Skip to main content

Downloads

MATLAB Code

The MATLAB code that accompanies Missile Flight Simulation cannot be downloaded from this website. Please request the code by e-mail at jeff@humalytica.com.

RCMap
Files cannot be downloaded from this website. The new site is under construction.

Excel Templates

Project Management PERT

ExtendSim Textbook Supplements

Models: 

The Models that accompany Discrete Event Simulation Using ExtendSim 8 cannot be downloaded from this website. Please request the Models by e-mail at jeff@humalytica.com.

PowerPoint Slides:

The PPT Slides that accompany Discrete Event Simulation Using ExtendSim 8 cannot be downloaded from this website. Please request the PPT Slides by e-mail at jeff@humalytica.com.

Combat Models

The Models that accompany Mathematical Modeling of Warfare and Combat Phenomenon cannot be downloaded from this website. Please request the Models by e-mail at jeff@humalytica.com.

Comments

Popular posts from this blog

Time Series Analysis using iPython

Time Series Analysis using iPython In this example, we will examine ARMA and ARIMA models with Python using the Statsmodels package. This package can be downloaded at  http://statsmodels.sourceforge.net/stable/index.html . Autogressive Moving-Average Processes (ARMA) and Auto-Regressive Integrated Moving Average (ARIMA) can be called from the tsa (Time Series) module from the Statamodels package. Note: I am not as expert in time-series analysis as I am in other areas of Analytics, so if you find errors I would be happy to know about them and correct them. Introduction ARIMA models are, in theory, the most general class of models for forecasting a time series, which can be made to be “stationary” by differencing (if necessary), perhaps in conjunction with nonlinear transformations such as logging or deflating (if necessary). A random variable that is a time series is stationary if its statistical properties are all constant over time. A stationary series has no trend

Where Did All The Thinking Go?

Where Did All The Thinking Go?   “Thinking is the hardest work there is, which is probably the reason so few engage in it.” ― Henry Ford What Do We Really Want? We live in a fast-food society, at least in the USA. We want what we want, now! We prefer not to work too hard for it, if we work at all, and many of us have a sense of entitlement. We believe all of us should go to college and get our degree, but not much effort should be expended in doing so. After all, we have lots of cheeseburgers to munch on and many parties to attend. “Five percent of the people think; ten percent of the people think they think; and the other eighty-five percent would rather die than think.” ― Thomas A. Edison When I was teaching, even at the United States Military Academy, I ran into this attitude often. Students wanted to do the minimum amount of work to get by with a passing grade. In contrast were the non-traditional students taking evening classes after working a fulltime job dur

Neural Networks using R

Neural Networks using R By Jeffrey Strickland on May 13, 2015 The intent of this article is not to tell you everything you wanted to know about artificial neural networks (ANN) and were afraid to ask. For that you’ll have to ask someone else. Here I only intend to tell you how you might use R to implement an ANN model. One thing I will say is that I rarely use an ANN. I have found them to work best in an ensemble model (using averaging) with logistics regression models. Using neuralnet neuralnet depends on two other packages: grid and MASS ( Venables and Ripley, 2002). It is used is primarily with functions dealing with regression analyses like linear models ( lm ) and general linear models ( glm ). As essential arguments, we must specify a formula in terms of response variables ~ sum of covariates and a data set containing covariates and response variables. Default values are defined for all other parameters (see next subsection). We use the data set infert that i