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| Course Description |
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| Course Name |
: |
Statistics |
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| Course Code |
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TBP104 |
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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) |
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Prof.Dr. ZEYNEL CEBECİ
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| Learning Outcomes of the Course |
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Plans and organizes the simple experiments and gains skills in organizing and summarizing empirical data
Understands the probability theory and gains the skills in calculating simple probabilities
Proficiency in basic inferential statistical data analysis
Gains skills in basic analyses of paired data
Gains skills in computing basic descriptive statistics on univariate data
Gains skills in understanding and interpreting the relationships between two variables
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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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This course aims to teach to interprete results of a variety of statistical techniques from both descriptive and inferential statistics; to understand the fundamental concepts in statistics including sampling, experimentation, variability, distribution, association, causation, estimation, confidence, hypothesis testing, and significance; to critically review and analyze statistical arguments |
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| Course Contents |
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Topics include both descriptive and inferential statistics: variables; graphical analysis of one or more qualitative and quantitative variables; numerical summaries to measure characteristics such as the center of a distribution, variation in a distribution, and symmetry or skewness in a distribution; random sampling; the normal distribution; the Central Limit Theorem; one and two sample hypothesis tests and confidence intervals involving means and proportions; one-way analysis of variance; the chi-square goodness-of-fit test; the chi-square test concerning independence in a two-way contingency table; Pearson correlation and testing for significance with simple linear regression |
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| Language of Instruction |
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Turkish |
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| Work Place |
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In classroom |
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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 |
Introduction to statistics, definition of terms and basic concepts, statistical symbols, data and variables, variable types |
Search for learning resources on "Introduction to statistics" on the Internet, and reading the texts about definition and goals of statistics |
Reading |
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2 |
Populations, samples, and samplings
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Reading the relevant topic chapters from printed and/or e-books |
Reading |
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3 |
Numerical and graphical summarizing data,
frequency tables, histograms, charts, type of graphics, Interpretation of distributions |
Download and setup R statisctical package from The Comprehensive R archive Network, and search for R tutorials on the Internet and download some of them |
Internet/computer work |
|
4 |
Measures of central tendency, arithmetic mean, weighted mean, harmonic mean, geometric mean, truncated mean, mode, median, quartiles, quantiles and percentiles |
Reading the relevant topic chapters from printed and/or e-books |
Reading |
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5 |
Measures of variation/dispersion: Range, Variance, Standard deviation, Coefficient of variation, Skewness and Curtosis, Standard error of mean |
Reading the relevant topic chapters from printed and/or e-books |
Reading |
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6 |
Introduction to probability: Probability rules, experiment, sample space, simple event, event, complement of an event, union and intersections of events, probability of an event, conditional probabilities, independent events, mutually exclusive events and Venn diagrams |
Reading the relevant topic chapters from printed and/or e-books.
Download and setup Java applets related with probability distributions, and run some simple exercises on calculation of probabilities |
Reading and exercise with Java applets |
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7 |
Discrete probability distributions: Binomial distributions, Poisson distributions
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Reading the relevant topic chapters from printed and/or e-books |
Reading |
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8 |
Continous probability distributions: Normal probability distributions, Standard normal distributions, standardizing the data |
Reading the relevant topic chapters from printed and/or e-books. |
Reading |
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9 |
Mid term exam |
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|
10 |
Interval estimation of a population proportion and estimation of a population mean, standard deviation known and unknown; determining sample size |
Reading the relevant topic chapters from printed and/or e-books |
Reading |
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11 |
Developing null and alternative hypothesis; type I and type II errors; tests about a population mean and proportion |
Reading the relevant topic chapters from printed and/or e-books |
Reading |
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12 |
Tests about differences between the means of two populations, independent samples and matched samples, Tests about the differences in proportion. Hypothesis tests about population variances and two population variances |
Reading the relevant topic chapters from printed and/or e-books |
Reading |
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13 |
Chi-square analysis: Goodness of fit test; test of independence using contingency tables |
Reading the relevant topic chapters from printed and/or e-books |
Reading |
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14 |
Analysis of bivariate data: Estimation of correlation ve linear regression, scatter plots |
Reading the relevant topic chapters from printed and/or e-books |
Reading |
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15 |
Final exam preperation |
. |
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16/17 |
Final exam |
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Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 IŞIK, A. (2006). Applied Statistics - I. Beta Basım Yayım Dağıtım A.Ş. 510 s. (ISBN 975-295-544-4). (in Turkish)
IŞIK, A. (2006). Applied Statistics - II. Beta Basım Yayım Dağıtım A.Ş. 700 s. ((ISBN 975-295-582-7). (in Turkish)
ATIL, H. (1998). Statistics. Ege Üniversitesi Ziraat Fakültesi Yayınları No: 531. 196 s. Ege Üniversitesi Ofset Atölyesi, Bornova - İzmir (in Turkish)
KAYAALP, G.T. & S. ÇANKAYA (2004). Statistics. Ç.Ü. Ziraat Fakültesi Genel Yayın No: 258, Ders Kitapları Yayın No: A-84. 122 s. Adana
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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 |
4 |
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 |
Has knowledge about agricultural engineering as well as agronomy and breeding of field crops. |
1 |
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2 |
Determines and solves the problems related to agricultural engineering as well as agronomy and breeding of field crops. |
4 |
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3 |
Graduates gain abilty to synthetize the basic concepts related to the field crops.
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1 |
|
4 |
Rrecognises problems related to agricultural engineering,makes decisions and takes initiative to solve the problems. |
2 |
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5 |
Gains knowledge about sustainable agriculture, protection of environment and natural sources, biodiversity and conservation of genetic sources. |
0 |
|
6 |
Gains ability to optimize the plant production by sustainable use of natural resources. |
1 |
|
7 |
Learns basic principles of breeding and biotechnology of field crops. |
4 |
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8 |
Chooses and uses modern technical equipments for the agricultural engineering applications as well as for the applications in the agronomy and breeding of field crops. |
3 |
|
9 |
Gains ability to establish suitable research experiments for the purpose and the ability to interpret its results by scientific methods. |
5 |
|
10 |
Works both individually and in a team. |
5 |
|
11 |
Internalizes the necessity of lifelong learning. |
0 |
|
12 |
Has an effective and healthy communication in his fıeld and use communication technologies. |
3 |
|
13 |
Improve themselves consistently by determining educational requirements in scientific, cultural and social areas depending on their abilities,besides their career development |
3 |
|
14 |
Shows respect to job ethic. |
0 |
|
15 |
Becomes competent in the legislation and management systems related to agricultural engineering. |
2 |
|
16 |
Becomes proficient in doing, applying, managing and monitoring plans and projects about agricultural engineering |
4 |
|
17 |
Evaluates the learned knowledge by analytical and critical approach. |
5 |
| * 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) |
15 |
3 |
45 |
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Out of Class Study (Preliminary Work, Practice) |
15 |
2 |
30 |
| Assesment Related Works |
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Homeworks, Projects, Others |
4 |
3 |
12 |
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Mid-term Exams (Written, Oral, etc.) |
1 |
2 |
2 |
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Final Exam |
1 |
2 |
2 |
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Total Workload: | 91 |
| Total Workload / 25 (h): | 3.64 |
| ECTS Credit: | 4 |
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