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Syllabus ( MATH 216 )


   Basic information
Course title: Statistics
Course code: MATH 216
Lecturer: Assist. Prof. Hadi ALIZADEH
ECTS credits: 5
GTU credits: 3 (3+0+0)
Year, Semester: 2, Spring
Level of course: First Cycle (Undergraduate)
Type of course: Compulsory
Language of instruction: English
Mode of delivery: Face to face
Pre- and co-requisites: none
Professional practice: No
Purpose of the course: The objective of this course is to give the knowledge of evaluating the obtained biochemical data by using statistical programs and to understand the logic of data comparison.
   Learning outcomes Up

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

  1. Explain hypotheses tests

    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. Describing, formulating, and analyzing real-life problems using mathematical and statistical techniques.

    Method of assessment

    1. Written exam
  2. Apply hypotheses tests

    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.

    Method of assessment

    1. Written exam
    2. Homework assignment
  3. Interpret analysis results

    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.

    Method of assessment

    1. Written exam
    2. Homework assignment
   Contents Up
Week 1: Statistics and the Research Process
Week 2: data displays and frequency distributions
Week 3: Central tendency, Dispersion, Distributional shape,
Week 4: Central tendency, Dispersion, Distributional shape,
Week 5: The theory of statistical inference
Week 6: sampling distributions
Week 7: One-sample hypothesis tests
Week 8: Midterm exam and solutions
Week 9: Two-sample tests
Week 10: Multi-sample tests for proportions
Week 11: Multi-sample tests for means
Week 12: The idea of association
Week 13: Nominal association,
Week 14: Ordinal association,
Week 15*: -
Week 16*: Final exam
Textbooks and materials:
Recommended readings: Ş. B. Şenol, “Uygulamalı Istatistik”, E.Ü. Fen Fakültesi Yayınları, No:150, (1993)
*
K. Özdemir, “Paket Programlar ile Istatiksel Veri Analizi” 1. Cilt, Kaan Kitabevi, (2002)
*
R. V. Hogg, A. T. Groig,” Introduction to Mathematical Statistics”, (1995)
*
J. Neter, W. Wasserman, “Applied Statistics”, (1988)
  * 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: 8 40
Other in-term studies: 0
Project: 0
Homework: 0
Quiz: 0
Final exam: 16 60
  Total weight:
(%)
   Workload Up
Activity Duration (Hours per week) Total number of weeks Total hours in term
Courses (Face-to-face teaching): 3 14
Own studies outside class: 2 14
Practice, Recitation: 2 10
Homework: 1 6
Term project: 0 0
Term project presentation: 0 0
Quiz: 0 0
Own study for mid-term exam: 10 1
Mid-term: 1 1
Personal studies for final exam: 20 1
Final exam: 1 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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