The focus of this class is a multivariate analysis of discrete data. We will learn basic statistical methods and discuss issues relevant for the analysis of some discrete distribution, cross-classified tables of counts, (i.e., contingency tables), success/failure records, questionnaire items, judge's ratings, etc. Being familiar with matrix algebra is helpful in completing this course. Perfect for students and teachers wanting to learn/acquire materials for this topic.
Statistics is often taught as though the design of the data collection and the data cleaning have already been done in advance. However, as most practicing statisticians quickly learn, typically problems that arise at the analysis stage, could have been avoided if the experimenter had consulted a statistician before the experiment was done and the data were conducted. This course is created to provide an understanding of how experiments should be designed so that when the data are collected, these shortcomings are avoided. Perfect for students and teachers wanting to learn/acquire materials for this topic.
This is a graduate level course/collection of lessons in analysis of variance (ANOVA), including randomization and blocking, single and multiple factor designs, crossed and nested factors, quantitative and qualitative factors, random and fixed effects, split plot and repeated measures designs, crossover designs and analysis of covariance (ANCOVA). Perfect for students and teachers alike looking to learn/acquire materials on ANOVA.
This graduate level course offers an introduction into regression analysis. A researcher is often interested in using sample data to investigate relationships, with an ultimate goal of creating a model to predict a future value for some dependent variable. The process of finding this mathematical model that best fits the data involves regression analysis. STAT 501 is an applied linear regression course that emphasizes data analysis and interpretation and is perfect for both students and teachers of statistics courses.
The program DistCalc calculates probabilities and critical values for the most important distributions. The purpose of this program is to show the concept of critical values and the replacement of printed distribution tables. The Distribution Calculator offers calculations for the normal distribution, the t distribution, the chi-square distribution, and the F distribution.
This site has a wide collection of statistical resources inluding an online textbook covering first-year non-calculus based statistics (e.g. Normal distribution, ANOVA, Chi-Square), a simulation/demonstration section containing Java Applets on these first-year topics (ANOVA, Binomial Distribution,Central Limit Theorem, Chi Square, Confidence Interval, Correlation, Central Tendency, Effect Size, Goodness of Fit, Histogram, Normal Distribution, Power, Regression, Repeated Measures, Restriction of Range, Sampling Distribution, Skew, t-test, Transformations), and case studies covering the topics in the first-year statistics course. There is also a page with some basic statistical analysis tools that will aid in doing the computations if you have a Java enabled browser. The source code for these resources can also be downloaded from this site.
Online Statistics: An Interactive Multimedia Course of Study is a resource for learning and teaching introductory statistics. It contains material presented in textbook format and as video presentations. This resource features interactive demonstrations and simulations, case studies, and an analysis lab.
This is a web application framework for R, in which you can write and publish web apps without knowing HTML, Java, etc. You create two .R files: one that controls the user interface, and one that controls what the app does. The site contains examples of Shiny apps, a tutorial on how to get started, and information on how to have your apps hosted, if you don't have a server.
Explore the functionality of your scientific calculator.
Gapminder seeks to educate all on the importance of "factfulness" and of knowing and contextualizing the statistics that describe the state of our world. These data sets provide facts from across the globe such as average income, life expectancy, energy use, education levels, and much more.