Statistics II

COMPETENCES AND SKILLS THAT WILL BE ACQUIRED AND LEARNING RESULTS. SPECIFIC SKILLS:

Students will acquire knowledge and skills necessary to:

1. Perform statistical inference in one population
2. Understand the key concepts in hypothesis testing
3. Become familiar with the issues of comparing two populations
4. Interpret and apply the concepts of the simple linear regression model
5. Carry out the abovementioned analyses in statistical software

GENERAL SKILLS

Students will be able to:

1. Develop their ability to think analytically
2. Become familiar with a statistical software
3. Establish a framework to solve problems
4. Develop their interactive skills
5. Enhance their critical thinking
6. Improve their learning skills and communication

DESCRIPTION OF CONTENTS: PROGRAMME

Chapter 1. Inference in one population
Introduction: parameters and statistical inference
Point estimators
The estimation of the mean and variance
The sampling distribution of the sample mean
Estimation using confidence intervals
Confidence interval for the mean of a normal population with known variance
Confidence interval for the mean in large samples
Confidence interval for the mean of a normal population with unknown variance: t distribution
Confidence interval for the variance of a normal population

Chapter 2. Basic concepts in hypothesis testing
Definition of a test of hypothesis
The null and alternative hypotheses
Type I and type II errors, power of the test
The concept of p-value and decision-making
Main steps needed to perform a test of hypothesis

Chapter 3. Comparing two populations
Independent samples from two populations
Inference for the population means in small samples
Inference for the population means in large samples
Comparing the variances of two normal populations: the F distribution

Chapter 4. Regression analysis: the simple linear regression model
The goal of regression analysis
The specification of a simple linear regression model
Least-squares estimators: construction and properties
Inference in the linear regression model
Inference for the slope
Inference for the variance
Mean response and confidence intervals
New response and prediction intervals

Chapter 5. Regression analysis: assumptions, model diagnostics, multiple linear regression model
The residual analysis
The ANOVA decomposition
Nonlinear relationships and linearizing transformations
The linear regression model in matrix form
Introduction to multiple linear regression

LEARNING ACTIVITIES AND METHODOLOGY
Theory (3 ECTS): During theoretical sessions, the contents of the course will be introduced, explained and ilustrated with examples. Teaching materials will be provided on the Internet.
Practice (3 ECTS): During practical sessions, black-board exercises will be solved. Software-related
activities will take place in the computer labs. In the 15th week of the term, a group-review session for the final exam will be held.

ASSESSMENT SYSTEM

60% of the semester mark will be obtained from the final exam. To pass the course, a minimum score of 4 (out of 10)
on the final exam is required.
The remaining 40% will be based on a continued assessment during the term. Students will be required
to demonstrate their understanding of theoretical concepts as well as their ability to apply the theory to solve problems.

The percentages are as follows:
17,5% from the grade in the first midterm exam.
17,5% from the grade in the second midterm exam.
5% from the evaluation of different activities carried out in class.
The activities in class whose evaluation corresponds to the final 5% of the grade may consist of one or several exercises. This number and the evaluation dates will be set by each group instructor according to the rate of progress of the teaching in the group.

% end-of-term-examination: 60

% of continuous assessment (assigments, laboratory, practicals?): 40

BASIC BIBLIOGRAPHY
- Paul Newbold Statistics for Business and Economics, Pearson Prentice Hall, 2010
- Cheng Lee Statistics for business and financial economics, World Scientific, 2000
ADITIONAL BIBLIOGRAPHY
- Sheldon Ross Introductory Statistics, Elsevier Academic Press, 2005