Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

P202 stats > final exam > Flashcards

final exam Flashcards

1
Q

correlation (r)

A

reflects the strength and direction of a relation between two continuous variables

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

When are correlations stronger?

A

They can be from 1 to -1, correlations closer to 1 are stronger than correlations closer to 0

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

What do negative correlations tell us?

A

the variables have a different relation between them than positive correlations

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

Examples of correlations

A

strong - r= .80
weak- r=. 10
positive- r= .80, .10
negative- r= -.80, -.10

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

Correlation does not mean what?

A

Causation

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

regression coefficient (b)

A

reflects how well one of those continuous variables predicts the other

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

predict

A

means we can see what happens with one variable and predict what will happen with the other

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Regression equation

A

y= a + b (x)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

What do the variables mean in regression equation?

A

x= predictor variable
y= criterion variable (score we are predicting)
b= regression coefficient
a= regression constant (where we start)

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

b in the formula means

A

for every 1 raw unit increase in x their is a b unit increase in y

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

a in the formula means

A

the predicted value of y when x equals zero

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

conceptual interpretation

A

For every one raw unit increase in [x -> hours slept last night] there is a [b -> 1] unit increase in [y -> happy mood]

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

Substantive interpretation

A

For every additional hour of sleep people are predicted to be one point happier

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

multiple regression

A

more than one predictor
Does income and sleep predict happiness?

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

simple regression

A

one predictor
Does income predict happiness?

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

multiple regression equation

A

y= a + b(1)x(1) + b(2)x(2) and so on depending on how many predictors

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

b 1.2

A

partial regression coefficient for X1

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

b 2.1

A

partial regression coefficient for X2

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

partial out

A

to remove shared credit from other predictions

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

conceptual interpretation for partial regression

A

for every raw unit increase in X there is a b 1.2 unit increase in Y partialing out the other predictions.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

substantive interpretation for partial regression

A

for every one raw unit increase in income their is a 0.5 unit increase in happiness partialing out sleep.

22
Q

R is tested with what?

A

F test

23
Q

b is tested with?

A

t test

24
Q

R squared reflects what?

A

the proportion of variation in Y examined by our set of predictors

25
Q

what does b show us?

A

strength and direction of prediction

26
Q

hierarchical regression

A

sets of predictors

Set A -> R squared
age
education
gender

Set B -> triangle r squared
money
sleep
stress

27
Q

logistic regression

A

allows to model each value in your categorical outcome
probability of guilty verdict = .8
probability of not guilty verdict = .2
odds ratio of guilty verdict= .8/.2

we add predictors to the model after

28
Q

Effect sizes

A

express magnitude and sometimes direction of effect or estimate, they are standardized

29
Q

d index

A

standardized mean difference, focuses on the standardized difference between two group averages

30
Q

Why are standardized units beneficial

A

because they remove raw units, we use standard deviation to remove raw units

31
Q

SD pooled

A

standard deviation from both participants

32
Q

conceptual interpretation of d

A

there is a (d) standard deviation unit difference between the average (y) for group 1 and group 2

33
Q

substantive interpretation of d

A

there is a 2.5 standard deviation unit difference between the average salaries for men and women in business

34
Q

benchmarking

A

d = .20 -small
d = .50 -medium
d= .80 -large

35
Q

conventional benchmarking is too?

A

general

36
Q

empirical benchmarking

A

compares d-index to other similar studies

37
Q

confidence interval

A

interval or numbers whose length reflects the precision of an estimate

Given a sample from a population, the CI indicates a range in which the population mean is believed to be found. Usually expressed as a 95% CI, indicating the lower and upper boundaries.

M= 200k, 95% CI (150k, 250k)

38
Q

M= 200k, 95% CI (150k, 250k) this starts with a?

A

lower limit of 150 and high limit of 250

39
Q

The width of a confidence interval reflects?

A

how precise or accurate our estimate is
TOO WIDE IS LESS PRECISE

40
Q

misinterpretation of confidence intervals

A
  1. 95% confidence interval has a 95% chance of containing the population parameter of interest
  2. a 95% confidence interval predicts that 95% of sample estimates from future studies will fail within it (no)
41
Q

data cleaning

A

preparing data involves cleaning it which ensures the “dataset” on which you conduct analyses is complete, correct and consistent

42
Q

complete

A

has all the data been recorded or transferred into data set

43
Q

data entry

A

recording all data in the form of scores from participants in your study

44
Q

transcription error

A

researchers entered data incorrectly

45
Q

reporting error

A

participants enter data incorrectly

46
Q

downloading error

A

sometimes data isn’t downloading correctly

47
Q

cleaning data

A

ensures its integrity and trustworthy

48
Q

assumptions

A

the things we hold true for an inferential test to operate the way we think it should

49
Q

inferential tests

A

they only work right if our assumptions are true, they can handle moderate or minor departures

50
Q

when statistical assumptions are violated the probability of a test statistic may be

A

inaccurate

51
Q

normality

A

outcome variable scores are normally distributed in the population

52
Q

homogeneity of variance

A

all groups have the same variance in the population (focuses on width not shape)

P202 stats (3 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions