Universitat Internacional de Catalunya
Business Statistical Inference
Other languages of instruction: English, Spanish
If the student is enrolled for the English track then classes for that subject will be taught in the same language.
Teaching staff
By appointment
Introduction
A course in statistics is offered in a wide variety of disciplines, from the social sciences to business to the natural sciences. The same statistical methods are applied across disciplines. Therefore, it should not be surprising that the tools you will learn to use in this course will benefit you in your future studies and careers regardless of whether your career interest is finance, accounting, strategy, management or marketing. In this course you will learn three important statistical procedures: (i) estimation, (ii) hypothesis testing and (iii) time series and forecasting
I believe statistics is best taught through a series of clear and carefully worked examples. A theoretical background to descriptive and inferential statistical methods will be provided, however a great deal of time will be spent teaching you how to apply the theory to the real world. Statistics is not about memorising formulas, rather it is about recognising the appropriate statistical test to conduct in a given situation. This requires practice by students. As we cover the topics, if you do not have a clear understanding of one topic it is wise to seek help immediately. The next topic will build upon the previous one. Please contact me for help if you have any questions.
Pre-course requirements
It is recommended that students have taken and understood the courses Statistics 1, Mathematics 1 and Mathematics 2”.
Objectives
- To develop a basic understanding of the different probability distributions
- To learn the basics on how to conduct experiments and test hypotheses
- To apply statistical terminology correctly
Competences/Learning outcomes of the degree programme
- 19 - To analyse quantitative financial variables and take them into account when making decisions.
- 28 - To be able to work in another language and use terminology and structures related to the economic-business world.
- 31 - To develop the ability to identify and interpret numerical data.
- 32 - To acquire problem solving skills based on quantitative and qualitative information.
- 35 - To analyse time series.
- 36 - To interpret quantitative and qualitative data and apply mathematical and statistical tools to business processes.
- 40 - To be able to choose statistical methods appropriate to the object of analysis.
- 42 - To be able to empirically analyse financial phenomena.
- 43 - To acquire skills for using statistical software.
- 50 - To acquire the ability to relate concepts, analyse and synthesise.
- 51 - To develop decision making skills.
- 52 - To develop interpersonal skills and the ability to work as part of a team.
- 53 - To acquire the skills necessary to learn autonomously.
- 54 - To be able to express one’s ideas and formulate arguments in a logical and coherent way, both verbally and in writing.
- 56 - To be able to create arguments which are conducive to critical and self-critical thinking.
- 64 - To be able to plan and organise one's work.
- 65 - To acquire the ability to put knowledge into practice.
- 66 - To be able to retrieve and manage information.
- 67 - To be able to express oneself in other languages.
Learning outcomes of the subject
Understand quantitative research terminology, notation and methods, specifically those related to inference.
Ability to analyse and summarise information from lectures and materials provided by the lecturer.
Choose the appropriate statistical method to solve any economic problem.
Syllabus
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Content |
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Course introduction Theme 9: Discrete Probability Distribution - Review of important concepts - Binomial probability distribution - Hypergeometric probability distribution - Poisson probability distribution - Examples and exercises
Theme 10a: Continuous Probability Distribution – Uniform Distribution - Review of important concepts - Probability distribution function of Uniform Distribution - Mean and variance of Uniform Distribution - Examples and exercises |
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Theme 10b: Exponential Distribution - Probability density function of the exponential distribution - Mean and variance of the exponential distribution - Exercises |
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Theme 10c: Continuous Probability Distribution – Normal Distribution - Definition and characteristics - Standard normal distribution - Use of tables - Standardisation |
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Theme 10c: Continuous Probability Distribution – Normal Distribution - Group presentations and exercises (10% of the final mark) |
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Theme 10c: Continuous Probability Distribution – Normal Distribution - The coin toss exercise - Normal distribution and its relationship with the binomial and the Poisson |
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Computer room. Theme 10c: Comparison of Normal Distribution with the Poisson and the Binomial. - Exercise |
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Mid-course Examination (20% of the final mark) |
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Theme 11: Sampling - Sampling methods - Central limit theorem - Exercises |
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Computer room. Theme 11: Sampling - Practice on sampling methods |
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Theme 12: Estimation I - Confidence intervals for the mean when σ is known - Exercises |
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Theme 12: Estimation II - Confidence interval for the mean when σ is unknown - Student-t distribution - Use of t-distribution tables - Exercises |
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Theme 12: Practical Exercise - The M&M project Theme 12: Estimation III - Confidence interval for a proportion |
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Theme 13a: One Hypothesis Sample Test |
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Theme 13b: Two Hypothesis Sample Test – Independent Samples |
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Theme 13b: Two Hypothesis Sample Test – Dependent Samples |
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Final Examination (70% of the final mark) |
Teaching and learning activities
In person
Theoretical explanations will be given in the classroom using photocopied material and lists of problems. The lecturer's explanations will be shown on the blackboard and supplemented by the above material.
Theory will be combined with solving related problems.
Evaluation systems and criteria
In person
ONLY FOR STUDENTS OF THE ENGLISH GROUP - PROF. PABLO AGNESE
Two evaluation methods will be used:
1. Continuous evaluation (40% of the grade): Midterm (30%) and class activities and participation (10%)
2. Final examination (60%) - The grade of the final exam must always be above 4.5/10.
If any student does not pass the course at the first attempt and is required to retake in July, the final grade will be that of the second-sitting examination.
Bibliography and resources
Keller, G. “Statistics for Management and Economics”. South Western Cengage Learning.
Lind, D.A., Marchal, W.G. & S.A. Wathen. “Statistical Techniques in Business and Economics”. McGraw-Hill International Edition.
Prat, A., Tort-Martorell, X., Grima, P., & L. Pozueta. “Métodos Estadísticos: Control y Mejora de la Calidad”. Edicions UPC.
Wonnacott T. & Wonnacott R.J. “Introductory Statistics”. John Wiley & Sons.