STAT 5302 is the second course in a two-semester sequence in Intermediate Data Analysis (5301-5302). We assume that students are familiar with organizing and summarizing data, the nature of relationships between variables, sampling distributions and the underlying rationale for hypothesis tests and confidence intervals. We also assume that students are comfortable with a variety of models and inferential procedures. Specifically, the material in 5302 relies heavily on the additive model (see the early part of the text for a description of this model) and one-way ANOVA. The course will cover simple linear regression, multiple linear regression, and two-way (and multi-way) ANOVA. For each of the common statistical methods covered in the course, we will focus on generation of appropriate models for data, estimation of the model parameters and their inference, and model diagnostics. Applications of the methods will be illustrated with data analysis.

Prerequisites: Stat 5301 (Intermediate Data Analysis I) or permission of the instructor

By the end of this course, students should successfully be able to:

1. Identify an appropriate analysis for data collected in a study
2. Carry out such an analysis
3. Examine whether the assumptions behind the analysis are reasonable
4. Recognize the strengths or weaknesses of the study based on how the data was collected
5. Understand how the design of a study affects the conclusions that can be made
6. Write and discuss what conclusions can be drawn from statistical analyses

This course satisfies the General Education foundation requirement in Mathematical and Quantitative Reasoning or Data Analysis which has the following goals and expected learning outcomes:

Goals: Successful students will be able to apply quantitative or logical reasoning and/or mathematical/ statistical methods to understand and solve problems and will be able to communicate their results.

Expected Learning Outcomes (ELOs): Successful students are able to:

1. Use logical, mathematical and/or statistical concepts and methods to represent real-world situations.
2. Use diverse logical, mathematical and/or statistical approaches, technologies and tools to communicate about data symbolically, visually, numerically and verbally.
3. Draw appropriate inferences from data based on quantitative analysis and/or logical reasoning.
4. Make and evaluate important assumptions in estimation, modeling, logical argumentation and/or data analysis.
5. Evaluate social and ethical implications in mathematical and quantitative reasoning.

This course also satisfies the Legacy General Education requirement in Data Analysis which has the following goals and expected learning outcomes:

Goals: Students develop skills in drawing conclusions and critically evaluating results based on data.

Expected Learning Outcomes (ELOs):

1. Students understand basic concepts of statistics and probability.
2. Students comprehend methods needed to analyze and critically evaluate statistical arguments.
3. Students recognize the importance of statistical ideas.

Required Textbook: The Statistical Sleuth: A Course in Methods of Data Analysis by Ramsey F. and Schafer D. (2012), 3rd Edition, Cengage Learning, ISBN-13:978-1-13349067-8. The textbook for this course is being provided via CarmenBooks. Through CarmenBooks, students obtain publisher materials electronically through Carmen/Canvas. You can access this eBook through the CarmenBooks reader link in the course navigation of the Carmen course for this class. For more information on the program or information on how to opt out, please visit the CarmenBooks website.

You will be required to do some basic statistical analyses on the computer using the statistical software package R for your assignments. The RStudio IDE (integrated development environment) is an easy-to-use interface to R.   RStudio requires R to be installed. Both R and RStudio are free, opensource software and can be downloaded from the following websites:

• R
• RStudio

Additional information on R will be provided on the course website.

Assigments

Homework Assignments: 30%

Midterm: 30%

Final Exam: 40%

Late Assignments

No late homework assignments will be accepted with few exceptions. If you have documented reasons for missing work or needing extra time, please contact me as soon as possible prior to the due dates. Where appropriate, due dates could be extended.  

Exams

There will be one midterm exam. The midterm will be in person, during class time. Information about the exam will be posted well in advance through the course website and also announced in class. The final exam will be cumulative but will emphasize the more recent material. It will be in person, during the scheduled final exam hours. There will be no make-up exams.