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| Course Description |
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| Course Name |
: |
Statistical Thinking |
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| Course Code |
: |
İSB152 |
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| Course Type |
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Compulsory |
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| Level of Course |
: |
First Cycle |
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| Year of Study |
: |
1 |
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| Course Semester |
: |
Spring (16 Weeks) |
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| ECTS |
: |
4 |
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| Name of Lecturer(s) |
: |
Prof.Dr. SADULLAH SAKALLIOĞLU
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| Learning Outcomes of the Course |
: |
Learn how to decide on Statistics
Understand the concepts of unit, variable, sample, population
Learn to select sample
Understand of principles for planning and experiment
Be able to summarize the data in a graphical
Be able to summarize the data in numerical
Learn to model of the relationship between the variables
Learn how to measure uncertainty with probabilty
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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 |
: |
The purpose of this course is to provide students an introduction to major concepts and tools of collecting, analyzing and interpreting data. |
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| Course Contents |
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Statistical Decision Making (testing of hypotesis, collecting data), Summarizing data graphically (Frequency plots, stem and leaf plots, histograms, time plots) and numericaly (Mean, median, mode). Introduction to observational studies and experiments, Measure uncertainty with probabilty. |
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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 |
How to make a decision with statistics |
Source reading |
Lecture, discussion and problem-solving |
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2 |
Explanation of null hypothesis, alternative hypothesis, decision rule, rejected and accepted regions, types of errors |
Source reading |
Lecture, discussion and problem-solving |
|
3 |
Explanation of the concetps unit, variable, sample, population, parameter and statistics. |
Source reading |
Lecture, discussion and problem-solving |
|
4 |
The language of sampling. Sampling methods |
Source reading |
Lecture, discussion and problem-solving |
|
5 |
Random numbers, Simple Random Sampling, Stratified Random Sampling |
Source reading |
Lecture, discussion and problem-solving |
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6 |
Systematic Sampling, Cluster Sampling, Multistage sampling |
Source reading |
Lecture, discussion and problem-solving |
|
7 |
Introduction to observational studies and experiments. Understanding observational studies |
Source reading |
Lecture, discussion and problem-solving |
|
8 |
Mid-term exam |
Rewview the topics discussed in the lecture notes and sources |
Written exam |
|
9 |
Response variable, Explanatory variable, |
Source reading |
Lecture, discussion and problem-solving |
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10 |
Principles of planning an experiment |
Source reading |
Lecture, discussion and problem-solving |
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11 |
Summarizing data graphically (Types of variables, Distribution of a variable, Pie Charts, Bar Graphs) |
Source reading |
Lecture, discussion and problem-solving |
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12 |
Summarizing data graphically (Displaying relationships between two qualitative variables, Frequency Plots, Histogram, Stem-and-leaf plots) |
Source reading |
Lecture, discussion and problem-solving |
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13 |
Summarizing data numerically (Mean, Median, Mode) |
Source reading |
Lecture, discussion and problem-solving |
|
14 |
Summarizing data numerically (Range, Quartiles, Interquartile range, Standart deviation) |
Source reading |
Lecture, discussion and problem-solving |
|
15 |
Using models to make decisions, Measure uncertainty with probabilty. |
Source reading |
Lecture, discussion and problem-solving |
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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) |
 M. Aliaga and B. Gunderson (2003) Interactive Statistics (Second Edition), Prentice Hall.
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| |
| Required Course Material(s) |
J.M Utts and R. F. Heckard (2002) Mind on Statistics, Duxbury
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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 |
80 |
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Homeworks/Projects/Others |
10 |
20 |
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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* |
|
1 |
Utilize computer systems and softwares |
0 |
|
2 |
Apply the statistical analyze methods |
5 |
|
3 |
Make statistical inference(estimation, hypothesis tests etc.) |
5 |
|
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 |
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6 |
Construct a model and analyze it by using statistical packages |
0 |
|
7 |
Distinguish the difference between the statistical methods |
4 |
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8 |
Be aware of the interaction between the disciplines related to statistics |
5 |
|
9 |
Make oral and visual presentation for the results of statistical methods |
5 |
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10 |
Have capability on effective and productive work in a group and individually |
0 |
|
11 |
Develop scientific and ethical values in the fields of statistics-and scientific data collection |
5 |
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12 |
Explain the essence fundamentals and concepts in the field of Probability, Statistics and Mathematics |
5 |
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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 |
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17 |
Use proper methods and techniques to gather and/or to arrange the data |
5 |
|
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 |
2 |
28 |
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Out of Class Study (Preliminary Work, Practice) |
14 |
2 |
28 |
| Assesment Related Works |
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Homeworks, Projects, Others |
10 |
2 |
20 |
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Mid-term Exams (Written, Oral, etc.) |
1 |
10 |
10 |
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Final Exam |
1 |
10 |
10 |
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Total Workload: | 96 |
| Total Workload / 25 (h): | 3.84 |
| ECTS Credit: | 4 |
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