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Statistics
OVERVIEW
CEA CAPA Partner Institution: Universidad Carlos III de Madrid
Location: Madrid, Spain
Primary Subject Area: Mathematics
Instruction in: English
Course Code: 18274
Transcript Source: Partner Institution
Course Details: Level 300
Recommended Semester Credits: 3
Contact Hours: 42
Prerequisites: It is advisable to have successfully completed the subjects of Probability and Differential, Integral and Vector Calculus.
DESCRIPTION
1. Introduction to Statistical inference.
1.0. Elements of descriptive statistics.
1.1. Population and sample.
1.2. Random sampling.
1.3. Main distributions in sampling.
1.4. Point estimation of parameters.
1.4.1. Definitions and properties.
1.4.2. Method of moments.
1.4.3. Maximum likelihood estimation
2. Confidence intervals.
2.1. Introduction
2.1.1. Pivotal quantities.
2.2. Confidence intervals for the mean and variance in a normal population.
2.3. Confidence intervals for the mean in non-normal populations.
2.4. Confidence intervals for two populations.
2.5. Bootstrap confidence intervals.
3. Contraste estadístico de hipótesis.
3.1. Introducción.
3.2. Errores Tipo I y Tipo II.
3.3. Potencia de un contraste.
3.4. Contraste de hipótesis para la media.
3.5. Contraste de hipótesis para la proporción.
3.6. Contraste de hipótesis para la varianza.
3.7. Contrastes de hipótesis para dos poblaciones.
3. Hypothesis statistical testing.
3.1. Introduction
3.2. Type I and Type II Errors.
3.3. Power of a statistical test.
3.4. Hypothesis test for the mean.
3.5. Hypothesis test for the proportion.
3.6. Hypothesis test for the variance.
3.7. Hypothesis test for two populations.
4. Non-parametric contrasts.
4.1. Introduction.
4.2. Goodness-of-fit test.
4.2.1. Test ¿2.
4.2.2. Kolmogorov-Smirnov Test.
4.2.3. Lilliefors Test.
4.2.4. Graphical tools.
4.3. Tests based on the binomial distribution.
4.4. Tests based on ranks.
4.5 Tests of independence and homogeneity.
5. Linear regression.
5.1. Introduction.
5.2. Simple linear regression
5.3. Multiple linear regression
1.0. Elements of descriptive statistics.
1.1. Population and sample.
1.2. Random sampling.
1.3. Main distributions in sampling.
1.4. Point estimation of parameters.
1.4.1. Definitions and properties.
1.4.2. Method of moments.
1.4.3. Maximum likelihood estimation
2. Confidence intervals.
2.1. Introduction
2.1.1. Pivotal quantities.
2.2. Confidence intervals for the mean and variance in a normal population.
2.3. Confidence intervals for the mean in non-normal populations.
2.4. Confidence intervals for two populations.
2.5. Bootstrap confidence intervals.
3. Contraste estadístico de hipótesis.
3.1. Introducción.
3.2. Errores Tipo I y Tipo II.
3.3. Potencia de un contraste.
3.4. Contraste de hipótesis para la media.
3.5. Contraste de hipótesis para la proporción.
3.6. Contraste de hipótesis para la varianza.
3.7. Contrastes de hipótesis para dos poblaciones.
3. Hypothesis statistical testing.
3.1. Introduction
3.2. Type I and Type II Errors.
3.3. Power of a statistical test.
3.4. Hypothesis test for the mean.
3.5. Hypothesis test for the proportion.
3.6. Hypothesis test for the variance.
3.7. Hypothesis test for two populations.
4. Non-parametric contrasts.
4.1. Introduction.
4.2. Goodness-of-fit test.
4.2.1. Test ¿2.
4.2.2. Kolmogorov-Smirnov Test.
4.2.3. Lilliefors Test.
4.2.4. Graphical tools.
4.3. Tests based on the binomial distribution.
4.4. Tests based on ranks.
4.5 Tests of independence and homogeneity.
5. Linear regression.
5.1. Introduction.
5.2. Simple linear regression
5.3. Multiple linear regression
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