Textbook Information
- Modern Mathematical Statistics with Applications (2nd Edition) by Jay Devore and Kenneth Berk
You can access the textbook by clicking the following link: Textbook. At this link, you can access both PDF and EPUB versions; any reference to specific page numbers from the text will be reflected in the PDF version.
Published Remarks
- This course may require you to take exams using certain proctoring software that uses your computer’s webcam or other technology to monitor and/or record your activity during exams. The proctoring software may be listening to you, monitoring your computer screen, viewing you and your surroundings, recording and storing any and all activity (including visual and audio recordings) during the proctoring process. By enrolling in this course, you consent to the use of the proctoring software selected by your instructor, including but not limited to any audio and/or visual monitoring which may be recorded. You will need to use an approved browser and one of the compatible operating systems which are listed in Honorlock’s Minimum Requirements table. Please contact your instructor with any questions.
Hardware Requirements
- Computer
Webcam for use with Honorlock, which is a proctoring software for exams
Software Requirements
- You will need to use an approved browser and one of the compatible operating systems which are listed in Honorlock’s Minimum Requirement table (https://honorlock.com/support/).’
Proctored Exams
- This course uses Honorlock to proctor exams.
Course Description
This course serves as an introduction to Probability. First, we will give a brief review of descriptive statistics: in this, we'll become familiar with basic terminology involving data sets, populations, and sampling; we will also compute measures of location and measures of variability for numerical data sets. Then, as we begin our study of probability, we will see how outcomes can be grouped into sets and how these sets can relate to one another (Set Theory). Then, we will learn how to compute the probability of any of these sets occurring, and the rules for doing this. We will spend a large portion of the course in discussing how groups of values can have their own "probability distribution". Such values are represented by what are called "random variables," and a random variable could represent the number of Heads obtained when flipping a coin 10 times in a row, or the height of a randomly selected person in a population. For these "distributions," we will learn how to compute the average (or expected) value and compute (on average) how often each value in the distribution varies from this. We will also learn to compute the probability of value(s) occurring within these distributions. This course will culminate with the Central Limit Theorem, which allows us to approximate probabilities in certain distributions with probabilities in the so-called "normal distribution."
Statistics is used throughout the sciences and engineering, and probability is foundational to the subject of statistics. This is one of the reasons why probability is so important. Also, probability is very useful in its own right. For example, all machines have parts that will, in time, wear out and need to be replaced. Probability allows us to model the lifetime of such parts and to compute the average length of time they will last, or to compute the probability of how long they will last past a certain point in time.
Prerequisite: Math 141
Objectives
By the end of this course, students will be expected to demonstrate competency in the following areas:
- Solve problems using rules of probability and set theory, including problems dealing with independent events and conditional probabilities.
- Calculate expected values and variances of Discrete and Continuous random variables
- Solve probability problems related to discrete and continuous distributions
Outcomes
Upon completion of the course, students should possess the following skills:
- Be able to write probability problems in terms of sets, including complements, unions, and intersections.
- Be able to employ appropriate counting techniques and rules of probability, such as conditional probabilities, the multiplication rule, and the addition rule.
- Be able to compute the expected value and variance of discrete and continuous random variables.
- Be able to employ the Central Limit Theorem.
Course Requirements and Grading
1. Grading Policy
Grading is based on midterm exams, quizzes, and homework assignments. The following weights are assigned to the different assessed components of the course:
| Category | Evaluation | Percentage |
| Midterm Exam 1 | Individual | 20% |
| Midterm Exam 2 | Individual | 20% |
| Midterm Exam 3 | Individual | 20% |
| Midterm Exam 4 | Individual | 20% |
| Turn-in Assignments (each module) | Individual | 10% |
| Quizzes (each module) | Individual | 10% |
Quizzes: The quizzes are taken, online, in Canvas. The problems in the quizzes will be very similar to the examples illustrated in the Modules. Quizzes are assigned on a weekly basis. Quizzes must be submitted by Monday night (11:59pm, US Eastern time) of the following week. Turn-in Assignments: Along with the weekly quizzes, there will be weekly turn-in assignments. The problems on these will sometimes also be similar to the examples in the modules. The problems may also consist of proofs and derivations. For the problems in these assignments, all work must be shown. Turn-in Assignments are assigned on a weekly basis. They are due by Monday night (11:59pm, US Eastern time) of the following week. Exams: An exam will be given for each of the four units of this course. The exams will be taken, online, in Canvas (similar to the quizzes). This course requires you to have a webcam for the exams. Again, exams will be conducted in Canvas and proctored via Honorlock, and your webcam will be used for monitoring and proctoring exams. Please contact your instructor if you are have any questions or concerns. 2. Grade Assignments Final grades are based on 100 percentage points. Final letter grades are assigned as follows:
| A | A- | B+ | B | B- | C+ | C | D | F |
| 92-100% | 90-91% | 88-89% | 82-87% | 80-81% | 78-79% | 70-77% | 60-69% | 0-59% |