Statistics

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Learning Outcomes

This course attempts to enhance the knowledge gained in the course Statistics and to provide an appropriate mathematical background that enables the students to study the fields of computers and computers networks. After successfully completing this course, students are expected to have acquired the basic knowledge of the fundamental concepts Statistics. The course directly supports most of the curriculum subjects and lessons: Note that during the lesson, specific examples of application are discussed in some of the above curricular subjects such as applications in telecommunication systems, cryptography, digital services such as e- learning, e-health. In conclusion, the students through the course process develop mathematical thinking and analyze, adapt their acquired knowledge to apply them to a variety of subjects in the field of study or the field of study, as well as to acquire new knowledge. In addition, they learn to solve complex or new problems in their scientific field of study by developing integrated as well as creative or innovative solutions and approaches while supporting their solutions and views in a methodical and scientific way. Finally, they learn to analyze and critically and responsibly choose ideas and information about those elements that concern them.

Course Contents

  • Sample theory, samples, replacement, random samples and numbers.
  • Sampling distributions and statistical interrelations.
  • Frequency and relative frequency.
  • Cluster sampling, stratified sampling, systematic sampling, statistical estimations unbiased estimations.
  • Reliability and confidence intervals.
  • Hypotheses testing and importance of statistics hypotheses, significance level, normal distribution testing.
  • Interpretation of criterion t for dependent and independent samples.
  • Adaptation test x2.
  • Adaptation curve, regression and cross-correlation adaptation curve, regression, least square.
  • Multiple regression, estimation fault, factors of cross-correlation.
  • Correlation and independence.
  • Interpretation of indicators of cross-correlation, Pearson, Spearman, Biserial, φ.
  • Propagation analysis, interpretation of propagation analysis.
  • Multiple-variables statistical analysis.
  • • M. Filippakis, Statistical Methods & Regression Analysis for New Technologies, Tsotras Publications, Athens 2017, 1rst Edition.
  • • T.Papaioannoy-S.Loukas, Introduction to Statistics, Stamoulis Publications, Athens (2002).
  • • Teaching Notes.