Resource Library

These lecture notes are composed of nearly 180 PowerPoint slides that have been coverted to a pdf file (6 per page) on Biomedical Imaging. The following topics are outlined: Vocabulary, Displaying Data, Central Tendency and Variability, Normal Z-scores, Standardized Distribution, Probability, Samples & Sampling Error, Type I and Type II Errors, Power of a Test, Hypothesis Testing, One Sample Tests, Two Independent Sample Tests, Two Dependent Sample Tests & Estimation, Correlation and Regression Techniques, Non-Parametric Statistical Tests, Applications of Central Limit Theorem, Law of Large Numbers, Design of Studies and Experiments, Fisher's F-Test, Analysis Of Variance(ANOVA), Principle Component Analysis (PCA), Chi-Square Goodness-of-fit test, Multiple Linear Regression, General Linear Model, Bootstrapping and Resampling.

This entry in the online encyclopeida, Wikipedia, describes Markov Chains, their properties, discrete state spaces, and formulas for calculating probabilities using Markov Chains. Links to examples and scientific applications are also included.

This site describes in detail 5 different types of random sampling, giving examples, definitions, and procedures.

This worksheet gives students 4 excercises in designing a case study. Each exercise presents a difference scenario and asks a series of questions about study design.

This site discusses survey questionnaires and interviews, provides links to detailed descriptions and pros and cons of each, and describes how to conduct them.

This site takes the user through the steps and decisions necessary when designing a survey. Pros and cons for each method are outlined and other issues in survey design are presented.

This site lists definitions of key terms related to experimental design and ANOVA, including factorial, blocking, interaction, and others. This site is part of the "Statistics Glossary" for the STEPS project (STatistical Education through Problem Solving).

This site explains the 2x2 factorial experimental design, it's components, and it's effects. Graphs illustrate the concepts discussed.

This resource defines what a p-value is, why .05 is significant, and when to use it. It also covers related topics such as one-tailed/two-tailed tests and hypothesis testing.

The applet in this section allows for simple data analysis of univariate data. Users can either generate normal or uniform data for k samples or copy and paste data from another source to a text box. A univariate analysis is performed for all k samples. A two-sample t-test (Pooled and Satterthwaite) is performed for k = 2. An ANOVA test is performed for k > 2. This page was formerly located at http://www.stat.vt.edu/~sundar/java/applets/Data.html