Statistical Inference & Data Analysis

Statistical Inference & Data Analysis (2225.YR.005017.1)

COURSE CONTRIBUTION TO PROGRAM

Course Learning Objectives

CONTENT

Methodology

Bibliography

Timetable and sections

General information

Prerequisites

Previous Knowledge

Workload distribution

1. SAMPLE DISTRIBUTIONS

2. ESTIMATION

3. HYPOTHESIS TESTING BASICS

4. NON-PARAMETRIC TESTS

5. DEPENDENCY

6. INTERDEPENDENCY

Relation between Activities and Contents

Assessment criteria

Timetable Year 2

Type:

Curs:

Period:

ECTS Credits:

Teaching Staff:

Group	Teacher	Department	Language
Year 2	Vicenta Sierra Olivera	Operaciones, Innovación y Data Sciences	CAT, ESP
Year 2	David Roche Valles	Operaciones, Innovación y Data Sciences	CAT, ESP

Statistics is a fundamental tool to make decisions in settings in which the quantity of data and/or the level of uncertainty 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 and valuable insights. In addition to the most common univariate and bivariate inferential 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.

Upon completing this course, students should be able to:
-Use statistical reasoning in practical applications.
-Make 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 form clusters.
-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.

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 of means, variance and proportions (one sample) 3.2 Test of 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 of 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

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

2.1 Introduction
2.2 Point estimation
2.2 Interval estimation

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

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

5.1 Introduction
5.2 Simple regression analysis
5.3 Multiple regression analysis

6.1 Introduction
6.2 Key components analysis
6.3 Cluster analyses

Lectures/participatory sessions:
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 studied 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. Case study analyses and resolution: 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. Concept tests: students will have to sit follow-up tests in class on the material covered in the previous sessions.

Description	%
Exams	70
Assimilation of practical content	20
Concept assessment	10

This course consists of two parts which will be assessed independently. Consequently, the mid-term exam (exam 1) will cover the first four subject blocks addressed in class, while the final exam (exam 2) will cover the last two.

Students' final marks for this class will be calculated as follows:
· Exams 1 and 2 on the subjects addressed: 70%
· Assimilation of practical content (2 tests): 20%
· Concept tests: 10%

Should students earn less than a 5 (out of 10) as their final class marks, they will have to re-sit the final exam on all the course material. In this case, the re-sit exam will represent 80% of their final marks for the class. The remaining 20% will correspond to their mean weighted mark on the other two components.

Enrolled students who will only be sitting the final exam (December) will be tested on both the theoretical and practical content seen in this subject.

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 students need to successfully keep up with the course (videos, readings, complementary exercises, databases, self-assessment tests, etc.).
2. Restricted access to micro-courses on the subject content via the Snackson application.

Group	Teacher	Department
Year 2	Vicenta Sierra Olivera	Operaciones, Innovación y Data Sciences
Year 2	David Roche Valles	Operaciones, Innovación y Data Sciences

From 2022/9/6 to 2022/9/22:
Each Thursday from 8:00 to 9:15.
Each Thursday from 9:15 to 10:30.
Each Tuesday from 8:00 to 10:30. (Except: 2022/9/13 and 2022/9/20)
Each Tuesday from 13:30 to 14:30. (Except: 2022/9/6)

From 2022/9/13 to 2022/10/11:
Each Tuesday from 8:00 to 9:15.
Each Tuesday from 9:15 to 10:30.
Each Tuesday from 13:30 to 14:30. (Except: 2022/9/13, 2022/9/20 and 2022/9/27)

From 2022/10/6 to 2022/10/19:
Each Thursday from 8:00 to 9:15. (Except: 2022/10/13)
Each Wednesday from 9:45 to 13:00. (Except: 2022/10/12)
Each Thursday from 9:15 to 10:30. (Except: 2022/10/13)
Each Thursday from 8:00 to 10:30. (Except: 2022/10/6)
Each Wednesday from 9:45 to 13:45. (Except: 2022/10/12)

From 2022/10/27 to 2022/11/29:
Tuesday and Thursday from 8:00 to 10:30. (Except: 2022/11/1)
Each Tuesday from 13:30 to 14:30. (Except: 2022/11/1)

Tuesday2022/12/13:
From 8:45 to 14:30.
From 8:45 to 13:30.

Type:	OBL	Curs:	2	Period:	S semester
ECTS Credits:	6 ECTS

Assimilation of practical content

Concept assessment