Flashcards in Statistics IX - Random Stuff Deck (13):

1

## How to calculate the geometric mean?

### multiply all values, then n-root it!

2

## 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.

3

## You want to sort your cider collection along meaningful variables or connections of variables. What model will you use?

### PCA

4

## How many PCs can there be at maximum in your data?

### Not more then original variables.

5

## How many H0 hypotheses are there in a two-way ANOVA?

###
H0(A)

H0(B)

H0(AB)

6

## The measurement error in repeated measures can be either ...

###
a random error or

a systematic error.

7

## Examples for a nominal scale:

### gender, nationality, group-names

8

## Examples for ordinal scale:

### rank order, Likert scale

9

## Examples for interval scale:

###
no natural zero:

degree Celsius

10

## Examples for ratio scale:

###
zero means there is nothing of it!

degree Kelvin

body height

counts

11

## 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?

12

## 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.

13