Probability

• Introduction to Categorical Data Analysis

This presentation is a part of a series of lessons on the Analysis of Categorical Data.  This lecture provides a review of probability and statistical concepts such as conditional probabilities, Bayes Theorem, sensitivity and specificity, and binomial and poisson distributions.

• Penn State STAT 800: Introduction to Applied Statistics

This is a graduate level introduction to statistics including topics such as probabilty/sampling distributions, confidence intervals, hypothesis testing, ANOVA, and regression.  Perfect for students and teachers wanting to learn/acquire materials for this topic.

• Penn State STAT 510: Applied Time Series Analysis

The objective of this course is to learn and apply statistical methods for the analysis of data that have been observed over time.  Our challenge in this course is to account for the correlation between measurements that are close in time. Perfect for students and teachers wanting to learn/acquire materials for this topic.

• Penn State STAT 505: Applied Multivariate Statistical Analysis

Those who complete this course will be able to select appropriate methods of multivariate data analysis, given multivariate data and study objectives; write SAS and/or Minitab programs to carry out multivariate data analyses; and interpret results of multivariate data analyses.  Perfect for students and teachers wanting to learn/acquire materials for this topic.

• Penn State STAT 504: Analysis of Discrete Data

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.

• HyperStat Online: Ch. 10 Testing Hypotheses with Standard Errors

This text explains the differences between t-tests, z-tests, tests with proportions, and tests of correlation.

• Online Statistics Education: An Interactive Multimedia Course of Study

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.

• Rice Virtual Lab Simulations (JAVA Applets)

A collection of Java applets and simulations covering a range of topics (descriptive statistics, confidence intervals, regression, effect size, ANOVA, etc.).

• Song: It Might Not Be That Bad

A song for use in helping students to apply Bayes Theorem and examine marginal and conditional proportions in a table to see how, for rare conditions, most positive test results may be false positives.  Lyrics and music by Tom Toce copyright 2015.  This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).

• Analysis Tool: RStudio Cloud

RStudio Cloud makes it easy for professionals, hobbyists, trainers, teachers and students to do, share, teach and learn data science using R.  Create analyses using RStudio directly from your browser - there is no software to install and nothing to configure on your computer.  Share your projects - and access those of others - without worrying about data transfer or package installation. Each project defines its own environment, and RStudio Cloud automatically reproduces that environment whenever anyone accesses the project.  It’s easy to share analyses with the world - but it’s also simple to collaborate with a select group in a private space. You control who can enter a space - and via roles, you have fine grained control over what each user can do.  There are also many learning materials available: interactive tutorials covering the basics of data science, cheatsheets for working with popular R packages, links to Datacamp courses, and a guide to using RStudio Cloud.