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Inferencia Estadística y Análisis de Datos (19BBA40010)

General information

Type:

OB

Curs:

2

Period:

S semester

ECTS Credits:

6 ECTS

Teaching Staff:

Group Teacher Department Language
Sec: A Vicenta Sierra Olivera Operaciones, Innovación y Data Sciences CAT
Sec: A David Roche Valles Operaciones, Innovación y Data Sciences CAT

Group Teacher Department Language
Sec: B Vicenta Sierra Olivera Operaciones, Innovación y Data Sciences ESP
Sec: B David Roche Valles Operaciones, Innovación y Data Sciences ESP

Group Teacher Department Language
Sec: C Vicenta Sierra Olivera Operaciones, Innovación y Data Sciences CAT
Sec: C David Roche Valles Operaciones, Innovación y Data Sciences CAT

Prerequisites

Statistics I

Previous Knowledge

Statistics I

Workload distribution

Workload distribution:
Lectures: 31.5 hours
Participatory sessions: 28.5 hours
Independent study: 55 hours
Tutorials/feedback: 10 hours

COURSE CONTRIBUTION TO PROGRAM

Statistics is a fundamental tool to make decisions in settings in which the quantity of data and/or the level of doubt does not allow us to extract information directly. In this class, we will explore some of the theoretical and practical principles that constitute the basis for predictions, estimates and testing hypotheses, all of which are used to transform the information we have into knowledge. In addition to the most common inferential and bivariate techniques, in this course we will also examine some useful multivariate techniques to resolve important problems in other contexts such as Marketing and Human Resources.

Course Learning Objectives

Upon completing this course, students should be able to:
-Use statistical reasoning in practical applications.
-Take decisions in environments affected by uncertainty.
-Relate the course to the other courses they are studying and to their future professional lives.
-Make inferences on unknown population parameters.
-Select the appropriate statistical technique to carry out estimates or forecasts, segment and fing groupings.
-Contrast hypotheses on parameters or on population distributions.
-Understand the limits of the techniques studied according to each context in which they are applied.
-Use statistical software to make data-based decisions.

CONTENT

1. SAMPLE DISTRIBUTIONS

1.1 Introduction to sample distributions
1.2 Distribution of sample mean
1.3 Distribution of sample proportion
1.4 Distribution of sample variance

2. ESTIMATION

2.1 Introduction
2.2 Point estimation
2.2 Interval estimation

3. HYPOTHESIS TESTING BASICS

3.1 Hypothesis testing concepts: P-value and the statistical power of tests
3.2 Test for means, variance and proportions (one sample)
3.2 Test for means, variance and proportions (two samples)
3.4 ANOVA test

4. NON-PARAMETRIC TESTS

4.1 Goodness-of-fit test
4.2 Pearson's chi-square test for independence and homogeneity

5. DEPENDENCY

5.1 Introduction
5.2 Simple regression analysis
5.3 Multiple regression analysis

6. INTERDEPENDENCY

6.1 Introduction
6.2 Key components analysis
6.3 Cluster analyses

Relation between Activities and Contents

1 2 3 4 5 6
Mid-term exam and final exam            
Follow-up tests            
Problem-solving            

Methodology

Lectures:
Faculty will combine their explanations on theoretical aspects with exercises and case studies. Complementarily, students will have controlled access to educational pills designed exclusively for the subject content and concepts via the Snackson app available for tablets and smartphones.

Participatory sessions:
Students will carry out a wide variety of activities in these sessions, including:
1. Problem assessment and resolution (individually and in groups)
2. 4 data analysis projects (cases): students have to demonstrate their skills and knowledge of statistical software and its use as well as their critical reading of results from these for decision-making purposes.
3. Follow-up tests: students will have to sit a minimum of three follow-up tests in class on the material covered in the previous sessions.

Assessment criteria

This course consists of two parts which will be assessed independently. Consequently, there will be a mid-term exam in October on the first four sections addressed in class. So long as students obtain a minimum mark of 4 out of 10 on this exam, the final exam will not include that material from the first four sections. The final exam will be offered at the end of the class on the remaining sections.

The final exam will be as follows:
A. Students who earn the minimum mark required on the mid-term exam will only have to sit the final exam on the content from sections 5 and 6.
B. Students who do not earn the minimum mark required on the mid-term exam will have to carry out the final exam on sections 5 and 6 as well as a second exam on the content of the first four sections.

In both cases, students must earn a minimum mark of 4 or above on the definitive exams in order to calculate their final marks for the class.

Students' final marks for this class will be calculated as follows:
· problem resolution exercises
· follow-up tests
· mean score between the two parts of this course.

Should students earn less below a 5 for their final class marks, they will have to re-sit the final exam on all the course material. Their marks on this re-sit exam will represent 100% of their final marks for the class.

Bibliography

Sections A, B and C:
Newbold, P.; Carlson, W. L.; Thorne, B. (2008). Estadística para administración y economía. Pearson - Prentice Hall.
Hair, J.F.; Anderson, R.E.; Tatham, R.L; Black, W.C. (2000) Análisis Multivariante. Pearson - Prentice Hall.

Section D and E:
Newbold, P., Carlson, W.L. & Thorne, B. (2012). Statistics for Business and Economics. Ed. Pearson - Prentice Hall
Hair, J.F.;.; Black, W.C; Babin, B.J.; Anderson, R.E. (2010) Multivariate Data Analysis. Pearson - Prentice Hall.

Complementary material:
1. The course website includes complementary material which will help students stay abreast of the course (videos, readings, complementary exercises, databases, self-evaluations, etc.).
2. Restricted access to micro-courses on the subject content via the Snackson application.

Timetable and sections

Group Teacher Department
Sec: A Vicenta Sierra Olivera Operaciones, Innovación y Data Sciences
Sec: A David Roche Valles Operaciones, Innovación y Data Sciences

Timetable Sec: A

Group Teacher Department
Sec: B Vicenta Sierra Olivera Operaciones, Innovación y Data Sciences
Sec: B David Roche Valles Operaciones, Innovación y Data Sciences

Timetable Sec: B

Group Teacher Department
Sec: C Vicenta Sierra Olivera Operaciones, Innovación y Data Sciences
Sec: C David Roche Valles Operaciones, Innovación y Data Sciences

Timetable Sec: C