This interactive tutorial on Exponential Smoothing helps learners understand the use of exponential smoothing, define exponential smoothing, cite the merits and demerits of exponential smoothing, and solve exercise problems using exponential smoothing.
This interactive module helps students to understand the definition of and uses for clustering algorithms. Students will learn to categorize the types of clustering algorithms, to use the minimal spanning tree and the k-means clustering algorithm, and to solve exercise problems using clustering algorithms.
This module is a short quiz which gives a review/assessment of the main concepts for this refresher course. At the bottom, there is a grading button to rate the understanding of the material.
This interactive tutorial on Basic Probability helps students understand the basic concepts of probability, define independent and compound events, use the basic properties of probability, understand the concept of conditional probability, and solve exercise problems using basic probability.
This tutorial on Random Variables helps students understand the definition of random variables, recognize and use discrete random variables, recognize and use continuous random variables, and solve exercise problems using random variables.
This interactive tutorial on Expectations helps students understand the concept of expectations, recognize and use variance and standard deviation, understand the method of moments, recognize and use co-variance, and solve exercise problems using expectations.
This tutorial on Distributions helps students understand the basic concept of probability distributions, recognize and use Binomial, Normal, Poisson, and Uniform Distributions, and solve exercise problems using probability distributions.
This self-test provides a review/assessment of the Probability section of this module. At the bottom, there is a grading button to rate the users' understanding of the material.
This tutorial on Multiple Regression helps students understand the definition, use the standard error of estimate, use rank correlation, and solve exercise problems using multiple regression.
