nos brinda ciertas ventajas en comparación a un modelo lineal de probabilidad, estimada por mínimos cuadrados ordinarios(MCO) para lo cual resaltaremos dichas diferencias. Los modelos de regresión con respuesta cualitativa son modelos de regresión en los cuales la variable dependiente puede ser de naturaleza cualitativa, mientras que las variables independientes pueden ser cualitativas o cuantitativas, o una mezcla de las dos; por ejemplo, si se está estudiando la relación entre ingresos y el pagar o no impuesto de renta, la respuesta o regresada solo puede tomar dos valores (si paga impuesto de renta o no paga dicho impuesto); otros ejemplos en que la regresada es cualitativa son si la familia posee o no vivienda propia, se aprueba o pierde un curso, padece determinada enfermedad o no la padece. La variable cualitativa en estos tipos de modelos no tiene que restringirse simplemente a respuestas de sí o no, la variable respuesta puede tomar más de dos valores, ser tricotómica o politómica, también se establecen modelos en lo que la variable dependiente es de carácter ordinal o de carácter nominal, en donde no hay preestablecido ningún tipo de orden. En este trabajo se analizara el modelo LOGIT en donde la variable dependiente es de carácter binario o dicotómico (sí o no). (Green 2001) Se trata pues de adoptar una formulación no lineal que obligue a que los valores estimados estén entre 0 y 1 ya que, la regresión con una variable binaria dependiente Y modeliza la probabilidad de que Y = 1. La regresión LOGIT utiliza una función de distribución logística, su función de distribución de probabilidad da lugar a probabilidades ente 0 y 1, y presenta un crecimiento no lineal (con mayores incrementos en la parte central).

In mathematics, a rational number is any number that can be expressed as the quotient or fraction p/q of two integers, a numerator p and a non-zero denominator q. Since q may be equal to 1, every integer is a rational number. The set of all rational numbers, often referred to as ”the rationals”, is usually denoted by a boldface Q (or blackboard bold , Unicode ); it was thus denoted in 1895 by Giuseppe Peano after quoziente, Italian for ”quotient”. The decimal expansion of a rational number always either terminates after a finite number of digits or begins to repeat the same finite sequence of digits over and over. Moreover, any repeating or terminating decimal represents a rational number. These statements hold true not just for base 10, but also for any other integer base (e.g. binary, hexadecimal). A real number that is not rational is called irrational. Irrational numbers include √2, , e, and . The decimal expansion of an irrational number continues without repeating. Since the set of rational numbers is countable, and the set of real numbers is uncountable, almost allreal numbers are irrational.