| Modulname |
Business statistics 1 (UNIC - University of Zagreb) |
| Gebiet |
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| Profil |
Profil International
Profil Freie Studien
|
| CPs |
5 CP |
| Campus |
Hier geht
es zum Vorlesungsverzeichnis |
| Voraussetzungen |
The module is held in English. A good knowledge of English is therefore a prerequisite for participation in the module. |
| Besonderheiten |
TN-Plätze: 15/30 Termin der ersten Sitzung: September 28, 9 am; Zoom Anmeldung: This should be defined but the Instititions leading the project; once the students decides to enroll in the course, I should be informed by email pposedel@agr.hr by September 15, up to two weeks prior the course. After successful registration, please send an email with registration confirmation to ecampus-optionalbereich@rub.de. This is necessary so that registration in Campus can take place after successful completion of the module. Zusammensetzung der Endnote: First Midterm 45% Second Midterm 40% 2 Quizzes and 2 homeworks 10% Activity 5% Prüfungstermin: Two terms in the period of January 30 – February 24 2022. |
| Blockseminar |
Nein |
| Vorkenntnisse |
The module is held in English. A good knowledge of English is therefore a prerequisite for participation in the module. |
| Veranstaltungszeit |
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| Dozenten |
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| Arbeitsaufwand |
90 h personal time and 60 h attendance: Preparation and wrap-up of sessions, active participation in seminar sessions, small homework assignments, completion of quizzes |
| Literatur |
P. Newbold, W. L. Carlson, B. Thorne: Statistics for Business and Economics, Global Edition, 9th Edition, Pearson 2019; P.S. Mann, Statistics for Bussines and Economics, J. Wiley, N.Y., 1995. M. Silver: Business Statistics, Mc Graw-Hill, 1997. |
| Modulteil |
[430024] Business statistics (second cycle) (UNIC - University of Zagreb) - WS 22/23, [430012] Business statistics (first cycle) (UNIC - University of Zagreb) - WS 22/23 |
| Modultyp |
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| Modulanbieter |
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| Inhalt |
Teil 1: Business statistics (first cycle), WiSe 2022/23, from October 3rd to November 18th and intermediate exams from November 14th to November 18th , via Zoom Teil 2: Business statistics (second cycle), from November 21st to January 20 and a final exam from January 23 to February 3rd , via Zoom The course covers the basics of descriptive and inferential statistics. The descriptive statistics' part includes content on data types, their organization, presentation and interpretation, measurement scales and numerical indicators. Special attention is devoted to the development of statistical literacy in the interpretation of statistical indicators. The basics of probability theory include necessary elements for understanding more complex concepts. The concepts of discrete and continuous random variables and their distributions are introduced and described: binomial, geometric, hypergeometric, Poisson, normal, uniform, exponential, and Student t-distribution. The rest of the course is devoted to inferential statistics - interval estimates of expectations and probabilities and testing hypotheses about population mean and proportion. The content topics are specified in detail as follows: In the descriptive part, the following concepts are covered: 1. Population and sample; 2. Qualitative and quantitative variables; 3. Measurement scales, graphical presentation of qualitative and quantitative data; various types of diagrams and their interpretation; histogram 4. Mean, median and mode; variance and standard deviation. coefficient of variation; quartiles, centiles; 5. Data entry in Excel, structure of the workspace, statistical functions in Excel; Application of Excel in determining numerical descriptive measures and graphical presentation of data; Basics of probability theory include: 1. Random experiment, outcome and event space; Independent and dependent events; 2. Conditional probability; Calculating conditional probabilities, Bayes formula; 3. Probability distribution of a discrete random variable; expectation and standard deviation; 5. Discrete probability distributions: Binomial random variable and binomial formula; Binomial distribution, geometric distribution, Poisson and hypergeometric distribution; examples. 6. Continuous probability distributions; Normal distribution. Examples and applications. Computing probabilities with standard normal distribution tables; Uniform distribution. In the inferential part, the following concepts are covered: 1. Random and non-random samples; selection of a random sample; Sample statistics. 2. Point and interval estimates. Confidence intervals; Estimates for Large and Small Samples. t-distribution. Interval probability estimates; 3. Hypothesis testing. Examples. Types of errors. Power of the test; Expectation hypothesis testing for large and small samples. Probability hypothesis testing. testing hypotheses about population mean and proportion. |
| Lernziele |
After successfully completing the course, students should be able to: 1. classify measurement scales and data types 2. organize data and show them graphically (bar and pie chart, histogram) 3. calculate the basic numerical measures of given data 4. apply Excel tools for descriptive statistics 5. determine the probabilities of events and use statistical tables 6. differentiate between distributions of discrete random variables and continuous random variables 7. identify and distinguish between different types of probability distributions 8. comment on the appearance of the normal distribution density function graph depending on the size of the standard deviation 9. construct confidence intervals for population mean and proportions 10. formulate a hypothesis, test it and make a conclusion about its truthfulness based on a statistical test |
