Institute: Vállalkozásmenedzsment Intézet (1084 Budapest, Tavaszmezõ u. 15-17.) Credit: 3
Type of classes: Nappali Language: English Semester: 2018/19/2
Level: Technical Management BSc;
Responsible Teacher: Dr. Nagy Viktor Teacher(s): Dr. Nagy Viktor;
Pre requirements: Statistics I.
Consultations (total/week): Heti Lectures: 1 Practice lectures: 2 Labs: 0 Consultations: 0
Type of Exam: félévközi jegy
Aim of the subject: Upon completion of this course, students should use the tools and formulas of inferential statistics.
Requirements during the semester
(homeworks, essays,
excercises, teamworks,
part and final
oral/written exam etc.):
Students are required to attend all classes. Should a student accumulate 5 absences (excused and/or unexcused) out of 14 in the semester class,
he/she will not receive academic credits. Students are required to pass two tests. Students may get homework, which should be handed in until the
next lesson or presented in some minutes in the lectures.
Week of
Topics of lectures/practices
1. Sampling: simple random, systematic, cluster, stratified and other sampling.
2. Representativeness. Biased sample, sampling errors. Sampling distributions. Central limit theorem. Standard errors.
3. Point estimate, interval estimate, confidence level, confidence interval, margin of error. Confidence intervals for the mean, proportion and variance.
4. Binomial, normal, t- and chi-square, F distributions.
5. Hypothesis testing I.
With one sample: for the mean, proportion and standard deviation
With two samples: for differences between means and proportions.
6. Hypothesis testing II.
Chi-square tests: goodness-of-fit and test for independence. ANOVA.
7. Test 1
8. Covariance, correlation coefficients, correlation quotient, rank correlation.
9. Bivariate linear correlation and linear/nonlinear regression analysis.
10. Multivariate regression analysis.
11. Time series analysis: moving averages. Time series models: trend, seasonality, cyclic behaviour, randomness.
12. Interpolation and extrapolation.
13. Test 2
14. Makeup exams
Type of evaluation,
repetition, calculation
of grade, etc.
Grade in this course is calculated numerically based on total points/percentages of the two tests although the instructor may raise or decrease it by one grade based on the active/inactive participation in classes or the level of the homework.
5 (excellent): 87 – 100 %
4 (good): 75 – 86 %
3 (satisfactory): 63 – 74 %
2 (pass): 51 – 62 %
1 (fail): 50 or less %
Should a student accumulate a total of 50 or less percentages, an additional chance is given to him/her to meet the requirements.
Type of evaluation,
repetition, calculation
of grade, etc.
in written
Compulsory literature: Freedman, David – Pisani, Robert –Purves, Roger (2007): Statistics: W.W.Norton & Company Inc, (4th Edition)
Suggested literature: Oakshott, Les (2016): Essential Quantitative Methods: For Business, Management and Finance. 6th Edition, Palgrave
Oakshott, Les (2014): Quantitative methods. Palgrave Macmillan
Donnelly, Robert (2007): The Complete Idiot`s Guide to Statistics. 2nd Edition, Alpha
Swift, Louise and Piff, Sally (2014): Quantitative Methods for Business, Management and Finance, Macmillan Education UK
Rumsey, Deborah J. (2011): Statistics For Dummies. 2nd Edition, Wiley
Rumsey, Deborah J. (2009): Statistics II for Dummies. Wiley, 2009