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Statistics I

Course outline

 

General Information:

 

School: Economics & Social Sciences

Department: Business Administration

Level of Studies: Undergraduate

Course code: 203

Semester: B

Course title: Statistics I

ECTS: 5

Weekly teaching hours: 4

Type of course: mandatory/general background

Prerequisite courses: No

Language of instruction and exams: Greek

The course is offered to Erasmus students: No

Course URL: https://eclass.uth.gr/courses/SE_BA_U173/

Learning outcomes:

-     Understanding instructions regarding the application of statistical methods, as well as concepts and procedures using a variety of exercises and examples.

-     Description of data and calculation of indicators that will briefly describe complex phenomena.

-     Drawing generalized conclusions about populations using samples from those populations.

-     Developing Statistical

-     Thinking to Make Better Business Decisions.

-     Collection and analysis of business data with the aim of drawing correct conclusions that lead to improved decisions and consequently to the more efficient operation of a process of a department or even the entire company.

-     Gain fluency in data management through spreadsheets and statistical software for decision making.

-     Presentation of statistical topics in the context of fields such as accounting, finance, management and marketing and explanation and application of the specific methods in business activities.

-     Emphasis on interpretation and analysis of statistical results beyond mathematical calculations.

-     Hands-on practice in understanding the applications of statistics to business problems.

-     Use of business statistics scripts.

General Skills:

-     Search, analysis and synthesis of data and information, using the necessary technologies

-     Adaptation to new situations

-     Decision making

-     Autonomous work

-     Teamwork

-     Work in an international environment

-     Work in an interdisciplinary environment

-     Generating new research ideas

-     Project planning and management

-     Respect for diversity and multiculturalism

-     Respect for the natural environment

-     Demonstration of social, professional and ethical responsibility and sensitivity to gender issues

-     Development of criticism and self-criticism

-     Promotion of free, creative and inductive thinking

-     Other skills

Moreover, the course aims to develop the following general skills:

-     Critical ability and self-criticism

-     Ability to cooperate

-     Interpersonal skills

-     Search data using the necessary technologies

Course Content:

-     Descriptive statistics (parameters of central tendency and location, variability, clustered data),

-     Probabilities (individual, compound, conditional),

-     Total probability and Bayes theorems,

-     Discrete and continuous random variables (Binocular, Poisson, Normal, etc.),

-     Sampling and sampling distributions (central limit theorem, student-t distributions, χ2, F),

-     Confidence intervals and hypothesis tests of population parameters (mean, percentage, variance) for a population.

-     Comparing two populations, the method of analysis of variance, simple linear regression,

-     Introduction to Multiple Regression.

Teaching & Learning Methods - Evaluation

-     Method of delivery: face-to-face

-     Use of information and communication technologies: Use of information and communication technologies in teaching, laboratory education, communication with students. Electronic communication with students, learning-process support through the “e-class” online platform.

Activities & Semester Workload

Lectures: 13 weeks * 3 hours = 39 hours.

Home Study: Continuous study of teaching notes and suggested bibliography is required (estimated 4 hours study requirement for each teaching unit), 13 weeks * 4 hours = 52 hours.

Submission of research paper - Problem Solving: After completing each module in theory, individual tasks are given to solve specific problems that facilitate a better understanding of the course (13 weeks * 2 hours = 26 hours).

Use of Statistical Software: Emphasis will be placed on interpreting the calculations of the above problems using statistical software (13 weeks * 2 hours = 26 hours).

 

Final Exams: Written exam (3 hours).

Total: 146

 

Evaluation of students:

The Final grade of the course results from 70% of the final written exams, while the remaining 30% results from the individual assignments.

Recommended bibliography:

Greek

Levine David, Szabat Kathryn, Stephan David, Στατιστική: Βασικές Αρχές με Έμφαση στην Οικονομία και τις Επιχειρήσεις, Εκδόσεις: BrokenHill, 2018.

Φιλιππάκης Μ., Στατιστικές Μέθοδοι και Ανάλυση Παλινδρόμησης για τις νέες τεχνολογίες, Εκδόσεις: Τσότρας, 2016.

Douglas Downing, Jeffrey Clark, Στατιστική των Επιχειρήσεων, Εκδόσεις: Κλειδάριθμος, 2010.

Ζαφειρόπουλος Κώστας, Εισαγωγή στη στατιστική και τις πιθανότητες, Εκδόσεις: Κριτική, 2017.

Spiegel Murray R., Stephens Larry J., Στατιστική, Εκδόσεις: Τζιόλα, 2016.

Berenson M. L., Levine D. M., Szabat K. A., Βασικές Αρχές Στατιστικής για Επιχειρήσεις: Έννοιες & Εφαρμογές, Εκδόσεις: BrokenHill, 2018.

 

English:

Hogg,R.V. and Tanis,E.A., Probability and Statistical Inference, Prentice Hall, 2000.

Aczel, A. D. and Sounderpandian, J., Complete Business Statistics, McGraw – Hill & Irwin, 2002.

Doane, D., Seward, L., Applied Statistics in Business & Economics, McGraw-Hill, 2013.

Jaggia, S., Kelly, A., Business Statistics: Communicating with numbers, McGraw-Hill, 2013.

Lind, D., Marshal, W., Wathen, S., Basic Statistics for Business and Economics, McGraw-Hill, 2013.