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| Course Description |
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| Course Name |
: |
Analysis Of Variance And Design Of Experiments |
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| Course Code |
: |
İSB312 |
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| Course Type |
: |
Compulsory |
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| Level of Course |
: |
First Cycle |
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| Year of Study |
: |
3 |
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| Course Semester |
: |
Spring (16 Weeks) |
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| ECTS |
: |
5 |
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| Name of Lecturer(s) |
: |
Asst.Prof.Dr. GÜLESEN ÜSTÜNDAĞ ŞİRAY
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| Learning Outcomes of the Course |
: |
Explain the basic concepts of Anova and cause of used Anova.
Construct the one-way Anova and analyze it.
Check the assumptions necessary for the analysis of variance model.
Make estimation for unknown parameters in the analysis of variance model.
Generate the expected mean square error according to the fixed effect and random effect, distinguish the difference between the two.
Apply confidence intervals and hypothesis tests about the parameters .
Determine the sources of differences (which experiment or experiments) in case of rejection of the null hypothesis.
Performs Anova by using SPSS and Minitab package programs.
Construct the two-way Anova and analyze it.
Construct the Latin square and Greko-Latin square designs and analyze them.
Construct the nested and factorial designs and analyze them.
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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 |
: |
To construct the necessary theoretical background in undergraduate teaching, to analyze the data that can be faced at the public and private sectors, to gain the knowledge, skills, and practicality for interpreting the results of the analysis. |
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| Course Contents |
: |
One-way Anova, Binary and multiple comparisons, Two-way Anova, Designs of Latin Square and Greko-Latin square, Nested design, Factorial design |
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| Language of Instruction |
: |
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 |
Basic concepts of analysis of variance, construction of one-way Anova and checking the model assumptions |
Source reading |
Lecture, problem-solving |
|
2 |
Estimation of parameters and generation of the confidence intervals |
Source reading |
Lecture, problem-solving, |
|
3 |
Division of the basic sum of squares, construction of the Anova table, testing of the hypothesis, obtaining the expected mean square errors |
Source reading |
Lecture, problem-solving |
|
4 |
Binary and multiple comparisons |
Source reading |
Lecture, problem-solving |
|
5 |
Examples of Anova with SPSS and Minitab package program |
Source reading |
Using statistical package program |
|
6 |
Construction of two-way Anova (case 1), estimation of parameters, division of the sum of squares, construction of the Anova table |
Source reading |
Lecture, problem-solving |
|
7 |
Construction of two-way Anova (cases 2 and 3), estimation of parameters, division of the sum of squares, construction of the Anova table |
Source reading |
Lecture, problem-solving |
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8 |
Mid-term exam |
Review the topics discussed in the lecture notes and sources |
Written exam |
|
9 |
Obtaining the expected mean square errors for two way Anova, missing observations |
Source reading |
Lecture, problem-solving |
|
10 |
Latin square design, estimation of parameters, division of the sum of squares, construction of the Anova table |
Source reading |
Lecture, problem-solving |
|
11 |
Latin square design, estimation of parameters, division of the sum of squares, construction of the Anova table, obtaining the expected mean square errors |
Source reading |
Lecture, problem-solving, |
|
12 |
Nested design, two and there step nested designs |
Source reading |
Lecture, problem-solving |
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13 |
l step nested design,obtaining the expected mean square errors, examples with SPSS and Minitab package program |
Source reading |
Lecture, problem-solving, using statistical package program |
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14 |
Factorial design |
Source reading |
Lecture, problem-solving |
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15 |
Factorial design, examples with SPSS and Minitab package program |
Source reading |
Lecture, problem-solving, using statistical package program |
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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) |
 Semra Oral Erbaş, Hülya Olmuş, 2005 "Deney Düzenleri ve İstatistik Analizleri" Gazi Kitabevi
Birdal Şenoğlu, Şükrü Acıtaş, 2011 "İstatistiksel Deney Tasarımı Sabit Etkili Modeller" (2. Baskı) Nobel Kitabevi
Ruth M. Mickey, Olive Jean Dunn ve Virginia A. Clark “Applied Statistics: Analysis of Variance and Regression” (3. Baskı) 2004, John Wiley, New York.
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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 |
2 |
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
|
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 |
Utilize computer systems and softwares |
4 |
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2 |
Apply the statistical analyze methods |
5 |
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3 |
Make statistical inference(estimation, hypothesis tests etc.) |
5 |
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4 |
Generate solutions for the problems in other disciplines by using statistical techniques |
3 |
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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 |
5 |
|
7 |
Distinguish the difference between the statistical methods |
5 |
|
8 |
Be aware of the interaction between the disciplines related to statistics |
2 |
|
9 |
Make oral and visual presentation for the results of statistical methods |
3 |
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10 |
Have capability on effective and productive work in a group and individually |
1 |
|
11 |
Develop scientific and ethical values in the fields of statistics-and scientific data collection |
2 |
|
12 |
Explain the essence fundamentals and concepts in the field of Probability, Statistics and Mathematics |
3 |
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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 |
4 |
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16 |
Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events |
5 |
|
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 |
0 |
| * 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 |
2 |
10 |
20 |
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Mid-term Exams (Written, Oral, etc.) |
1 |
10 |
10 |
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Final Exam |
1 |
20 |
20 |
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Total Workload: | 134 |
| Total Workload / 25 (h): | 5.36 |
| ECTS Credit: | 5 |
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