Aan de slagGa gratis aan de slag

Thuis
Cursussen
Multivariate kansverdelingen in R

Gemiddeldevector en variantie-covariantiematrix

Create Your Free Account

or

Email Address

Password

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Deze oefening maakt deel uit van de cursus

Multivariate kansverdelingen in R

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

In this introduction to multivariate data, you will learn how to read and summarize it. You will learn how to summarize multivariate data using descriptive statistics, such as the mean vector, variance-covariance, and correlation matrices. You'll then explore plotting techniques to provide insights into multivariate data.

Exercise 1: Meerdimensionale data lezen Exercise 2: Meervoudige data lezen met read.table Exercise 3: Datatypes voor kolommen specificeren Exercise 4: Gemiddeldevector en variantie-covariantiematrix

Huidige oefening

Exercise 5: De gemiddeldevector berekenen Exercise 6: De variantie-covariantiematrix berekenen Exercise 7: De correlatiematrix berekenen Exercise 8: Meervoudige data plotten Exercise 9: Pairs-plot met base graphics en lattice Exercise 10: 3D-plottechnieken

This chapter will introduce you to the most important and widely used multivariate probability distribution, the multivariate normal. You will learn how to generate random samples from a multivariate normal distribution and how to calculate and plot the densities and probabilities under this distribution. You will also learn how to test if a dataset follows multivariate normality.

Exercise 1: Multivariate normal distribution Exercise 2: Samples from multivariate normal distributions Exercise 3: Identify the distribution of a normal sample Exercise 4: Density of a multivariate normal distribution Exercise 5: Calculating the density of multivariate normal Exercise 6: Calculating dmvnorm over a grid Exercise 7: Cumulative Distribution and Inverse CDF Exercise 8: Using the pmvnorm function Exercise 9: Calculating probability contours using qmvnorm Exercise 10: Checking normality of multivariate data Exercise 11: Graphical tests for multivariate normality Exercise 12: Numerical tests for multivariate normality Exercise 13: Test multivariate normality by wine type

This chapter introduces a host of probability distributions to model non-normal data. In particular, you will be introduced to multivariate t-distributions, which can model heavier tails and are a generalization of the univariate Student's t-distribution. You will be introduced to various skew distributions, which are specifically designed to model data that are right or left skewed.

Exercise 1: Other common Multivariate distributions Exercise 2: Generate samples from multivariate t-distribution Exercise 3: Identify the distribution Exercise 4: Density and cumulative density for multivariate-t Exercise 5: Density of multivariate t-distribution Exercise 6: Cumulative distributions and quantiles of t Exercise 7: Comparing normal and t probabilities Exercise 8: Multivariate skewed distributions Exercise 9: Drawing samples from skew distributions Exercise 10: Plotting and testing of skewed-densities Exercise 11: Examine skewness from contour plot Exercise 12: Parameter estimation for multivariate skew-normals

In the final chapter, you will be introduced to techniques for analyzing high dimensional data, including principal component analysis (PCA) and multidimensional scaling (MDS). You will also learn to implement these techniques by analyzing data.

Exercise 1: Principal Component Analysis Exercise 2: Number of PCs for the state.x77 dataset Exercise 3: Using the princomp function Exercise 4: Choosing the number of components Exercise 5: Calculating the proportion of variation explained Exercise 6: Choosing the number of PCs Exercise 7: Number of components explaining 95% variation Exercise 8: Number of PCs using scree plot Exercise 9: Interpreting PCA attributes Exercise 10: Loadings and scores for the PCs Exercise 11: Visualizing PCA using the factoextra library Exercise 12: Multi-dimensional scaling Exercise 13: Multidimensional scaling in two dimensions Exercise 14: Multidimensional scaling in three dimensions Exercise 15: Congratulations