Foundation of Quantitative Analysis
(1) General
| School: | Of the Environment | ||
| Academic Unit: | Department of Marine Sciences | ||
| Level of studies: | Postgraduate | ||
| Course Code: | Semester: | Α | |
| Course Title: | Foundation of Quantitative Analysis | ||
| Independent Teaching Activities | Weekly Teaching Hours | Credits | |
| Lectures | 3 | ||
| Lab/Tutorials | 2 | ||
| Total credits | 10 | ||
| Course Type: | General background | ||
| Prerequisite Courses: | No | ||
| Language of Instruction and Examinations: | English | ||
| Is the course offered to Erasmus students: | Yes | ||
| Course Website (Url): | https://www.mar.aegean.gr/index.php?lang=en&lesson=101&pg=3.2.2 | ||
(2) Learning Outcomes
Learning Outcomes
Upon successful completion of this course the student should be able to:
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Demonstrate the ability to apply fundamental concepts to exploratory data analysis.
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Plan data acquisition studies, avoiding common design flaws that cause bias, inefficiency, and the influence of uncontrolled factors.
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Understanding the basic concepts of probability and random variables.
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Understanding the concept of sampling statistics and especially the description of the behavior of the sample mean.
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Understanding the basics of Statistical Inference including confidence intervals and hypothesis testing.
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Application of Inference methods related to the means of normal distributions.
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Application and interpretation of basic summary and two-variable data modeling techniques and use of Inference methods in the context of simple linear models with normally distributed errors.
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Understanding one-factor analysis of variance
General Competences
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Search for, analysis and synthesis of data and information, with the use of the necessary technology
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Decision-making
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Working independently
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Team work
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Working in an international environment
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Working in an interdisciplinary environment
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Production of new research ideas
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Respect for the natural environment
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Criticism and self-criticism
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Production of free, creative and inductive thinking
(3) Syllabus
Descriptive statistics methods
Statistical distributions-Central limit theorem
Hypotheis testing
One-sample t-test, Independent and Paired two-sample t-tests
Non-parametric methods for two independent and paired samples
One-way ANOVA and Kruskal-Wallis test, Post-hoc tests
Simple and Multiple Linear Regression – General Linear Models
Parametric and non-parametric correlation
Introduction to Generalized Linear Models
Applications in coastal management with R and RStudio
(4) Teaching and Learning Methods - Evaluation
| Delivery: | Face to face, Asynchronous e-learning | |||||||||||||||||||||
| Use of Information and Communication Technology: | Students practice with R and RStudio. Communication with students via e-mail and eclass platform. Uploading course material on eclass system. | |||||||||||||||||||||
| Teaching Methods: |
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| Student Performance Evaluation: | Language of evaluation: English Method of evaluation: Final written exam: problems solving using R (20%) Final written exam: problems solving (80%) |
(5) Attached Bibliography
- Suggested bibliography:
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Verzani J. 2014. Using R for Introductory Statistics. Taylor & Francis
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Crawley MJ. 2012. The R Book. Wiley
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Diez DM, Barr CD, Cetinkaya-Rundel M. 2012. OpenIntro Statistics. http://www.openintro.org/stat/
- Related academic journals:
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Environmental and Ecological Statistics