﻿ Business Statistics I / BS Courses taught in English / Undergraduate studies / Faculty of Agriculture

 ECTS 6.00 English language R1 E-learning R1 Teaching hours 60 Lectures 44 Practicum 12 Seminar 4 Lecturer Assist. Prof. Biserka Kolarec, PhD Associate teacher for exercises Assist. Prof. Biserka Kolarec, PhD Grading Sufficient (2) 60-69% Good (3) 70-79 % Very good (4) 80-89 % Excellent (5) 90 -100%

## Course coordinator

Assist. Prof. Biserka Kolarec, PhD

## Course description

This module presents the basics of descriptive and inferential statistics in the context of agricultural economics. The part concerned with descriptive statistics pays special attention to organization, presentation, and interpretation of different types of data. The intention here is to develop an ability to critically assess and interpret statistical data and to avoid common pitfalls. A short review of basic concepts of probability is a bridge to the part devoted to the inferential statistics. This part starts by an introduction to discrete and continuous random variables and the most important distributions, followed by the classical topics of estimations and hypotheses testing about the mean and proportion.

## General competences

- raising the level of statistical literacy
- acquiring knowledge and skills necessary to understand, analyze and solve problems arising in the course of practical work
- developing an ability to critically assess and interpret statistical data and to avoid common pitfalls
- using statistical software with confidence

## Types of instruction

• Assessments
• Consultations
• Lectures
individual work on concrete problems in order to acquire the level of statistical literacy necessary for understand, analyze and solve practical problems arising in the course of work in agricultural economics.
• Practicum
on computers
• Seminars
solving an individual problem

## Learning outcomes

 Learning outcome Evaluation methods organize data and present them grafically individual and practical work, project calculate numerical descriptive measures of data homework, exam, practical work apply Excel tools for descriptive statistics exam, practical work, project distinguish between discrete and continuous random variables and their probability distributions homework, practical work, project determinate probabilities and use statistical tables homework, practical work, project, exam construct confidence intervals for means and proportions homework, practical work, project set up a hypothesis and test it homework, practical work, project, exam be able to use mathematical software and interpret obtained results project work

## Working methods

### Teachers' obligations

1. Course planning
2. Selection and creation of teaching materials
3. Evaluation of course, teaching materials and curriculum
4. Construct tests
5. Grade students on the basis of their achievement

### Students' obligations

1. Attend lectures regularly
2. Do homeworks and participate actively during lectures
3. Write tests and win at least 25% of points on each test to get the signature
4. Do individual projects

Evaluation elements Maximum points or Share in evaluation Grade rating scale Grade Direct teaching hours Total number of average student workload ECTS
1st exam 40 % 60-69 %
70-79 %
80-89 %
90-100 %
Sufficient (2)
Good (3)
Very good (4)
Excellent (5)
30 90 2
2nd exam 30 % 60-69 %
70-79 %
80-89 %
90-100 %
Sufficient (2)
Good (3)
Very good (4)
Excellent (5)
15 45 2
3rd exam 30 % 60-69 %
70-79 %
80-89 %
90-100 %
Sufficient (2)
Good (3)
Very good (4)
Excellent (5)
15 45 2
Total 100 % 60 180 6
3rd exam interval estimations and hypothesis testing 16.th week

## Weekly class schedule

1. The purpose of statistics. Descriptive and inferential statistics. Basic concepts. Types of variables. Scales of measurement.
2. Organizing and graphing of qualitative and quantitative data. Interpretation of different types of diagrams. Recognizing and avoiding common pitfalls.
3. Measures of central tendency – mean, median and mode. Measures of dispersion. Measures of position.
4. Index theory Measures of association Basic definitions and examples from the economic theory Types of measures of association
5. Elements of probability I Experiment, outcomes and sample space. Three conceptual approaches to probability. Examples.
6. Elements of probability II Dependent versus dependent events. Conditional probability. Bayes&#39; theorem.
7. Discrete random variables and their probability distributions I Probability distribution of a discrete random variable. Mean and standard deviation. The binomial probability distribution.
8. Discrete random variables and their probability distributions II The Poisson probability distribution. The hypergeometric probability distribution.
9. Continuous random variables and their probability distributions I Continuous probability distribution. The normal distibution. The standard normal distribution. Applications.
10. Continuous random variables and their probability distributions II The normal approximation to the binomial distribution.
11. Populations and samples Random and nonrandom samples. Selecting a simple random sample. Sampling errors.
12. Estimation of the mean Point and interval estimates. Interval estimation of a population mean for large and small samples. The t probability distribution
13. Estimation of the proportion Interval estimates of a population proportion. Sample size determination.
14. Hypothesis tests about the mean Hypothesis tests. Rejection and non-rejection regions. Two types of errors. Hypothesis tests about a population mean for large and small samples.
15. Hypothesis tests about the proportion Hypothesis tests about a population proportion.

## Obligatory literature

1. P.S. Mann, Statistics for Business and Economics, J. Wiley, N. Y., 2005.
2. M. Silver: Business Statistics, Mc. Graw Hill, London, 1997.

## Recommended literature

1. L. Kazmier, Schaum&#39;s Easy Outline of Business Statistics, McGraw-Hill, N. Y., 2003.
2. D. Huff, How to lie with statistics, WW Norton, N. Y., 1993.

## Similar course at related universities

• Matematik und Statistik, BOKU
• Statistik, University of Hohenheim