This course provides you with a solid understanding of modern linear regression and ANOVA models. It also covers some common but advanced regression models as well as survival analysis.

We will have a closer look at how these models work and how R can be used to build, visualise, and interpret such models. We will use modern techniques like the bootstrap and permutation tests, to obtain confidence intervals and p-values without having to assume a normal distribution for your data. We will cover non-linear regression models like logistic regression and Poisson regression, where the response variable can be either binary (yes/no), counts, or prevalence. In survival analysis, we’ll have a look at Kaplan-Meier survival curves and regression models, including Cox proportional hazards regression. Mixed models are used to analyse data with repeated measurements on the same subjects.

This course will teach you how visually explore data in R, and how to create great-looking graphics using the powerful ggplot2 package. We´ll also discuss cluster analysis, including hierarchichal and centroid-based methods, factor analysis and structural equation models (SEM), used to measure and analyse the relationship between observed and hidden variables, as well as mediation analysis.

Topics covered include outlier detection, visualisation of trends, and multivariate data. It also covers dimension-reduction of complex data using principal component analysis (PCA). Cluster analysis is used to find subgroups in exploratory analyses of your data. SEM allows us to study causal relationships between variables in our data and latent (unobservable) variables, such as difficult-to-measure attitudes. Mediation analysis is used to understand the mechanism behind causal relationships.

In this course, you’ll learn about two fundamental tools in statistical analysis: hypothesis tests and confidence intervals. We’ll also discuss important concepts like p-values, power, and sample size calculcations.

Classical statistical tests like the t-test, nonparametric tests and the chi-squared test are covered, along with modern computer-intensive methods like the bootstrap. The latter allows us to obtain p-values and confidence intervals without many of the constraints of traditional methods (such as requiring that the data follow a normal distribution), bringing your statistical toolbox up to the 21st century.

This course will teach you how visually explore data in R, and how to create great-looking graphics using the powerful ggplot2 package.

Topics covered include outlier detection, visualisation of trends, and multivariate data. It also covers dimension-reduction of complex data using principal component analysis (PCA).

In this course, we discuss cluster analysis, including hierarchichal and centroid-based methods, and factor analysis and structural equation models (SEM), used to measure and analyse the relationship between observed and hidden variables.

Cluster analysis is used to find subgroups in exploratory analyses of your data. SEM allows us to study causal relationships between variables in our data and latent (unobservable) variables, such as difficult-to-measure attitudes.

Kursen hjälper dig att snabbt komma igång med R Commander och behandlar de centrala delarna av programmet. Rcmdr 1 är en idealisk bas för de mer avancerade kurserna i vårt kursutbud.

Första dagen inleds med en genomgång av statistikprogrammet R och tilläggspaketet R Commander. Kursdeltagarna får strategier att på bästa sätt få tillgång till programmets kraftfulla data och grafhanteringsfunktioner. Andra kursdagen ägnas åt beskrivande statistik och statistisk inferens (eller statistisk slutledning). Detta ger en bra introduktion, både för nybörjare och för den som vill fräscha upp gamla kunskaper, om centrala statistiska begrepp och de vanligaste statistiska.

Kursen ger en grundlig förståelse för enkla och avancerade modeller i regression och ANOVA.

Under första dagen kommer regression, som är den naturliga fortsättningen på korrelationer, alltså sambandet mellan två numeriska variabler att behandlas. I kursen kommer modeller med en eller flera förklarande variabler att behandlas. Under dag två behandlas ANOVA som är den naturliga fortsättningen på Student’s t-test, alltså jämförelse av gruppmedelvärden. I kursen kommer modeller med en eller flera förklarande variabler att behandlas liksom interaktioner och olika post-hoc test.