You have the option to take the course free of charge. If you wish to receive a Certificate of Continuing Education from the Center for Continuing Education and Lifelong Learning of the Hellenic Open University (HOU), you may submit the corresponding application by selecting: Obtaining a Certificate.
Applications may be submitted until the end of the course, that is, until March 27, 2023.
This course covers the basic principles of statistics. Emphasis is placed on both descriptive and inferential statistics. The various types of variables we may encounter are presented, along with the corresponding ways in which we can describe them. Particular emphasis is placed on investigating relationships between variables and on basic methods of statistical analysis, such as:
• confidence intervals
• hypothesis tests (chi-square test of independence, t-test, etc.)
• analysis of variance and regression analysis
Particular emphasis is placed on the use of SPSS software, on common errors that frequently occur in statistical analyses, and on the concept of uncertainty.
There are no
Teacher
Dimitris Mavridis
Associate Professor of Statistics, Department of Elementary Education, University of Ioannina.
Dimitris Mavridis is an Associate Professor of Statistics in the Department of Elementary Education at the University of Ioannina. He holds a Ph.D. in Statistics from the Athens University of Economics and Business and worked as apostdoctoral researcher at the School of Mathematics at the University of Edinburgh. His research interests focus on statistical methods for data synthesis, particularly meta-analysis and network meta-analysis. He is interested in statistical methods for addressing publication bias, the problem of missing data, and other aspects of network meta-analysis. He is a statistical editor for *Evidence Based Mental Health* (EBMH) and *Research Synthesis Methods*. At *EBMH*, he is responsible for the “Statistics in Practice” series, which publishes instructional articles on data synthesis. He is also the scientific coordinator for work packages in two HORIZON2020 projects (OPERAM and COMPAR-EU), where he coordinates several network meta-analyses. He teaches in the Comparative Effectiveness Research master’s program at the University of Paris. He has published more than 40 meta-analyses in high-impact journals such as The Lancet, JAMA, BMJ, the *European Heart Journal*, *Stroke*, and numerous methodological articles related to the development of statistical meta-analysis models.
More information about the course instructor: here.
Georgios Seitidis
Ph.D. candidate at the University of Ioannina, M.Sc. in Statistics (CV)
Katerina-Maria Kontouli
Ph.D. Candidate at the University of Ioannina, MSc in Research Methodology in Biomedicine, Biostatistics, and Clinical Bioinformatics (CV)
Ourania Koutsiorouba
Research Associate, Master of Science in Biostatistics (CV)
Stavros Nikolakopoulos
Postdoctoral Researcher at the University of Ioannina, PhD, Department of Biostatistics (CV)
Sofia Tsokani
Ph.D. candidate at the University of Ioannina, M.Sc. in Statistics and Operations Research (CV)
Christos Christogiannis
Ph.D. candidate at the University of Ioannina, M.Sc. in Statistics and Operational Research (CV)
Week 1: Introductory Course
• The Usefulness of Statistics
• The concept of uncertainty
• Examples of the Misuse of Statistics
• Observational studies and randomized trials
Week 2: Combinatorics – Probability
• Multiplicative principle
• Transfers – Assignments – Combinations
• Basic concepts of probability – Venn diagrams
• Axiomatic foundation of probability
• Committed probability
Week 3: Descriptive and Inferential Statistics
• Descriptive Statistics – Types of Variables
• Descriptive statistics and graphs for qualitative variables
• Descriptive statistics and graphs for quantitative variables
• Inferential Statistics – Sample and Population
• Inferential Statistics – Normal Distribution
• Inferential Statistics – Confidence Intervals
Week 4: Uncertainty and Hypothesis Testing
• Null hypothesis and possible errors
• Hypothesis tests for continuous data
• Interpretation of the p-value
• Normality test
• T-test
• Normality test
• Nonparametric tests (Mann-Whitney test)
• The chi-square test of independence
• Tests: Fischer, McNemar, and Mantel-Haenszel
• Exercise for testing hypotheses with dichotomous data
Week 5: Analysis of Variance and Regression Analysis
• Analysis of variance
• Pearson's correlation coefficient
• Regression analysis
• Multiple linear regression
• Testing the hypotheses of simple and multiple regression
Week 6: Descriptive Statistics and Hypothesis Testing Using SPSS
• Data entry and variable handling
• Presentation of Variables – Charts
• Chi-square test
• Normality Test – Measures of Central Tendency and Dispersion
• T-test
• ANOVA analysis of variance test
• Pearson correlation test
• Graphs
• Analysis of variance and regression analysis
You can find more information in the program's course catalog.
See the study guide here.
This course is intended for:
• for researchers who want to conduct their own research
• anyone who wants to be able to read and understand the pros and cons of a published study
• anyone who wishes to take a critical look at the statistical analyses and figures presented in their daily life, e.g., by the media
Upon completion of the course, participants are expected to be able to understand
✔ the concept of uncertainty
✔ the statistical analyses that can be performed on the various types of variables
✔ the statistical analyses required to investigate relationships between variables
✔ scientific research based on statistical findings
To obtain a certificate, you must
1. Successful completion of the course work
Specifically:
✔ Trainees are assessed through exercises and multiple-choice questions (quizzes) based on the content of the video lectures. To successfully complete the course, the final grade in each of the following exercise categories must be greater than or equal to 50%. Specifically, the following scores are required:
• greater than or equal to 50% on the Module Quizzes
• greater than or equal to 50% in the Weekly Quizzes
2. Payment of the applicable fee
Check the table at the top of this page for information on the participation fee.
Contact
• Email: info@coursity.gr
- Course Code: StatAnal
- Course Start Date: February 13, 2023
- 72 training hours (2.88 ECTS)
- Course Fee: Free
- Certificate Download: €60
