Probability and Statistics for Science and Technology

Section 01 Holt 185, MoWeThFr 12:00PM - 12:50PM

Section 02 Holt 189, MoWeThFr 3:00PM - 3:50PM

Contact Information and Office Hours

Edward A. Roualdes

Office Hours:

Holt 204 Tu 12 - 12:50

Community Coding in MLIB 442 Tu 2 - 3:50 and Th 2 - 2:50

or by appointment


No textbook is required.

Additional Requirements

  • Access to a computer will be essential to master the material of this course.
  • We will learn to code in Python using Jupyter Notebooks via Anaconda.

Course Grading

Your final grade for this course will be given according to the \(+/-\) grading system, based on the following percentages and scale: \(90 - 100\), A; \(80 - < 90\), B; \(70 - < 80\), C; \(60 - < 70\), D; \(<60\), F.

Component Percentage
Labs, Worksheets, Participation, and Quizzes 15%
Homework 15%
Exam 01 20%
Exam 02 20%
Final 30%


All homework assignments will be submit to our GitHub Classroom no later than 12:00am on the date the assignment is due. Working with other students on homework is allowed, subject to the Academic Integrity Policy below. After the due date, you are allowed to turn in homework before the next exam for up to 50% credit. After this exam you will not be allowed to turn in late homework.

On all homework, minus five million points for printing not-summarized data.


There will be \(3\) tests inclusive of the final. Make-up tests are subject to the Make-Up Policy below. All exams are comprehensive and will not be given earlier than the scheduled date for your class.

Make-Up Policy

Course work can only be made-up in the case of a documented absence. To receive credit you must notify me in advance, or in the case of emergency, as soon as possible (within roughly 24 hours). All undocumented absences will result in a zero.

Getting Help

  • Me – though I reserve the right to refuse to provide help within \(24\) hours of an exam.

  • Community Coding – TuWTh 2pm - 3:50pm in Yolo 206.

  • You can visit the Math Tutor Lab on the fourth floor of Meriam Library. You should also visit your instructor during his/her office hours.

Diversity Policy

Respect: Students in this class are encouraged to speak up and participate during class meetings. Because the class will represent a diversity of individual beliefs, backgrounds, and experiences, every member of this class must show respect for every other member of this class.

Academic Integrity Policy

Students are permitted and encouraged to collaborate on all assignments other than examinations. However, each student must turn in their own work. Further, it is the expressed expectation of this instructor that all students demonstrate integrity and individual responsibility in all actions related to this course. Unethical behavior of any kind is unacceptable and will be prosecuted vigorously. Any sign of cheating in any way on any course exams or assignments will be addressed directly, according to university standards. If you do not understand what plagiarism is, or what cheating entails, you must seek information regarding this matter from the current University Catalog and from me. The consequences of plagiarism begin with a failing grade on the work, and possibly a failing grade in the course, depending upon university action. More information is found at Student Judicial Affairs.

Disability Support

If you have any disability related needs in terms of taking exams or other accommodations, please contact Disability Support Service (Colusa Hall 898-5959 or campus information 898-INFO for directions) on campus to obtain the appropriate documentation. Afterwards, come by my office and identify your needs within the first two weeks of class so that any necessary arrangements can be made.

Course Outline

  • Discrete Uniform Distribution
    • Probability
    • Random Variables
  • Estimating Proportions
    • goal of statistics
    • data basics
  • The Bernoulli Distribution
    • estimating proportions
  • Likelihood
    • optimization – by hand and on a computer
    • other random varibles so as to practice the likelihood
    • variables, data structures, common functions, defining functions, and manipulating data
  • Random Variables
    • random variables (discrete/continuous)
    • probability distribution functions,
    • parameters of distributions
    • expectation, variance, and standard deviation
  • Conditional probability
    • independence, sampling with/without replacement
    • dplyr – group_by, summarise, filter, mutate
  • Sampling Distribution
    • sampling distribution, central limit theorem, confidence intervals
  • Bootstrap
    • basic idea, standard error of mean
  • Modeling
    • mean
    • means by group
    • hypothesis tests: paired, two sample t-tests, ANOVA
    • linear regression
    • logistic regression
  • Prediciton Error
    • quantifying prediction error
    • training/testing prediction error
  • Regularization
    • improving out of sample prediction error
    • k-fold cross validation