[GVMAD1KTNC] Analyzing Databases with SPSS
|Institute:||Vállalkozásmenedzsment Intézet (1084 Budapest, Tavaszmezõ u. 15-17.)||Credit:||3|
|Type of classes:||Nappali||Language:||English||Semester:||2012/13/2|
|Level:||Gazdálkodási és menedzsment BSc alapszak; Kereskedelmi és marketing BSc alapszak; Műszaki menedzser BSc alapszak;|
|Responsible Teacher:||Dr. habil. Szeghegyi Ágnes||Teacher(s):||Lénárt Imre;|
|Pre requirements:||Statistics I., Statistics II., Analyzing Economic Models with MS Excel|
|Consultations (total/week):||Heti||Lectures:||0||Practice lectures:||2||Labs:||0||Consultations:||0|
|Type of Exam:||félévközi jegy|
|Aim of the subject:||As the students finished their studies in the field of statistics, this course deals with the practical application of them, furthermore they will learn the up-to-date methods of the different field of the science of statistics. As the lessons about the application of statistical methods finished the last main topic is the multivariate models, as a new field of analyzing large databases as marketing research and opinion polls.|
|Requirements during the semester
part and final
oral/written exam etc.):
Students are required to pass two tests. The first one is based on the usage of statistical methods, and SPSS-skills (50 points). As a second test students are given a specified problem to be solved using SPSS (50 points).
Students are required to attend all classes. Should a student accumulate 5 classes out of 14 during a semester, he/she may not receive academic credits.
|Topics of lectures/practices|
|1.||Introduction to SPSS|
|4.||Formatting, splitting, filters, variable transformation and computation|
|5.||Statistical inference, estimation, hypothesis testing|
|8.||Stochastic relations – association, correlation, multivariate correlation|
|9.||Regression analysis – Two variable regression|
|10.||Regression analysis – Multiple regression|
|11.||Multivariate models – Factor Analysis, Principal Component Analysis|
|12.||Multivariate models – Cluster analysis|
|13.||Multivariate models – Discriminant Analysis|
|Type of evaluation,
of grade, etc.
Grades in this course are calculated numerically based on total points of the two tests although the instructor may raise them by one grade based on the active participation in classes.
5 (excellent): 88 – 100 points
4 (good): 75 – 87 points
3 (satisfactory): 63 – 74 points
2 (pass): 51 – 62 points
1 (fail): 50 or less points
Should a student accumulate 50 or less points, an additional chance per test is given to him/her to meet the requirements.
|Compulsory literature:||This course does not require any textbooks. Students are provided with access to relevant articles and online materials as required.|
|Suggested literature:||Field, Andy (2009): Discovering Statistics Using SPSS (Introducing Statistical Method), Sage Publications Ltd Pallant, Julie (2010): SPSS Survival Manual: A step by step guide to data analysis using SPSS, Open University Press Pelsmacker, Patrick De – Kenhove Patrick Van – Janssens, Wim – Katrien Wijnen (2008): Marketing Research with SPSS, Financial Times/ Prentice Hall|