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Course Description |
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Course Name |
: |
Applied Statistics |
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Course Code |
: |
İSB472 |
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Course Type |
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Optional |
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Level of Course |
: |
First Cycle |
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Year of Study |
: |
4 |
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Course Semester |
: |
Spring (16 Weeks) |
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ECTS |
: |
5 |
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Name of Lecturer(s) |
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Prof.Dr. SADULLAH SAKALLIOĞLU |
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Learning Outcomes of the Course |
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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
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Mode of Delivery |
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Face-to-Face |
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Prerequisites and Co-Prerequisites |
: |
None |
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Recommended Optional Programme Components |
: |
None |
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Aim(s) of Course |
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To expose to data analysisi process, starting from a data set to the presentation and interpretation of its statistical analysis. |
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Course Contents |
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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 |
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Language of Instruction |
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Turkish |
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Work Place |
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Faculty of Arts and Sciences Annex Classrooms |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
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1 |
Presentation and interpretation of data |
Source reading |
Lecture, discussion |
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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 |
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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 |
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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 |
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13 |
Estimation, confidence intervals, presentation and interpretation of results |
Source reading |
Lecture, discussion and problem-solving |
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14 |
Nested design |
Source reading |
Lecture, discussion |
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15 |
Survival Analysis |
Source reading |
Lecture, discussion |
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16/17 |
Final exam |
Rewview the topics discussed in the lecture notes and sources |
Written exam |
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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.
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| |
| Required Course Material(s) | |
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Assessment Methods and Assessment Criteria |
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Semester/Year Assessments |
Number |
Contribution Percentage |
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Mid-term Exams (Written, Oral, etc.) |
1 |
60 |
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Homeworks/Projects/Others |
5 |
40 |
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Total |
100 |
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Rate of Semester/Year Assessments to Success |
40 |
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Final Assessments
|
100 |
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Rate of Final Assessments to Success
|
60 |
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Total |
100 |
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| 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 |
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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 |
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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). |
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| Student Workload - ECTS |
| Works | Number | Time (Hour) | Total Workload (Hour) |
| Course Related Works |
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Class Time (Exam weeks are excluded) |
14 |
3 |
42 |
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Out of Class Study (Preliminary Work, Practice) |
14 |
3 |
42 |
| Assesment Related Works |
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Homeworks, Projects, Others |
5 |
5 |
25 |
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Mid-term Exams (Written, Oral, etc.) |
1 |
10 |
10 |
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Final Exam |
1 |
15 |
15 |
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Total Workload: | 134 |
| Total Workload / 25 (h): | 5.36 |
| ECTS Credit: | 5 |
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