This course develops logical, empirically based arguments using statistical techniques and analytic methods. Elementary statistics, probability, and other types of quantitative reasoning useful for description, estimation, comparison, and explanation are covered. Emphasis is on the use and limitations of analytical techniques in planning practice.

OER may be distributed in a variety of formats, including electronically online, removable media (e.g. CD/DVD, or USB), and/or paper hard copies. In order to maximize its reach and visibility, OER is often distributed online which introduces new considerations such as managing file size and selecting appropriate descriptive data (commonly referred to as metadata). File size is an especially important consideration as small manageable files can be more easily downloaded in bandwidth-constrained areas. As part of the African Health OER Network, completed OER are often hosted on multiple servers: an institutional server, the official Network web space with OER Africa, and on the University of Michigan (U-M) Open.Michigan website.The aim of these guidelines is to encourage the creation of OER that is easily discoverable, accessible, and adaptable.

This 14-minute video lesson shows how to figure out the probability of running out of water on a camping trip. [Statistics playlist: Lesson 39 of 85]

Managers and engineers are constantly attempting to optimize, particularly in the design and operation of complex systems. This course is an application-oriented introduction to (systems) optimization. It seeks to:

Motivate the use of optimization models to support managers and engineers in a wide variety of decision making situations;
Show how several application domains (industries) use optimization;
Introduce optimization modeling and solution techniques (including linear, non-linear, integer, and network optimization, and heuristic methods);
Provide tools for interpreting and analyzing model-based solutions (sensitivity and post-optimality analysis, bounding techniques); and
Develop the skills required to identify the opportunity and manage the implementation of an optimization-based decision support tool.

It contains binomial distribution, poisson distribution along with discrete uniform distributions with their formulas and thier properties with solved examples

(Nota: Esta es una traducción de un recurso educativo abierto creado por el Departamento de Educación del Estado de Nueva York (NYSED) como parte del proyecto "EngageNY" en 2013. Aunque el recurso real fue traducido por personas, la siguiente descripción se tradujo del inglés original usando Google Translate para ayudar a los usuarios potenciales a decidir si se adapta a sus necesidades y puede contener errores gramaticales o lingüísticos. La descripción original en inglés también se proporciona a continuación.)

En este módulo, los estudiantes reconectan y profundizan su comprensión de las estadísticas y los conceptos de probabilidad introducidos por primera vez en los grados 6, 7 y 8. Los estudiantes desarrollan un conjunto de herramientas para comprender e interpretar la variabilidad en los datos, y comienzan a tomar decisiones más informadas de los datos . Trabajan con distribuciones de datos de varias formas, centros y diferenciales. Los estudiantes se basan en su experiencia con datos cuantitativos bivariados del grado 8. Este módulo prepara el escenario para un trabajo más extenso con muestreo e inferencia en calificaciones posteriores.

Encuentre el resto de los recursos matemáticos de Engageny en https://archive.org/details/engageny-mathematics.

English Description:
In this module, students reconnect with and deepen their understanding of statistics and probability concepts first introduced in Grades 6, 7, and 8. Students develop a set of tools for understanding and interpreting variability in data, and begin to make more informed decisions from data. They work with data distributions of various shapes, centers, and spreads. Students build on their experience with bivariate quantitative data from Grade 8. This module sets the stage for more extensive work with sampling and inference in later grades.

Find the rest of the EngageNY Mathematics resources at https://archive.org/details/engageny-mathematics.