MA251: Statistics II

Course Syllabus for "MA251: Statistics II"

Please note: this legacy course does not offer a certificate and may contain broken links and outdated information. Although archived, it is open for learning without registration or enrollment. Please consider contributing updates to this course on GitHub (you can also adopt, adapt, and distribute this course under the terms of the Creative Commons Attribution 3.0 license). To find fully-supported, current courses, visit our Learn site.

This course will introduce you to a number of statistical tools and techniques that are routinely used by modern statisticians for a wide variety of applications. First, we will review basic knowledge and skills that you learned in MA121: Introduction to Statistics. Units 2-5 will introduce you to new ways to design experiments and to test hypotheses, including multiple and nonlinear regression and nonparametric statistics. You will learn to apply these methods to building models to analyze complex, multivariate problems. You will also learn to write scripts to carry out these analyses in R, a powerful statistical programming language. The last unit is designed to give you a grand tour of several advanced topics in applied statistics.

Learning Outcomes

Upon successful completion of this course, the student will be able to:

  • apply statistical hypothesis testing for one population;
  • conduct statistical hypothesis testing and estimation for two populations;
  • apply multiple regression analysis to analyze a multivariate problem;  
  • analyze the outputs for a multiple regression model and interpret the regression results;  
  • conduct test hypotheses about the significance of a multiple regression model and test the significance of the independent variables in the model;
  • select appropriate multiple regression models using automatic model selection, forward selection, backward elimination, and stepwise selection;
  • recognize and address issues when using multiple regression analysis;
  • identify situations when nonparametric tests are appropriate;
  • conduct nonparametric tests; and
  • explain the principles underlying General Linear Model, Multilevel Modeling, Data Mining, Machine Learning, Bayesian Belief Networks, Neural Network, and Support Vector Machine.

Course Requirements

In order to take this course, you must:

√    have access to a computer;

√    have continuous broadband Internet access;

√    have the ability/permission to install plug-ins or software (e.g., Adobe Reader or Flash);

√    have the ability to download and save files and documents to a computer;

√    be able to download and install R;

√    have the ability to open Microsoft files and documents (.doc, .ppt, .xls, etc.);

√    be competent in the English language;

√    have read the Saylor Student Handbook; and

√    have completed the following course: MA121: Introduction to Statistics.

Course Information

Welcome to MA 251. Below, please find some general information on the course and its requirements.
Course Designer: Tuan Dinh
Primary Resources: This course is comprised of a range of different free, online materials. However, the course makes primary use of the following:

Requirements for Completion: In order to complete this course, you will need to work through each unit and all of its assigned materials. The quizzes are designed to test your knowledge of the topics covered in the readings and the video lectures. The assignments are designed to teach you how to apply what you learn through the readings and the video lectures to solve real-world statistical problems, using R package. In order to complete this course, you will need to earn a 70% or higher on the Final Exam. Your score on the exam will be tabulated as soon as you complete it. If you do not pass the exam, you may take it again.
Time Commitment: The materials for this course will take approximately 188 hours to complete. Note that the time advisory for each unit also contains a time advisory for creating the element of courseware described in that unit.

Table of Contents: You can find the course's units at the links below.