Do I know you? Pt. 1 Flashcards

1
Q

ANOVA

A

Parametric

test whether the values across multiple groups are different

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Pearson’s R

A

Parametric

examine how 2 things are related

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

Spearman’s Rho

A

non-parametric

examine how 2 things are related

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Two tailed t test

A

parametric

compare the values between 2 groups

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Mann Whitney U

A

non-parametric

compare the values between 2 groups

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Kruskall Wallis

A

non-parametric

test whether the values across multiple groups are different

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Kernel density (3 facts)

A
  1. removes statistical noise from data by smoothing it
  2. uses gaussian weighting (close = more weight)
  3. good for showing generalized densities of points
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Normal gaussian distribution 3 facts

A
  1. follows a bell curve
  2. uses parametric stats
  3. defined by mean and standard deviation
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

null hypothesis

A

no relationship/association/effect in the population

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

square of the error

A

explaining difference between observed and expected values

assess accuracy of model

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

non-stationarity

A

looking into local variation in geographically weighted regression

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

cross-k function

A
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

correlogram

A

shows semivariance and covariance on 1 graph

shows correlation at different lags

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

cross plot

A

scatterplot of all semivariance values at different lags (doesn’t show the variance line)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

k nearest neighbors

A

selects the closest n neighbors

good because it counts the same number of features each time

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

kriging 3 facts

A
  1. gives you an estimate of the errors
  2. models spatial variation
  3. weights are based on distance between points and their overall spatial arrangement
17
Q

2 stages of kriging

A
  1. estimate the structure of the data along with autocorrelation to pick the best model
  2. interpolate
18
Q

deletes all objects in the current work space in R

A

rm(list=ls())