What This Document Is
These are lecture notes from AMS 572, Data Analysis I, at Stony Brook University, specifically covering Lecture #6 from September 28, 2010. This resource focuses on the foundational principles of statistical inference, bridging theoretical concepts with practical application in commonly used statistical software. It’s designed to support students learning to draw conclusions and make informed decisions based on data.
Why This Document Matters
This material is essential for students enrolled in introductory data analysis courses, particularly those seeking a strong understanding of hypothesis testing and sample size determination. It’s most valuable when used in conjunction with course lectures and assignments, serving as a detailed reference to reinforce key ideas. Individuals preparing for more advanced statistical modeling or needing to apply these concepts in research or professional settings will also find it beneficial.
Topics Covered
* Power Calculation for inferences related to a single population mean.
* Determining appropriate sample sizes for statistical studies.
* Implementation of statistical procedures using SAS and R software.
* Confidence Interval approaches to sample size determination.
* Inference on population variance, including normality considerations.
* An introduction to Analysis of Variance (ANOVA).
What This Document Provides
* A structured presentation of the theoretical underpinnings of power analysis and sample size calculations.
* Illustrative examples demonstrating the application of these concepts to real-world scenarios.
* Guidance on utilizing SAS and R for performing statistical analyses.
* An overview of various statistical tests, including parametric and non-parametric options.
* A foundation for understanding more complex statistical methodologies covered in subsequent coursework.
