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  Course Description
Course Name : Applied Statistics

Course Code : İSB472

Course Type : Optional

Level of Course : First Cycle

Year of Study : 4

Course Semester : Spring (16 Weeks)

ECTS : 5

Name of Lecturer(s) : Prof.Dr. SADULLAH SAKALLIOĞLU

Learning Outcomes of the Course : Discuss how to summarize the data
Apply the rules of Probability Theory
To be able to parameter estimates
Apply parametric and non-parametric hypothesis tests
State regression models and assumptions
know the classification models
Apply survival analysis

Mode of Delivery : Face-to-Face

Prerequisites and Co-Prerequisites : None

Recommended Optional Programme Components : None

Aim(s) of Course : To expose to data analysisi process, starting from a data set to the presentation and interpretation of its statistical analysis.

Course Contents : Presenting and Summarising the Data.- Estimating Data Parameters.- Parametric Tests of Hypotheses.- Non-Parametric Tests of Hypotheses.- Statistical Classification.- Data Regression.- Data Structure Analysis.- Survival Analysis .- Directional Data

Language of Instruction : Turkish

Work Place : Faculty of Arts and Sciences Annex Classrooms


  Course Outline /Schedule (Weekly) Planned Learning Activities
Week Subject Student's Preliminary Work Learning Activities and Teaching Methods
1 Presentation and interpretation of data Source reading Lecture, discussion
2 Probability theory Source reading Lecture, discussion
3 Estimation of parameters Source reading Lecture, discussion and problem-solving
4 Parametric tests Source reading Lecture, discussion
5 Parametric tests Source reading Lecture, discussion
6 Nonparametric tests Source reading Lecture, discussion and problem-solving
7 Regression models Source reading Lecture, discussion
8 Mid-term exam Rewview the topics discussed in the lecture notes and sources Written exam
9 Regression models Source reading Lecture, discussion and problem-solving
10 One-way classification Source reading Lecture, discussion
11 Estimation of parameters, confidence intervals, presentation and interpretation of results Source reading Lecture, discussion and problem-solving
12 Two-way classification Source reading Lecture, discussion
13 Estimation, confidence intervals, presentation and interpretation of results Source reading Lecture, discussion and problem-solving
14 Nested design Source reading Lecture, discussion
15 Survival Analysis Source reading Lecture, discussion
16/17 Final exam Rewview the topics discussed in the lecture notes and sources Written exam


  Required Course Resources
Resource Type Resource Name
Recommended Course Material(s)  Bilge Aloba Köksal (2003), İstatistik-Analiz ve Metotları, Gözden Geçirilmiş 6. Baskı, Çağlayan Kitabevi
 Olive Jean Dunn, Virginia A. Clark (1987). Applied Statistics: Analysis of Variance and Regression, John Wiley & Sons; 2nd edition.
Required Course Material(s)


  Assessment Methods and Assessment Criteria
Semester/Year Assessments Number Contribution Percentage
    Mid-term Exams (Written, Oral, etc.) 1 60
    Homeworks/Projects/Others 5 40
Total 100
Rate of Semester/Year Assessments to Success 40
 
Final Assessments 100
Rate of Final Assessments to Success 60
Total 100

  Contribution of the Course to Key Learning Outcomes
# Key Learning Outcome Contribution*
1 Utilize computer systems and softwares 4
2 Apply the statistical analyze methods 4
3 Make statistical inference(estimation, hypothesis tests etc.) 4
4 Generate solutions for the problems in other disciplines by using statistical techniques 4
5 Discover the visual, database and web programming techniques and posses the ability of writing programme 0
6 Construct a model and analyze it by using statistical packages 3
7 Distinguish the difference between the statistical methods 4
8 Be aware of the interaction between the disciplines related to statistics 3
9 Make oral and visual presentation for the results of statistical methods 4
10 Have capability on effective and productive work in a group and individually 3
11 Develop scientific and ethical values in the fields of statistics-and scientific data collection 4
12 Explain the essence fundamentals and concepts in the field of Probability, Statistics and Mathematics 4
13 Emphasize the importance of Statistics in life 5
14 Define basic principles and concepts in the field of Law and Economics 0
15 Produce numeric and statistical solutions in order to overcome the problems 5
16 Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events 4
17 Use proper methods and techniques to gather and/or to arrange the data 4
18 Professional development in accordance with their interests and abilities, as well as the scientific, cultural, artistic and social fields, constantly improve themselves by identifying training needs 3
* Contribution levels are between 0 (not) and 5 (maximum).

  Student Workload - ECTS
Works Number Time (Hour) Total Workload (Hour)
Course Related Works
    Class Time (Exam weeks are excluded) 14 3 42
    Out of Class Study (Preliminary Work, Practice) 14 3 42
Assesment Related Works
    Homeworks, Projects, Others 5 5 25
    Mid-term Exams (Written, Oral, etc.) 1 10 10
    Final Exam 1 15 15
Total Workload: 134
Total Workload / 25 (h): 5.36
ECTS Credit: 5