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Syllabus ( MATH 219 )


   Basic information
Course title: Probability and Statistics
Course code: MATH 219
Lecturer: Assist. Prof. Hadi ALIZADEH
ECTS credits: 4
GTU credits: 2 ()
Year, Semester: 2017-2018 , Fall
Level of course: First Cycle (Undergraduate)
Type of course: Compulsory
Language of instruction: Turkish
Mode of delivery: Face to face
Pre- and co-requisites: none
Professional practice: No
Purpose of the course: This course is an introduction to probability and statistics. It aims to collect, order, summarize and interpret the data which is related to the subject. In addition it aims to model the data by using various probabilistic calculations.
   Learning outcomes Up

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

  1. Analyse the data that collected for any research.

    Contribution to Program Outcomes

    1. Having the knowledge about the scope, applications, history, problems, and methodology of mathematics that are useful to humanity both as a scientific and as an intellectual discipline.
    2. Describing, formulating, and analyzing real-life problems using mathematical and statistical techniques.
    3. Ability to work in interdisciplinary research teams effectively.
    4. Adapt to a fast-changing technological environment, improving their knowledge and abilities constantly.

    Method of assessment

    1. Written exam
    2. Homework assignment
  2. Make statistical inference by using probability

    Contribution to Program Outcomes

    1. Having the knowledge about the scope, applications, history, problems, and methodology of mathematics that are useful to humanity both as a scientific and as an intellectual discipline.
    2. Describing, formulating, and analyzing real-life problems using mathematical and statistical techniques.
    3. Ability to work in interdisciplinary research teams effectively.
    4. Having improved abilities in mathematics communications, problem-solving, and brainstorming skills.

    Method of assessment

    1. Written exam
    2. Homework assignment
  3. Know the statistical distributions and make modelling.

    Contribution to Program Outcomes

    1. Having the knowledge about the scope, applications, history, problems, and methodology of mathematics that are useful to humanity both as a scientific and as an intellectual discipline.
    2. Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.
    3. Ability to work in interdisciplinary research teams effectively.

    Method of assessment

    1. Written exam
   Contents Up
Week 1: Basic statistical concepts, data, population, sampling
Week 2: Data collecting, Frequency Tables, Graphs
Week 3: Data summarizing, Location Parameters
Week 4: Scale Parameters
Week 5: Combination, Permutation, Ordering
Week 6: Probability
Week 7: Random variables, Probability (density) functions
Week 8: Bernouilli, Binom, Geometric and Pascal Distributions
Week 9: Midterm Exam and solutions
Week 10: Poisson Distribution and Normal Distribution
Week 11: Z Table and Central Limit Theorem
Week 12: Other Continious Distributions
Week 13: Regression Analysis, Estimation the coefficients
Week 14: Regression Analysis, Interpretation
Week 15*: -
Week 16*: Final Exam
Textbooks and materials: • Probability of Statistics for Engineering & Scientists. Walpole E.W., Myers R.H., Myers S.L., Ye K. Pearson Education, Prentice Hall Inc.
• Probability Random Variables and Stochastic Processes, A.Papolis, McGraw Hill

Recommended readings: • Akdeniz Fikri, Olasılık ve İstatistik, Akademisyen Kitabevi
• Schervish M.J. and DeGroot M.H., Probability and Statistics, McGraw Hill
  * Between 15th and 16th weeks is there a free week for students to prepare for final exam.
Assessment Up
Method of assessment Week number Weight (%)
Mid-terms: 9 30
Other in-term studies: 0
Project: 0
Homework: 3, 6, 12, 15 30
Quiz: 0
Final exam: 16 40
  Total weight:
(%)
   Workload Up
Activity Duration (Hours per week) Total number of weeks Total hours in term
Courses (Face-to-face teaching): 2 14
Own studies outside class: 1 14
Practice, Recitation: 0 0
Homework: 10 4
Term project: 0 0
Term project presentation: 0 0
Quiz: 0 0
Own study for mid-term exam: 10 1
Mid-term: 2 1
Personal studies for final exam: 10 1
Final exam: 2 1
    Total workload:
    Total ECTS credits:
*
  * ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
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