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Course Description |
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Course Name |
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Analysis of Variance |
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Course Code |
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ISB-544 |
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Course Type |
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Optional |
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Level of Course |
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Second Cycle |
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Year of Study |
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1 |
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Course Semester |
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Spring (16 Weeks) |
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ECTS |
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6 |
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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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Have the knowledge of the terms regarding the experimental and sampling units Understand the basic logic of analysis of variance. Determine how the level of the factor affects mean response Perform parameter estimation and confidence interval Perform hypothesis testing for analysis of variance models
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Mode of Delivery |
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Face-to-Face |
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Prerequisites and Co-Prerequisites |
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None |
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Recommended Optional Programme Components |
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None |
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Aim(s) of Course |
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To teach different methods used in the design of experiments and examine how these methods are connected to statistical models. |
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Course Contents |
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Principles of experimental design; Analysis of variance models; One-way analysis of variance: Balanced case; Two-way analysis of variance: Balanced case; Analysis of variance: Unbalanced case; Analysis of covariance; Random effect models and mixed effect models |
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Language of Instruction |
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Turkish |
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Work Place |
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Department Seminar Room |
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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 |
Designing an experiment |
Reading the references |
Lecture, discussion |
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2 |
One-way analysis of variance with fixed effects |
Reading the references |
Lecture, discussion |
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3 |
Checking if the data fit model, parameter estimation, confidence intervals |
Reading the references |
Lecture, discussion and use the statistical package programs |
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4 |
One-way analysis of variance with random effects |
Reading the references |
Lecture, discussion |
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5 |
Checking if the data fit the model, parameter estimation, confidence intervals |
Reading the references |
Lecture, discussion and use the statistical package programs |
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6 |
Balanced two-way factorial design with fixed effects |
Reading the references |
Lecture, discussion |
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7 |
Balanced two-way factorial design with frandom effects |
Reading the references |
Lecture, discussion |
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8 |
Mid-term exam, homework |
Review the topics discussed in the lecture notes and sources |
Written exam |
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9 |
Unbalanced two-way factorial design with fixed effects |
Reading the references |
Lecture, discussion |
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10 |
Parameter estimation, confidence intervals |
Reading the references |
Lecture, discussion and use the statistical package programs |
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11 |
Randomized Complete block design |
Reading the references |
Lecture, discussion |
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12 |
Mixed models |
Reading the references |
Lecture, discussion |
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13 |
Estimation and confidence intervals for variance components |
Reading the references |
Lecture, discussion and use the statistical package programs |
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14 |
Repeated measures designs |
Reading the references |
Lecture, discussion |
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15 |
Analysis of variance table and F tests |
Reading the references |
Lecture, discussion |
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16/17 |
Final exam |
Review 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) |
Rencher, A. C. (2000), Linear Models in Statistics, Wiley and Sons Inc., New York.
Mickey, R.M.; Dunn, O.J.; Clark, V.A. (2004), Applied Statistics, Analysis of Variance and Regression, Wiley and Sons Inc..
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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
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100 |
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Rate of Final Assessments to Success
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60 |
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Total |
100 |
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
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1 |
Possess advanced level of theoretical and applicable knowledge in the field of Probability and Statistics. |
3 |
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2 |
Conduct scientific research on Mathematics, Probability and Statistics. |
3 |
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3 |
Possess information, skills and competencies necessary to pursue a PhD degree in the field of Statistics. |
4 |
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4 |
Possess comprehensive information on the analysis and modeling methods used in Statistics. |
5 |
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5 |
Present the methods used in analysis and modeling in the field of Statistics. |
4 |
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6 |
Discuss the problems in the field of Statistics. |
1 |
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7 |
Implement innovative methods for resolving problems in the field of Statistics. |
1 |
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8 |
Develop analytical modeling and experimental research designs to implement solutions. |
5 |
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9 |
Gather data in order to complete a research. |
3 |
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10 |
Develop approaches for solving complex problems by taking responsibility. |
3 |
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11 |
Take responsibility with self-confidence. |
3 |
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12 |
Have the awareness of new and emerging applications in the profession |
3 |
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13 |
Present the results of their studies at national and international environments clearly in oral or written form. |
3 |
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14 |
Oversee the scientific and ethical values during data collection, analysis, interpretation and announcment of the findings. |
4 |
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15 |
Update his/her knowledge and skills in statistics and related fields continously |
3 |
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16 |
Communicate effectively in oral and written form both in Turkish and English. |
3 |
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17 |
Use hardware and software required for statistical applications. |
4 |
| * 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 |
6 |
30 |
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Mid-term Exams (Written, Oral, etc.) |
1 |
8 |
8 |
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Final Exam |
1 |
18 |
18 |
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Total Workload: | 140 |
| Total Workload / 25 (h): | 5.6 |
| ECTS Credit: | 6 |
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