# Course

 Course Number: ENGRG 6050 Course Name: Applied Statistics (Online) Course Description: This course is an on-line introductory course in statistics. This foundation course is designed to prepare a student for study in the Master of Science in Engineering program or the Master of Science in Project Management program. This course will cover basic concepts of probability, discrete and continuous random variables, confidence intervals, hypothesis testing, and applications of statistics including simple linear regression, multiple regression, basic design of experiments and ANOVA. This course is not appropriate for students seeking a MS or MA degree in mathematics. Prerequisites: MATH 2740 with a grade of "C" or better. Level: Graduate Credits: 3 Format: Online Program: Masters of Science in EngineeringMasters of Science in Project Management
Registration Instructions

NOTE: The information below is representative of the course and is subject to change. The specific details of the course will be available in the Desire2Learn course instance for the course in which a student registers.

Learning Outcomes
Upon completion of this course, you should be able to:
1. Summarize data both graphically and numerically.
2. Compute and interpret probabilities.
3. Analyze data using an appropriate hypothesis test or confidence interval.
4. Conduct appropriate analyses using software for simple and multiple linear regression, control charts, and experimental design.
5. Analyze assumptions for all analyses discussed.
6. Communicate the results of an analysis in a professional manner.

Unit Descriptions
Unit 1 Introduction
This first unit introduces you to some of the basic ideas and principles that provide a background for the application of statistics. You will learn how to describe data using graphical and numerical methods as well as the basic concepts of probability that underlie the methods we will cover in later units. Much of this unit will be review for many of you. If it is a review for you, be sure that you have a firm understanding of the concepts before you move on.
Some students may find the chapter 3 material (on probability and counting methods) to be difficult, if you do â€“ hang in there. It does get easier once this material is completed. You can always send me an email or give me a call if you are having trouble.
• Chapter 1 Introduction
• Chapter 2 Describing Data
• Chapter 3 Probability
Unit 2 Random Variables
In this unit, we will learn about the two different types of random variables and how to calculate means and variances for each. Lesson 3 covers discrete random variables and Lesson 4 covers continuous random variables. These two lessons consist of background material used in the remaining units. Lesson 5 introduces the idea of statistical inference, where you will learn how to appropriately create and use basic confidence intervals and hypothesis tests.
• Chapter 4 Discrete Probability Distributions
• Chapter 5 Continuous Probability Densities
• Chapter 6 Sampling Distributions
• Chapter 7 Inferences Concerning a Mean
Unit 3 Estimation and Inference
In this unit we get to the meat-and-potatoes of statistics. You will continue to learn how to construct confidence intervals used to estimate each of the population parameters you have learned about. You will also learn how to construct confidence intervals that compare parameters from different populations. Hypothesis tests are a method of inference used to make decisions about population parameters. Be sure to continue to pay attention to the commentary where hypothesis test steps are outlined, as you must include these steps in every test that you do. We will also learn about hypothesis tests for categorical variables.
• Chapter 8 Comparing Two Treatments
• Chapter 9 Inferences Concerning Variances
• Chapter 10 Inferences Concerning Proportions
• Chapter 11 Simple Linear Regression (sections 1, 2, 5 &6)
Unit 4 Applications
In this final unit we are going to cover three major topics: multiple linear regression, design of experiments, and control charts. Multiple Linear Regression is covered in Lesson 9 and will finish the remainder of Chapter 11. Multiple Linear Regression is different from Simple Linear Regression (which you learned about in Lesson 8) in that we are now using 2 or more independent variables to try to explain what is happening with the dependent variable (a much more realistic situation).
In Lesson 10 you will learn the basics of experimental design and how to analyze the results of basic designs. You will use Minitab to carry out all of the analyses, as they are very time consuming if done by hand. An introduction to Control charts will be covered in lesson 11.  There are many different types of control charts that will be covered, so pay attention to when it is appropriate to use each one.  Control Charts are used in industry to help determine when a process may be going off track.
• Chapter 11 Multiple Linear Regression (sections 3 & 4)
• Chapter 12 Analysis of Variance
• Chapter 13 Factorial Experimentation
• Chapter 15 The Statistical Content of Quality Improvement Programs (Control Charts)

Exams:  4
Assignments:
Scores on weekly homework, group projects, online discussions, and the exams will determine the final grades.
• There will be 11 homework assignments, one for each lesson, worth 20 possible points each. After they are graded, homework assignments may be resubmitted to earn back up to half of the missed points.  The last date for re-submission is the date the exam covering the corresponding material is due.
• For each online discussion, students are required to write an original post as well as respond to at least one of their classmatesâ€™ postings in a non-trivial manner.
• Each of the five group projects will be 20 points.
• There will be four exams, each worth 100 points.
• The exams are open book, however they are to be completed individually with no assistance from outside resources. Students who do not comply will receive a failing grade for this course and be reported to the Dean of Students. A notation may be made on your transcript also.