Teaching Plan Information

1. Course details

1.1. Content area

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1.2. Course nature

Optional

1.3. Course level

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1.4. Year of study

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1.5. Semester

First semester

1.6. ECTS Credit allotment

6.0

1.7. Language of instruction

English

1.8. Prerequisites

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1.9. Recommendations

Good background in High School Algebra

1.10. Minimum attendance requirement

Attendance is compulsory

1.11. Subject coordinator

Adolfo Quiros Gracian

1.12. Competences and learning outcomes

1.12.1. Competences / Results of the training and learning outcomes

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1.12.2. Learning outcomes

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1.12.3. Course objectives

The main aim of this course is to introduce the student to the basic statistical concepts that will permit a first approach to the descriptive and the inferential statistical tools, giving enough background to interpret the basic Statistics results found in scientific papers. The course is completed with a short introduction to the elementary concepts in Probability, essential to give a scientific foundation to Mathematical Statistics. These general objectives may be summarized in the following four points:

1. Introduction to the basic Statistical tools to analyze data proceeding from a variety of sources.
2. Introduction to the basics of Probability.
3. Ability to read and understand statistical texts from several scientific areas.
4. Use of basic computing statistical tools.

1.13. Course contents

• DESCRIPTIVE STATISTICS: Graphical and numerical representation of quantitative data. Paired data: covariance, regression line, correlation coefficient.    
  
• PROBABILITY MODELS AND SAMPLING: Discrete random variables. Bernoulli trials. Binomial distribution. Continuous random variables. Uniform distribution. Normal distribution. Sampling. Estimators. Distributions related to the normal distribution: Chi square, Student's t, F.
  POINT ESTIMATION: The concept of a point estimator. Properties. Criteria to determine good point estimators.
  
• CONFIDENCE INTERVALS: Constructing confidence intervals. Confidence intervals for proportions. Confidence intervals for means in normal populations. Paired data. Approximate intervals from large samples. Minimum sample size.
  HYPOTHESIS TESTING: Setting of the problem. Null and alternative hypothesis. Type I and Type errors. Significance level and rejection set. Tests for ratios. Tests for mean in  normal populations. Paired data. Relationship between confidence intervals and hypothesis testing. What is the p-value? Non-parametric tests: goodness of fit.

1.14. Course bibliography

• MCCLAVE, JAMES T. and SINCICH, TERRY. Statistics (12^th international ed.) Pearson Education International. ISBN 13: 978-0-321-80728-1.

         Other texts

• MOORE, DAVID S.  The Basic Practice of Statistics. W. H. Freeman, (several editions).
• MILTON, J. SUSAN and ARNOLD, JESSE C. Introduction to Probability & Statistics. McGraw-Hill, (several editions).

2. Teaching-and-learning methodologies and student workload

2.1. Contact hours

The course will usually meet 4 hours per week. They will be dedicated to covering the material of the course, discussing and solving exercises, using specialized computer software, and doing exams and quizzes.

Homework will be assigned regularly. Part of it will be graded. Homework can be worked out in groups, but should be turned in individually.

2.2. List of training activities

3. Evaluation procedures and weight of components in the final grade

3.1. Regular assessment

During the semester, two quizzes and a midterm will be given. The final grade will determined as follows: the final exam will count 40%, the midterm will count 30%, homework and quizzes 30%.

This basic grade will be modulated by a continuous assessment based on in-class participation and the evolution of the marks in the written tests.

3.1.1. List of evaluation activities

3.2. Resit

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3.2.1. List of evaluation activities

4. Proposed workplan

Our weekly sessions will follow, more or less, the following schedule:

• Introduction and overview. Statistics, what is it? Types of data.

• Data description, summaries. Diagrams, plots, numbers.

• Introduction to Probability, elementary problems.

• A more formal approach to probability.

• Random variables. Discrete random variables.

• Bernoulli trials, binomial distribution.

• Continuous random variables. Uniform and normal distributions.

• Sampling and the Central Limit Theorem.

• Point and interval estimation. Means and Proportions.

• Hypothesis testing.

• Hypothesis testing for the mean, known variance.

• Hypothesis testing for mean and variance.

• Two-sample inferences, equal variances.

Apart from the final exam there will be two quizzes and a midterm. We will have a review session before the midterm. You can check the dates in the official calendar.