Statistical Inference & Data Analysis

Statistical Inference & Data Analysis (2215.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

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. 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 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

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 for means, variance and proportions (one sample)
3.2 Test for means, variance and proportions (two samples)
3.4 ANOVA test

4.1 Goodness-of-fit test
4.2 Pearson's chi-square test for 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 (deliverable): 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 follow-up tests in class on the material covered in the previous sessions.

Description	%
Exam 1	20
Exam 2	15
Practical assessable activities, part 1	30
Problem-solving exercises (content integration)	35

This course consists of two parts which will be assessed independently. Consequently, there will be an exam in October on the first four subject blocks addressed in class. The second exam will be offered at the end of the class on subject blocks 5 and 6.

Students' final marks for this class will be calculated as follows:
· Exams 1 and 2 on the subjects addressed
· 7 to 8 and asessable practical exercises

Failing to attend and complete either of the exams will result in a 0.

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 7 to 8 assessable practical exercises carried out during the course.

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 to stay abreast of 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

From 2021/9/7 to 2021/9/21:
Tuesday and Thursday from 8:00 to 9:15. (Except: 2021/9/7)
Tuesday and Thursday from 9:15 to 10:30. (Except: 2021/9/7)
Each Tuesday from 8:00 to 10:30. (Except: 2021/9/14 and 2021/9/21)
Each Tuesday from 13:30 to 14:30. (Except: 2021/9/7)

From 2021/9/16 to 2021/10/7:
Tuesday and Thursday from 8:00 to 9:15. (Except: 2021/9/16, 2021/9/21 and 2021/9/28)
Tuesday and Thursday from 9:15 to 10:30. (Except: 2021/9/21 and 2021/9/28)

From 2021/10/5 to 2021/10/20:
Each Thursday from 8:00 to 10:30. (Except: 2021/10/7)
Each Wednesday from 14:45 to 18:00. (Except: 2021/10/6 and 2021/10/13)
Each Tuesday from 13:30 to 14:30. (Except: 2021/10/12 and 2021/10/19)

From 2021/10/20 to 2021/11/16:
Each Wednesday from 14:45 to 18:45. (Except: 2021/10/27, 2021/11/3 and 2021/11/10)
Tuesday and Thursday from 8:00 to 10:30. (Except: 2021/10/21)
Each Tuesday from 13:30 to 14:30. (Except: 2021/11/16)

From 2021/11/16 to 2021/11/25:
Each Thursday from 8:00 to 9:15. (Except: 2021/11/25)
Each Thursday from 9:15 to 10:30. (Except: 2021/11/25)
Tuesday and Thursday from 8:00 to 10:30. (Except: 2021/11/16 and 2021/11/18)
Each Tuesday from 13:30 to 14:30.

Friday2021/12/10:
From 8:45 to 14:15.
From 8:45 to 13:30.

Wednesday2022/6/29:
From 13:45 to 18:00.
From 13:45 to 19:00.

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

Practical assessable activities, part 1

Problem-solving exercises (content integration)