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# Tutorial

• ### 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.

• ### Designing Tests To Maximize Learning

This one-page document gives advice on how to construct and give exams. It focuses on making exams a positive experience for both instructors and students. It is written by Rich Felder an expert in Engineering education.
• ### Types of Surveys

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

This site explains the 2x2 factorial experimental design, it's components, and it's effects. Graphs illustrate the concepts discussed.
• ### Research Methods Knowledge Base : The t-Test

This particular textbook lesson addresses the independent t-test. It presents to the user how to compute the t statistic and then how to interpret the results.
• ### Comparing Two Independent Sample Means

Textbook-like example showing the independent t-test. Gives a nice way for students to think through the problem and interpret results.
• ### Scatter Plot (Engineering Statistics Handbook)

An explanation of scatter plots, their use, purpose and interpretation. It provides examples of the various relationships described by scatter plots as well as case studies and related techniques.
• ### Independent Group t-Test

This tutorial on the Two Sample t test includes its definition, assumptions, hypotheses, and results as well as tests for equal variance and graphical comparisons. An example using output from the WINKS software is given, but those without the software can still use the tutorial. An exercise is given at the end that can be done with any statistical software package.
• ### Central Limit Theorem

This tutorial illustrates the basic principles of the Central Limit Theorem and enhances conceptual understand of why the Central Limit Theorem is important to inferential statistics.
• ### Normal Distribution (Engineering Statistics Handbook)

This section of the Engineering Statistics Handbook gives the normal probability density function as well as the standard normal distribution equations. Example graphs of the distributions are shown and a justification of the Central Limit Theorem is included.