Statistics II
Code: 102114 ECTS Credits: 6| Degree | Type | Year | Semester |
|---|---|---|---|
| 2501231 Accounting and Finance | FB | 2 | 1 |
| 2501232 Business and Information Technology | FB | 2 | 1 |
Contact
- Name:
- Maria Dolores Márquez Cebrián
- Email:
- MariaDolores.Marquez@uab.cat
Use of Languages
- Principal working language:
- catalan (cat)
- Some groups entirely in English:
- No
- Some groups entirely in Catalan:
- Yes
- Some groups entirely in Spanish:
- No
Teachers
- Anabel Blasco Moreno
- Nestor Garcia Alvarez
- Pol Montemayor Cejas
Prerequisites
It is recommended that the student has passed the following subjects: Statistics I, Mathematics I and Mathematics II. This way, the student will have acquired the competences necessary to Statistics II with the best warranty of success.
Objectives and Contextualisation
This subject will enable the students to understand and apply the statistical method to solve problems characteristic of economics and business. Thus, starting from empirical evidence gathered in a given sample the students will be able to arrive to conclusions scientifically valid which will help them in decision making.
This subject must also provide students with the theoretical foundations that will enable them to follow satisfactorily other subjects of quantitative content (Econometrics or Forecasting Models in Accounting & Finance; Operations Research in Business & Information Technologies); as well as tools that will help them with a better understanding of subjects such as Macroeconomics or Decission Suport Systems (B&IT) in which some statistical concepts (theoretical or practical) can play an important role.
Competences
- Accounting and Finance
- Analysing, summarising and assessing information.
- Communicating in oral and written form in Catalan, Spanish and English, in order to be able to summarise and present the carried out project in both forms.
- Efficiently searching information, discriminating irrelevant information.
- Interpreting and using mathematical tools and statistics in order to identify and solve problems of the economical and business scope with deterministic or/and random components.
- Business and Information Technology
- Interpreting and using mathematical and statistic tools in order to identify and solve problems of the economical and business scope with deterministic or/and random components.
Learning Outcomes
- Analysing and establishing both quantitative and qualitative conclusions about the behaviour of variables with random components.
- Analysing the variables through the contrast of hypothesis about their main characteristics.
- Analysing, summarising and assessing information.
- Appropriately drawing up technical reports according to the customer's demands.
- Communicating in oral and written form in Catalan, Spanish and English, in order to be able to summarise and present the carried out project in both forms.
- Describing and analysing the causal relationship between economic variables.
- Efficiently searching information, discriminating irrelevant information.
- Students must be capable of analysing, summarising, organising, planning and solving problems and making decisions.
Content
Statistics II - Inferential Statistics
Unit 1 Introduction to Inferential Statistics and Estimation
1.1 Inferential Statistics: Definition and Inference Methods
1.2 Definition, characteristics and Distribution of the main sample statistics: mean, variance and proportion
1.3 Methods of point estimation and interval estimation
1.4 Properties of estimators: bias, efficiency and consistency
1.5 Methods of estimation: maximum likelihood and method of moments
Unit 2 Parametric hypothesis tests
2.1 Concept of parametric test: null hypothesis and alternative hypothesis
2.2 Test statistic and error type
2.3 Tests on the population mean, population variance and population proportion
2.4 Sample comparison test
2.5 Analysis of Variance
2.6 The p-value
Unit 3 Goodness-of-fit and analysis of the relationship between variables
3.1 Chi-Square goodness-of-fit test for discrete variables
3.2 K-S goodness-of-fit test for continuous variables
3.3 Test of independence between qualitative variables
3.4 Analysis of the correlation between quantitative variables: correlation coefficient
Unit 4 Introduction to the regression model
4.1 Presentation and objectives of the model
4.2 Hypothesis of the model specification
4.3 Estimation by Ordinary Least Squares (OLS) and their properties
4.4 Model testing
4.5 Coefficient of the goodness-of-fit and relationship between the correlation and the regression analysis
4.6 Forecasting
Methodology
Teaching will be offered on campus or in an on-campus and remote hybrid format depending on the number of students per group and the size of the rooms at 50% capacity.
The activities that will allow the students to learn the basic concepts included in this course are:
1. Theory lectures where the instructor will explain the main concepts.
The goal of this activity is to introduce the basic notions and guide the student learning.
2. Problem Sets
A problem set which students will have to solve individually will be included in every unit. The goal of this activity is twofold. On one hand students will work with the theoretical concepts explained in the classroom, and on the other hand through this practice they will develop the necessary skills for problem solving.
3. Lab sessions
The aim of this activity is to comment on and solve any possible doubt that students may have had solving the problem assignment. This way they will be able to understand and correct any errors they may have had during this process. Moreover, in this activity, we use statistical software to better grasp the statistical concepts and methods.
The level of use of ICT will be subject to availability and the number of students registered in the groups.
4. Tutoring hours
Students will have some tutor hours in which the subject instructors will help them solve any doubts they may have.
The proposed teaching methodology may undergo some modifications according to the restrictions imposed by the health authorities on on-campus courses.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Activities
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Lab sessions | 13 | 0.52 | 1, 2, 6 |
| Lectures with ITC support | 33 | 1.32 | 1, 2, 6 |
| Type: Supervised | |||
| Tutoring and monitoring work in progress | 7.5 | 0.3 | 1, 2, 6 |
| Type: Autonomous | |||
| Exercici solving | 93 | 3.72 | 1, 2, 6 |
Assessment
The evaluation of the students will be carried out according to the following activities:
1. A midterm exam
Written evidence in which the student will not be allowed to consult any kind of teaching material. The maximum resolution time will be 50 minutes. This test does not release matter.
2. A final exam
Written evidence in which the student will not be allowed to consult any kind of teaching material. The maximum resolution time will be 2 hours, and will include all the subject matter of the course.
The exam is designed so that the student performs a last learning effort that is considered necessary to consolidate the previously acquired knowledge, thus guaranteeing the success in the continuous learning process of the greatest possible number of students.
3. Submission of problem sets and essays, and/or lab practices
Students will submit, at the request of the teaching staff and following their instructions, various exercises, lab practices and/or essays to be solved individually and/or in groups of between 2 and 4 students.
Evaluation criteria
The grade of the midterm exam will weight a 20% of the average grade of the subject.
The grade of the final exam will weight a 50% of the average grade of the subject.
The grade of the submission of exercises, essays and/or lab practices will weight a 30% of the average grade of the subject.
Therefore, the average grade of the subject is computed as:
average grade of the subject = 20% (grade of the midterm exam) +
+ 50% (grade of the final exam)+
+ 30% (grade exercises/essays/lab practices)
The subject will be considered "passed" if the following two requirements are met:
1. the average grade of the subject is equal to or greater than 5 and
2. the grade of the final exam is equal to or greater than 3.
- A student that meets the first requirement above but does not meet the second will receive an average grade of the subject equal to 4.5, and will qualify for the re-evaluation test according to what is established in the section "Retake Process" below.
- A student that meets the second requirement above but does not meet the first, or any of them, will qualify for the re-evaluation test according to what is established in the section "Retake Process" below.
A student who has not participated in any of the assessment activities will be considered "Not evaluable"
Calendar of evaluation activities
The dates of the evaluation activities (exercises in the classroom, assignments, ...) will be announced well in advance during the semester. The dates of the final exam and mid-term exam are scheduled in the assessment calendar of the Faculty.
"The dates of evaluation activities cannot be modified, unless there is an exceptional and duly justified reason why an evaluation activitycannot be carried out. In this case, the degree coordinator will contact both the teaching staff and the affected student, and a new date will be scheduled within the same academic period to make up for the missed evaluation activity." Section 1 of Article 115. Calendar of evaluation activities (Academic Regulations UAB).
Students of the Faculty of Economicsand Business, who inaccordance with the previous paragraph need to change an evaluation activity date must process the requestby filling out an Application for exams' reschedule https://eformularis.uab.cat/group/deganat_feie/application-for-exams-reschedule
Grade revision process
After all grading activities have ended, students will be informed of the date and way in which the course grades will be published.
Students will be also be informed of the procedure, place, date and time of grade revision following University regulations.
Retake Process
"To be eligible to participate in the retake process, it is required for students to have been previously been evaluated for at least two thirds of the total evaluation activities of the subject." Section 3 of Article 112 ter. The recovery (UAB Academic Regulations). Additionally, it is required that the student to have achieved an average grade of the subject between 3.5 and 4.9.
The date of the retake exam will be posted in the calendar of evaluation activities of the Faculty. Students who take this exam and pass, will get a grade of 5 for the subject. If the student does not pass the retake, the grade will remain unchanged, and hence, student will fail the course.
The proposed evaluation activities may undergo some changes according to the restrictions imposed by the health authorities on on-campus courses.
Irregularities in evaluation activities
In spite of other disciplinary measures deemed appropriate, and in accordance with current academic regulations, "in the case that the student makes any irregularity that could lead to a significant variation in the grade of an evaluationactivity, it will be graded with a 0, regardless of the disciplinary process that can be instructed. In case of various irregularities occur in the evaluation of the same subject, the finalgrade of this subject will be 0". Section 10 of Article 116. Results of the evaluation. (UAB Academic Regulations).
Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Exercises, essays and/or lab practices | 30% | 0.5 | 0.02 | 1, 2, 3, 7, 6, 4, 5 |
| Final exam | 50% | 2 | 0.08 | 1, 2, 6, 8 |
| Midterm exam | 20% | 1 | 0.04 | 1, 2, 6 |
Bibliography
Canavos, GC Applied probability and statistical methods. McGraw-Hill. McGraw-Hill. 1998
Heumann C, Schomaker M. and Shalabh Introduction to Statistics and Data Analysis Springer 2016
https://link.springer.com/content/pdf/10.1007%2F978-3-319-46162-5.pdf
Lind, DA et al. Statistical Techniques in Business and Economics. McGraw-Hill. McGraw-Hill. 2012
Newbold P. Statistics for business and economics. Pearson-Prentice Hall. Pearson-Prentice Hall. 2005
Links:
http://www.seeingstatistics.com