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Teaching GuideTerm Faculty of Humanities |
| Grao en Xestión Dixital de Información e Documentación |
 Subjects |
| Fundamentals of Statistics |
| Contents |
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| Identifying Data | 2022/23 | |||||||||||||
| Subject | Fundamentals of Statistics | Code | 710G04040 | |||||||||||
| Study programme |
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| Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
| Graduate | 2nd four-month period |
First | Basic training | 6 | ||||||||||
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| Topic | Sub-topic |
| The following topics develop the contents established in the file of the Verification Report, being: | Sources of information and statistical methodology. Introduction to the use of statistical analysis programs (R software). Univariate descriptive statistics. Descriptive statistics of two variables. Analysis of dependence between variables and regression between statistical variables. Introduction to time series and index numbers. Methods for outlier detection (outliers). Probability. Random variables and probability distributions. |
| Introduction to statistics and sources of information | Introduction and purpose of statistics. Major concepts of statistics and data science. Data processing methodologies and main problems they address. Sources of statistical information. Organization of official statistics at national and international level (ONU agencies, Eurostat, INE, and IGE, among others). Main statistics in the socioeconomic field (demographic, social, employment, economic statistics). Digital information sources. |
| Descriptive statistics of a unidimensional variable. | General concepts. Frequencies tables. Measures of position, dispersion and shape. Graphical representation of unidimensional variables. |
| Descriptive statistics of more than one variable. | Descriptive statistics of two variables. Measures of position and dispersion. Graphical representation of multivariate data. Dependence relationship between qualitative variables. Dependence relationship between quantitative variables: simple linear regression. Other regression models. Descriptive methods of unsupervised classification by clusters and for the detection of outliers. |
| Introduction to the use of programs for the statistical analysis of data (R software). | Description of the statistical software R. Structure of R. Introduction to programming with R. Functions. Object definition. Assignment. Creating and importing databases. Obtaining graphs. Elaboration of reports. |
| Introduction to time series and index numbers. | Indicators in libraries and the field of documentation. Bibliometric indexes. Index numbers. Simple and composite index numbers. Introduction to descriptive analysis of time series. |
| Probability | Basic concepts. Operations with events. Laplace's rule. Properties of probability. Conditional probability. Product rule, rule of total probabilities, Bayes' rule. Applications to documentation problems. |
| Random variables | Definition of a random variable. Discrete random variables. Continuous random variables. Probability mass function. Density function. Distribution function. Mean, variance, calculation of probabilities and quantiles. |
| Probability distributions | Binomial distribution. Hypergeometric distribution. Negative binomial distribution. Normal distribution. Uniform distribution. Exponential distribution. Distributions associated to the normal distribution. Other distributions. |
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