From PALS Flashcards

1
Q

What is | in R

A

‘OR’

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2
Q

Three types of R variables

A

Numeric
Characters (also called ‘strings”
Logical (eg true/false)

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3
Q

A variable with more than one variable

A

Vector

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4
Q

Character you use to select variables out of a dataframe

A

$

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5
Q

Types of scores - untouched metric

A

= raw/observed

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6
Q

Scaled to the metric mean

A

= Deviation scores (always sums to zero)

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7
Q

Scaled to a specific mean, with specified amount of st dv per unit

A

= standardised score

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8
Q

Mean is set to 0 and 1 standard deviation of 1 per unit

A

= Z score

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9
Q

What are random variables

A

perpetually unknowable variables pertaining to the real world

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10
Q

Two types of variable

A

Continuous (decimal points make sense)

Discrete (or factor) (ps categorical variable = discrete variables

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11
Q

Standard Dev of a sampling distribution

A

= Standard error

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12
Q

Examples of observed test statistic

A

t-statistic, chi-squared, F-statistic

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13
Q

Theoretical probability distribution

A

gives you the equivalent of a sampling distribution without yo having to construct it

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14
Q

Are CIs a function of p values

A

Yep

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15
Q

What defines the CI

A

standard error + alpha criterion value

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16
Q

Which error types do alpha criterions control

A

Type 1 (false positives)

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17
Q

Effect size..

A

= strength of relationship

18
Q

Pearson’s r is…

A

an effect size measure

19
Q

Cramer’s V is…

A

an effect size measure

20
Q

R squared is…

A

an effect size measure

21
Q

Measuring systematic co-occurrance is done with..

A

association

22
Q

Correlation coefficient is a standardised…

A

covariance (essentially covariance of Z-scores)

23
Q

Range for Pearson’s r

24
Q

What is r in correlation

A

sample coefficient

25
What is rho in correlation
population parameter
26
When do you use Cramer's V
association between categorical variables
27
Cramer's V range
0-1?
28
What is the value of a 0% CI
The sample statistic itself
29
What does simple regression mean
1 x DV and 1 x IV
30
How you calculate St Dev
Sort of variance
31
Variance
Average variation
32
Variation
Total variation...
33
Covariance is standardised?
False
34
Linear regression model equation
Y(hat) = a + bX + e
35
What is OLS in regression
Ordinary Least Squares (smallest sum of squares)
36
R-squared
proportion of variance explained by the model
37
Under what conditions do you need to use an adjusted R squared
Small sample size | Large number of IVs
38
Assumptions for regression (4)
1. Independence of observations 2. Linearity 3. Homoscedasticity 4. Normality of residuals
39
How do you test Homoscedasticity
Breusch-Pagan (want a big p value)
40
What is homoscedasticity anyway
residuals distributed equally above and below zero
41
How can you tell if you're looking at standardised multiple regression analysis results
there is no intercept reported (or it is 0)