# STAT 501: Regression Methods

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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. Generally, statistical regression is collection of methods for determining and using models that explain how a response variable (dependent variable) relates to one or more explanatory variables (predictor variables).

This graduate level course covers the following topics:

• Understanding the context for simple linear regression.
• How to evaluate simple linear regression models
• How a simple linear regression model is used to estimate and predict likely values
• Understanding the assumptions that need to be met for a simple linear regression model to be valid
• How multiple predictors can be included into a regression model
• Understanding the assumptions that need to be met when multiple predictors are included in the regression model for the model to be valid
• How a multiple linear regression model is used to estimate and predict likely values
• Understanding how categorical predictors can be included into a regression model
• How to transform data in order to deal with problems identified in the regression model
• Strategies for building regression models
• Distinguishing between outliers and influential data points and how to deal with these
• Handling problems typically encountered in regression contexts
• Alternative methods for estimating a regression line besides using ordinary least squares
• Understanding regression models in time dependent contexts
• Understanding regression models in non-linear contexts

Here is a link to the Online Notes for STAT 501.

STAT 500, Matrix Algebra (see Review)

The textbook is required, and either of the two editions below are acceptable.  Here are the two options for the required textbook for this course. Students may use either:

The larger Applied Linear Statistical Models by Kutner, Nachtsheim, and Neter (5th edition) OR the smaller Applied Linear Regression Models by the same authors, Kutner, Nachtsheim, and Neter (4th edition).

The first half of the larger Applied Linear Statistical Models contains sections on regression models, the second half on analysis of variance and experimental design. This first half of the 5th edition text is available published as Applied Linear Regression Models by Kutner, Nachtsheim, and Neter (4th edition).

Students may use either textbook listed as they are identical.

The larger Applied Linear Statistical Models also includes 16 chapters on analysis of variance and experimental design not covered in this course, however these topics are covered in STAT 502 where these chapters are required. Students may consider purchasing the larger text if they are taking both courses. Applied Linear Statistical Models is considered to be one of the "bibles" of applied statistics so it probably will have value to you beyond this course.

This course uses Minitab statistical software. Students can use any software they wish for assignments, but most will find it easiest to use Minitab. Plus, examples for the course units will be demonstrated using Minitab. See the Statistical Software page for more information about obtaining a copy of Minitab.

A mixture of (nearly) weekly data analysis assignments and non-computer assignments constitute 55% of the grade. Two midterm exams and a final exam constitute the other 45% of the grade. These exams are proctored.

All Penn State policies regarding ethics and honorable behavior apply to this course. Academic integrity is the pursuit of scholarly activity free from fraud and deception and is an educational objective of this institution. All University policies regarding academic integrity apply to this course. Academic dishonesty includes, but is not limited to, cheating, plagiarizing, fabricating of information or citations, facilitating acts of academic dishonesty by others, having unauthorized possession of examinations, submitting work of another person or work previously used without informing the instructor, or tampering with the academic work of other students.

For any material or ideas obtained from other sources, such as the text or things you see on the web, in the library, etc., a source reference must be given. Direct quotes from any source must be identified as such.

All exam answers must be your own, and you must not provide any assistance to other students during exams. Any instances of academic dishonesty WILL be pursued under the University and Eberly College of Science regulations concerning academic integrity. For more information on academic integrity, see Penn State's statement on plagiarism and academic dishonesty.

The Eberly College of Science Code of Mutual Respect and Cooperation embodies the values that we hope our faculty, staff, and students possess and will endorse to make The Eberly College of Science a place where every individual feels respected and valued, as well as challenged and rewarded.

Penn State welcomes students with disabilities into the University's educational programs. If you have a disability-related need for reasonable academic adjustments in this course, contact the Office for Disability Services (ODS) at 814-863-1807 (V/TTY). For further information regarding ODS, please visit the Office for Disability Services Web site at http://equity.psu.edu/ods/.