BSc: Probability And Statistics.f22

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Probability and Statistics

  • Course name: Probability and Statistics
  • Code discipline: CSE206
  • Subject area: Math

Short Description

The course is designed to provide Software Engineers and Computer Scientists by correct knowledge of basic (core) concepts, definitions, theoretical results and applied methods & techniques of Probability Theory and Mathematical Statistics. The main idea of the course is to study mathematical basis of modelling random experiments. The course includes constructing a probability space, a model of a random experiment, and its applications to practice. After that, random variables and their properties are considered. As examples of applying this theoretical background, limit theorems of probability theory are proved (law of large numbers, central limit theorem) and some elements of mathematical statistics are studied.

Course Topics

Course Sections and Topics
Section Topics within the section
Basics of Probability
  1. Sampling Procedures, Collection of Data
  2. Measures of Location: The Sample Mean and Median, Measures of Variability, Discrete and Continuous Data
  3. Sample Space, Events, Probability of an Event, Additive Rules, Conditional Probability, Bayes’ Rule
  4. Discrete Probability Distributions. Continuous Probability Distributions, Joint Probability Distributions
  5. Mean of a Random Variable, Variance and Covariance of Random Variables, Chebyshev’s Theorem
Some Probability Distributions
  1. Binomial and Multinomial Distributions,
  2. Hypergeometric Distribution,
  3. Poisson Distribution and the Poisson Process
  4. Some Continuous Probability Distributions
  5. Uniform, Normal, Gamma, Exponential, Chi-Squared, Beta, Lognormal, Weibull Distributions
Basics of Statistics
  1. Sampling Distribution of Means and the Central Limit Theorem.
  2. t-Distribution, F-Distribution,Quantile and Probability Plots
  3. Estimating the Mean, Proportion, Variance, Differences, Maximum Likelihood
  4. Testing a Statistical Hypothesis, Test for Independence, Test for Homogeneity
  5. Least Squares and the Fitted Model, Choice of a Regression Model,
  6. Analysis-of-Variance Approach

Intended Learning Outcomes (ILOs)

What is the main purpose of this course?

This calculus course will provide an opportunity for participants to:

  • understand key principles involved in differentiation and integration of functions
  • solve problems that connect small-scale (differential) quantities to large-scale (integrated) quantities
  • become familiar with the fundamental theorems of Calculus
  • get hands-on experience with the integral and derivative applications and of the inverse relationship between integration and differentiation.

ILOs defined at three levels

We specify the intended learning outcomes at three levels: conceptual knowledge, practical skills, and comprehensive skills.

Level 1: What concepts should a student know/remember/explain?

By the end of the course, the students should be able to ...

  • know the probability function and its properties
  • know the law of total probability and Bayes’ theorem
  • explain the independence of events and of random variables
  • know the different continuous distributions
  • know the multivariate distributions for discrete and continuous cases
  • know the maximum likelihood estimator method

Level 2: What basic practical skills should a student be able to perform?

By the end of the course, the students should be able to ...

  • construct a mathematical model of a random experiment (probability space)
  • calculate conditional probabilities
  • use probability generating functions for discrete random variables
  • find confidence intervals for parameters of a normal distribution
  • estimate unknown parameters of distributions

Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios?

By the end of the course, the students should be able to ...

  • find expected value, variance and other characteristics of a random variable
  • apply limit theorems (law of large numbers and central limit theorem)
  • find parameters of a simple linear regression

Grading

Course grading range

Grade Range Description of performance
A. Excellent 90-100 -
B. Good 75-89 -
C. Satisfactory 60-74 -
D. Fail 0-59 -

Course activities and grading breakdown

Activity Type Percentage of the overall course grade
Midterm 20
Quizzes 28 (2 for each)
Final exam 50
In-class participation 7 (including 5 extras)

Recommendations for students on how to succeed in the course

  • Participation is important. Attending lectures is the key to success in this course.
  • Review lecture materials before classes to do well.
  • Reading the recommended literature is obligatory, and will give you a deeper understanding of the material.

Resources, literature and reference materials

Open access resources

  • Jerrold E. Marsden and Alan Weinstein, Calculus I, II, and II. Springer-Verlag, Second Edition 1985
  • Zorich, V. A. Mathematical Analysis I, Translator: Cooke R. (2004)

Software and tools used within the course

  • No.

Activities and Teaching Methods

Teaching and Learning Methods within each section
Teaching Techniques Section 1 Section 2 Section 3
Problem-based learning (students learn by solving open-ended problems without a strictly-defined solution) 1 1 1
Project-based learning (students work on a project) 0 0 0
Modular learning (facilitated self-study) 0 0 0
Differentiated learning (provide tasks and activities at several levels of difficulty to fit students needs and level) 1 1 1
Contextual learning (activities and tasks are connected to the real world to make it easier for students to relate to them) 0 0 0
Business game (learn by playing a game that incorporates the principles of the material covered within the course) 0 0 0
Inquiry-based learning 0 0 0
Just-in-time teaching 0 0 0
Process oriented guided inquiry learning (POGIL) 0 0 0
Studio-based learning 0 0 0
Universal design for learning 0 0 0
Task-based learning 0 0 0
Activities within each section
Learning Activities Section 1 Section 2 Section 3
Lectures 1 1 1
Interactive Lectures 1 1 1
Lab exercises 1 1 1
Experiments 0 0 0
Modeling 0 0 0
Cases studies 0 0 0
Development of individual parts of software product code 0 0 0
Individual Projects 0 0 0
Group projects 0 0 0
Flipped classroom 0 0 0
Quizzes (written or computer based) 1 1 1
Peer Review 0 0 0
Discussions 1 1 1
Presentations by students 0 0 0
Written reports 0 0 0
Simulations and role-plays 0 0 0
Essays 0 0 0
Oral Reports 0 0 0

Formative Assessment and Course Activities

Ongoing performance assessment

Section 1

  1. A plane curve is given by , . Find
    the asymptotes of this curve;
    the derivative .
  2. Apply Leibniz formula Find if .
    Draw graphs of functions
    Find asymptotes
  3. Find the derivatives of the following functions:
    • ;
    • that is given implicitly by .

Section 2

  1. Find the following integrals:
    • ;
    • ;
    • .
  2. Find the indefinite integral .
  3. Find the length of a curve given by , .

Section 3

  1. Find limits of the following sequences or prove that they do not exist:
    • ;
    • ;
    • .

Final assessment

Section 1

  1. Apply the appropriate differentiation technique to a given problem.
  2. Find a derivative of a function
  3. Apply Leibniz formula
  4. Draw graphs of functions
  5. Find asymptotes of a parametric function

Section 2

  1. Apply the appropriate integration technique to the given problem
  2. Find the value of the devinite integral
  3. Calculate the area of the domain or the length of the curve

Section 3

  1. Find a limit of a sequence
  2. Find a limit of a function

The retake exam

Retakes will be run as a comprehensive exam, where the student will be assessed the acquired knowledge coming from the textbooks, the lectures, the labs, and the additional required reading material, as supplied by the instructor. During such comprehensive oral/written the student could be asked to solve exercises and to explain theoretical and practical aspects of the course.