Flashcards in Statistics IX - Random Stuff Deck (13):
How to calculate the geometric mean?
multiply all values, then n-root it!
What does wikipedia say about PCA?
PCA is the simplest of the true eigenvector-based multivariate analyses. Often, its operation can be thought of as revealing the internal structure of the data in a way that best explains the variance in the data. If a multivariate dataset is visualised as a set of coordinates in a high-dimensional data space (1 axis per variable), PCA can supply the user with a lower-dimensional picture, a "shadow" of this object when viewed from its (in some sense; see below) most informative viewpoint. This is done by using only the first few principal components so that the dimensionality of the transformed data is reduced.
You want to sort your cider collection along meaningful variables or connections of variables. What model will you use?
How many PCs can there be at maximum in your data?
Not more then original variables.
How many H0 hypotheses are there in a two-way ANOVA?
The measurement error in repeated measures can be either ...
a random error or
a systematic error.
Examples for a nominal scale:
gender, nationality, group-names
Examples for ordinal scale:
rank order, Likert scale
Examples for interval scale:
no natural zero:
Examples for ratio scale:
zero means there is nothing of it!
Describe logistic regression!
Like multiple regression, but the DV is a dichotomous variable. Logistic regression estimates the probability of the DV occurring as the value of the IV changes.
E.g: What are the odds of a suicide occurring at various levels of alcohol use?
Define the coefficient of variation!
In probability theory and statistics, the coefficient of variation (CV) is a normalized measure of dispersion of a probability distribution or frequency distribution.