# Resource Library

#### Statistical Topic

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• ### 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.

• ### Penn State STAT 503: Design of Experiments

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.

• ### Penn State STAT 502: Analysis of Variance and Design of Experiments

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.

• ### Penn State STAT 501: Regression Methods

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.

• ### HyperStat Online: Ch. 8 Confidence Intervals

This resource gives a thorough definition of confidence intervals. It shows the user how to compute a confidence interval and how to interpret them. It goes into detail on how to construct a confidence interval for the difference between means, correlations, and proportions. It also gives a detailed explanation of Pearson's correlation. It also includes exercises for the user.

• ### JFreeReport

JFreeReport is a free Java report library. It has the following features: full on-screen print preview; data obtained via Swing's TableModel interface (making it easy to print data directly from your application); XML-based report definitions; output to the screen, printer or various export formats (PDF, HTML, CSV, Excel, plain text); support for servlets (uses the JFreeReport extensions) complete source code included (subject to the GNU Lesser General Public Licence); extensive source code documentation.

• ### HyperStat Online: Ch. 16 Chi Square

This resource defines and explains Chi square. It takes the user through 5 different categories: 1) Testing differences between p and pi 2) More than two categories 3) Chi-square test of independence 4) Reporting results 5) Exercises.

• ### HyperStat Online: Ch. 6 Sampling Distributions

This chapter of the HyperStat Online Textbook discusses in detail sampling distributions of various statistics (mean, median, proportions, correlation, etc.), differences between such statistics, the Central Limit Theorem, and standard error, giving formulas, examples, and exercises.

• ### HyperStat Online: Ch. 11 Power

This site defines power and explains what factors may affect it, such as significance level, sample size and variance.

• ### Getting Started with R

These handouts/links give a foundational understanding of how to set up and use R