STATISTICS
- Course
- STAT502 - STATISTICS
- Department
- Basic Sciences and Humanities
- Course Type
- Course
- Status
- Required
- Language
- English
- Credit
- 3
- ECTS
- 0
- T+P+L
- 3 + 0 + 0
- Course Coordinator(s)
- -
- Prerequisite
- -
- Keywords
- -
Course Description
This course is designed to introduce basic statistical techniques necessary for carrying out a scientific research. It covers descriptive statistics and inferential statistics; variables and levels of measurement; measures of central tendency; display data by using graphs, charts, histograms, tables, etc.; a population and samples drawn from it; measures of variability of given data; the normal curve; the concept of probability in making statistical decisions; distributions of sample means; intervals for making statistical inference, hypothesis testing; correlation to detect relationships between and among phenomena; regression analysis; hypothesis testing to make statistical inference; tests (t-test) for the difference between the population means; the null hypothesis; analysis of variance (ANOVA), and analysis of covariance (ANCOVA); non-parametric techniques (chi-square test) for nominal data; other non-parametric techniques.
STATISTICS
Evaluation Tools (Active Term)
No evaluation items have been defined.
Course outcomes
- 01 They analyse the descriptive statistics of psychological data sets in SPSS. 4
- 02 They summarize the data sets by using graphs in SPSS 4
- 03 They can hypothesize the research problems 5
- 04 They can apply proper t tests for comparing two population means based on given research problems in SPSS. 4
- 05 They can appraise and apply the variance analysis for comparing group means more than two in SPSS 4
Course Syllabus
| Week | Topic |
|---|---|
| Week 1 | Introduction to the course; basic concepts and terms related to descriptive and inferential statistics. |
| Week 2 | Entering data collected to the computer, organizing and getting data ready for statistical analyses |
| Week 3 | normal distribution and sampling distribution of the mean; establishing confidence intervals |
| Week 4 | Graphs in SPSS |
| Week 5 | Descriptive statistics: Measures of central tendency and position measurements (arithmetic mean, median, mode, percentile and quartile) |
| Week 6 | Descriptive statistics: measures of variability (range, variance, standard deviation); display of data. |
| Week 7 | Standard error and confidence interval |
| Week 8 | writing research questions, research hypotheses, null hypotheses; hypothesis testing. |
| Week 9 | Inferential statistics: t-test applications (one-sample t-test, independent samples t-test, dependent samples t-test) |
| Week 10 | Inferential statistics: t-test applications (one-sample t-test, independent samples t-test, dependent samples t-test) |
| Week 11 | Midterm Exams |
| Week 12 | Inferential statistics: t-test applications (one-sample t-test) |
| Week 13 | Analysis of variance applications (one-way ANOVA) |
| Week 14 | Analysis of variance applications |
| Week 15 | Final Exams |
Reference Books & Course Materials
- 01 Green, S. B., Salkind, N. J. (2014). Using SPSS For Windows And Macintosh -7TH Edition. Boston: Pearson.
- 02 Elzey, F. F. (1985). Elementary Statistical Techniques. California: Brooks/Cole.
- 03 Coursepack prepared by the instructor.
Learning Outcomes
No learning outcomes have been defined.
Program Outcomes
- PCST PÇ1: Develop and deepen knowledge acquired in plant science and technologies based on undergraduate level competencies.
- PCST PÇ2: Understand the interaction between plant science and technologies field and related disciplines.
- PCST PÇ3: Combine and interpret the theoretical and practical knowledge related to horticultural plants with the knowledge, data and findings obtained from different disciplines, to be able to create new knowledge and theories by synthesizing them by using the theoretical and practical knowledge related to horticultural plants in the field of expertise and supporting the current developments with quantitative and qualitative.
- PCST PÇ4: Solve problems by using research methods and solving cause and effect relationships.
- PCST PÇ5: Conduct a study requiring expertise on plant science and technologies independently.
- PCST PÇ6: Develop an analytical approach for solving unpredictable complex problems encountered in applications of plant science and technologies, to design the research process, to produce solutions by taking responsibility and to evaluate and defend the results obtained.
- PCST PÇ7: Lead the plant science and technologies in an environment that requires solving problems.
- PCST PÇ8: Access to resources related to plat science and technologies, benefiting from these resources and self-renewal.
- PCST PÇ9: Transfer his / her studies, developments in his / her field of expertise and research results using oral, written and visual tools.
- PCST PÇ10: Use advanced computer software and information and communication technologies as necessary in relation to plant science and technologies.
- PCST PÇ11: Collect data with scientific methods for solving problems related to plant science and technologies, to be able to control and interpret the collected data by taking into consideration the social, scientific and ethical values.
- PCST PÇ12: Utilize and explain integrated knowledge in plant science and technologies in interdisciplinary studies, communicate it effectively, and critically evaluate the conclusions drawn.
Po-Lo Matrix
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