Statistics II
15.0 ECTS creditsThe course covers the following subject areas:
Basic Statistical Inference
This module covers the way in which conclusions about a population can be drawn based on a random sample, as well as the way in which properties of two different populations can be compared on the basis of two random samples.
Variance Analysis
This module covers generalisations of hypothesis testing for situations where there are more than two random samples.
Regression Analysis
This module covers methods for assessing and estimating connections between different variables. The emphasis is placed in linear models with one or more explanatory variables, but the module also includes a number of non-linear models.
Non-Parametric Models
This module covers a number of non-parametric methods, which can sometimes be used when the conditions required for applying traditional methods are not present.
Time-Serial Analysis
This module provides an introduction to prognosis methodology and different methods for organising a time series in components such as trend, tendency, season, and chance.
Basic Statistical Inference
This module covers the way in which conclusions about a population can be drawn based on a random sample, as well as the way in which properties of two different populations can be compared on the basis of two random samples.
Variance Analysis
This module covers generalisations of hypothesis testing for situations where there are more than two random samples.
Regression Analysis
This module covers methods for assessing and estimating connections between different variables. The emphasis is placed in linear models with one or more explanatory variables, but the module also includes a number of non-linear models.
Non-Parametric Models
This module covers a number of non-parametric methods, which can sometimes be used when the conditions required for applying traditional methods are not present.
Time-Serial Analysis
This module provides an introduction to prognosis methodology and different methods for organising a time series in components such as trend, tendency, season, and chance.
Progressive specialisation:
G1F (has less than 60 credits in first‐cycle course/s as entry requirements)
Education level:
Undergraduate level
Admission requirements
15 credits in Statistics plus upper secondary level Mathematics C.
Selection:
Selection is usually based on your grade point average from upper secondary school or the number of credit points from previous university studies, or both.