Brainscape's Knowledge GenomeTM

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


See full index

YEAR 2 Stats > Regression 2.0 > Flashcards

Regression 2.0 Flashcards

1
Q

The proportion of variance explained by the model (predictor variables combined) is known as…?

A

R^2

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

What does R^2 measure?

A

The proportion of variance explained by the model (predictor variables combined)

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

Variance explained by x1 expressed as a proportion of the total variance in y

This is known as…?

A

Zero-order^2

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

What is zero-order^2?

A

Variance explained by x1 expressed as a proportion of the total variance in y

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

Unique variance explained by x1, expressed as a proportion of the total variance in y

This is known as…?

A

Part^2

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

What is part^2?

A

Unique variance explained by x1, expressed as a proportion of the total variance in y

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

Unique variance explained by xi, expressed as a proportion of the variance in y that remains after the variance explained by other predictors has been removed

This is known as…?

A

Partial^2

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

What is partial^2?

A

Unique variance explained by xi, expressed as a proportion of the variance in y that remains after the variance explained by other predictors has been removed

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

How do we report zero-order, partial and part as a %?

A
  1. Square the correlations to determine the proportion of variance
  2. Multiply by 100 to express in percentage terms
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Calculate the zero-order, partial and part as a % for age and naughty list ratings based on these SPSS outputs

Age

Zero-order correlations = .573
Partial correlations = .523
Part correlations = .473

Naughty list rating

Zero-order correlations = -.430
Partial correlations = -.344
Part correlations = -.282

A

Age

Zero-order % = .573^2 * 100 = 32.8
Partial % = .523^2 * 100 = 27.4
Part % = .473^2 * 100 = 22.4

Naughty list rating

Zero-order % = -.430^2 * 100 = 18.5
Partial % = -.344^2 * 100 = 11.8
Part % = -.282^2 * 100 = 8.0

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

What is the formula for the variance explained by both x1 and x2 (i.e. shared variance), expressed as a proportion of the total variance in y?

A

Zero-order correlation^2 - part correlation^2

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

What is the formula for unexplained variance?

A

1-R^2

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

Variance explained by each predictor is measured using…?

A

Zero-order^2

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

Unique variance explained by each predictor is measured using…?

A

Part^2

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

Explained variance shared by multiple predictors is measured using…?

A

Variance overlap between each predictor

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

Variance explained all predictors combined (their unique and their shared variance), expressed as a proportion of the total variance in y

This is known as…?

A

Total Explained Variance

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

What is the formula for partial correlation^2?

A

Partial correlation^2 = unique variance / (unexplained variance + unique variance)

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

Calculate the partial correlation^2 based on these results:

Age

Zero-order % = .573^2 * 100 = 32.8
Part % = .473^2 * 100 = 22.4

Naughty list rating

Zero-order % = -.430^2 * 100 = 18.5
Part % = -.282^2 * 100 = 8.0

A

Age

Partial correlation^2 = unique variance / (unexplained variance + unique variance)

22.4 / ((1- (22.4 + (32.8 - 22.4) + 8.0)) + 22.4
= 27.4

Naughty list rating

8.0 / ((1- (22.4 + (32.8 - 22.4) + 8.0)) + 8.0
= 11.8

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

What does the zero-order correlation^2 of age tell us about the total variance in Christmas Joy?

Age

Zero-order % = .573^2 * 100 = 32.8
Partial % = .523^2 * 100 = 27.4
Part % = .473^2 * 100 = 22.4

A

Age can explain 33% of the total variance in Christmas joy

Same result as a simple regression that only includes age as a predictor

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

What does the part correlation^2 of age tell us about the total variance in Christmas Joy?

Age

Zero-order % = .573^2 * 100 = 32.8
Partial % = .523^2 * 100 = 27.4
Part % = .473^2 * 100 = 22.4

A

Age can explain 22% of the total variance in Christmas joy uniquely

i.e. variance explained by age when we don’t include variance also explained by other variables

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

What does the partial correlation^2 of age tell us about the total variance in Christmas Joy?

Age

Zero-order % = .573^2 * 100 = 32.8
Partial % = .523^2 * 100 = 27.4
Part % = .473^2 * 100 = 22.4

A

Age can explain 27% of the remaining variance in Christmas joy, after removing variance explained by other predictors

i.e. variance uniquely explained by age, expressed as a proportion of the variance that remains after the variance explained by other predictors has been removed

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

What does the zero-order correlation^2 of naughty list ratings tell us about the total variance in Christmas Joy?

Naughty list rating

Zero-order % = -.430^2 * 100 = 18.5
Partial % = -.344^2 * 100 = 11.8
Part % = -.282^2 * 100 = 8.0

A

Naughty list ratings can explain 19% of the total variance in Christmas joy

Same result as a simple regression that only includes naughty list ratings as a predictor

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

What does the part correlation^2 of naughty list ratings tell us about the total variance in Christmas Joy?

Naughty list rating

Zero-order % = -.430^2 * 100 = 18.5
Partial % = -.344^2 * 100 = 11.8
Part % = -.282^2 * 100 = 8.0

A

Naughty list ratings can explain 8% of the total variance in Christmas joy uniquely

i.e. variance explained by naughty list ratings when we don’t include variance also explained by other variables

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
24
Q

What does the partial correlation^2 of naughty list ratings tell us about the total variance in Christmas Joy?

Naughty list rating

Zero-order % = -.430^2 * 100 = 18.5
Partial % = -.344^2 * 100 = 11.8
Part % = -.282^2 * 100 = 8.0

A

Naughty list ratings can explain 12% of the remaining variance in Christmas joy, after removing variance explained by other predictors

i.e. variance uniquely explained by naughty list ratings, expressed as a proportion of the variance that remains after the variance explained by other predictors has been removed

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
25
Q

Calculate the zero-order, partial and part as a % for Mary, Jane and Paul based on these SPSS outputs

There are no unexplained variances in this model

Mary
Unique variance = 0.6
Shared variance with Jane = 0.2
Shared variance with Paul = 0.04

Jane
Unique variance = 0.1
Shared variance with Mary = 0.2
Shared variance with Paul = N/A

Paul
Unique variance = 0.06
Shared variance with Mary = 0.04
Shared variance with Jane = N/A

A

Mary
Zero-order correlations = 84%
0.6 + 0.04 + 0.2 = 0.84

Partial correlations = 100%
0.6 / 0 + 0.6 = 1.0

Part correlations = 60%
0.6 = .600

Jane
Zero-order correlations = 30%
0.1 + 0.2 = 0.3

Partial correlations = 100%
0.1 / 0 + 0.1 = 1.0

Part correlations = 10%
0.1 = .100

Paul
Zero-order correlations = 10%
0.06 + 0.04 = 0.1

Partial correlations = 100%
0.06 / 0 + 0.06 = 1.0

Part correlations = 6%
0.06 = .060

26
Q

What is ‘standard’ multiple regression?

A

All predictor variables are entered at the same time

27
Q

All predictor variables are entered at the same time

a. ‘Standard’ multiple regression
b. Hierarchical regression

A

a. ‘Standard’ multiple regression

28
Q

What is hierarchical regression?

A

Predictor variables are entered in a specified
order of ‘steps’, based on theoretical grounds

29
Q

Predictor variables are entered in a specified
order of ‘steps’, based on theoretical grounds

a. ‘Standard’ multiple regression
b. Hierarchical regression

A

b. Hierarchical regression

30
Q

Obtain a measure of the overall variance explained (R^2)

Obtain measures of the influence of each separate predictor (coefficients)

The most common form of regression

a. ‘Standard’ multiple regression
b. Hierarchical regression

A

a. ‘Standard’ multiple regression

31
Q

What is the most common form of regression?

A

‘Standard’ multiple regression

32
Q

The relative contribution of each ‘step’ (set of predictor variables) can be evaluated in terms of what it adds to the prediction of the outcome variable

(i.e. the additional variance it explains)

a. ‘Standard’ multiple regression
b. Hierarchical regression

A

b. Hierarchical regression

33
Q

Why do we use hierarchical regression?

A

To examine the influence of predictor variables(s) on an outcome variable, after ‘controlling for’ (i.e. partialling out) the influence of other variables

34
Q

To examine the influence of predictor variables(s) on an outcome variable, after ‘controlling for’ (i.e. partialling out) the influence of other variables

What do we use?

A

Hierarchical Regression

35
Q

You are interested in how well optimism predicts life satisfaction, but you want to investigate the predictive power of optimism after controlling for age-associated variation in life satisfaction

What do we use?

a. Hierarchical regression
b. ‘Standard’ multiple regression

A

a. Hierarchical regression

36
Q

Previous research has established that self-efficacy and motivation are important predictors of learning performance; you want to know if enjoyment of materials and peer support (previously unexplored variables) can explain additional variance
in learning performance

What do we use?

a. Hierarchical regression
b. ‘Standard’ multiple regression

A

a. Hierarchical regression

37
Q

You are interested in how well optimism predicts life satisfaction, but you want to investigate the predictive power of optimism after controlling for age-associated variation in life satisfaction

What are Step 1 and Step 2 of our hierarchical regression?

A

Step 1 = age
Step 2 = optimism

38
Q

Previous research has established that self-efficacy and motivation are important predictors of learning performance; you want to know if enjoyment of materials and peer support (previously unexplored variables) can explain additional variance
in learning performance

What are Step 1 and Step 2 of our hierarchical regression?

A

Step 1 = self-efficacy and motivation
Step 2 = peer support and enjoyment

39
Q

On SPSS, what does ‘a. Predictors: (Constant), naughty list rating (1 = low; 10 = high)’ mean in the ‘Model Summary’ table?

A

Regression results for Model 1 (Step 1), which only
included naughty list rating as a predictor variable

40
Q

On SPSS, what does ‘b. Predictors: (Constant), naughty list rating (1 = low; 10 = high), age’ mean in the ‘Model Summary’ table?

A

Regression results for Model 2 (Step 2), which included naughty list rating AND age as predictor variables

41
Q

Proportion of variance explained by the model
(predictor variables combined, if >1)

This is referred to as…?

A

R^2

42
Q

What does R^2 measure?

A

The proportion of variance explained by the model
(predictor variables combined, if >1)

43
Q

Adjusted to account for the degrees of freedom (a better estimate of the variance explained
in the population)

This is known as…?

A

Adjusted R^2

44
Q

What is adjusted R^2?

A

Adjusted to account for the degrees of freedom (a better estimate of the variance explained
in the population)

45
Q

On SPSS, what does ‘b. Predictors: (Constant), naughty list rating (1 = low; 10 = high)’ mean in the ‘ANOVA’ table?

A

ANOVA results for Model 1 (Step 1), which only
included naughty list rating as a predictor variable

46
Q

On SPSS, what does ‘b. Predictors: (Constant), naughty list rating (1 = low; 10 = high), age’ mean in the ‘ANOVA’ table?

A

ANOVA results for Model 2 (Step 2), which included
naughty list rating AND age as predictor variables

47
Q

What do the ANOVA results for Model 2 (Step 2), which included naughty list rating AND age as predictor variables assess?

A

Assesses whether the overall regression model (with
all predictors included at that step) accounts for
significantly more variance than the simplest model
(b = 0)

48
Q

Assesses whether the overall regression model (with
all predictors included at that step) accounts for
significantly more variance than the simplest model
(b = 0)

a. t-tests
b. correlation
c. regression
d. ANOVA

A

d. ANOVA

49
Q

What do the ANOVA results for Model 1 (Step 1), which only included naughty list rating as a predictor variable assess?

A

Assesses whether the overall regression model (with
all predictors included at that step) accounts for
significantly more variance than the simplest model
(b = 0)

50
Q

The change in R^2 from b = 0 to Model 1 or 2 is known as…?

A

R Square Change

51
Q

What does R Square Change measure?

A

The change in R^2 from b = 0 to Model 1 or 2

52
Q

R Square Change is the same as ___ for Model 1

A

R^2

53
Q

Provides a measure of how much the model has improved the prediction of y, relative to the level of inaccuracy of the model

This is known as…?

A

F change

54
Q

What does F change measure?

A

Provides a measure of how much the model has improved the prediction of y, relative to the level of inaccuracy of the model

55
Q

F Change is the same as ___ for Model 1

A

F

56
Q

Predictors: (Constant), naughty list rating (1 = low; 10 = high) on the Model Summary table on SPSS compares…?

a. The simplest model (b = 0) with Model 1 (which included just naughty list rating)

b. Model 1 (which included just naughty list rating) and Model 2 (which includes naughty list rating AND age)

A

a. The simplest model (b = 0) with Model 1 (which included just naughty list rating)

57
Q

Predictors: (Constant), naughty list rating (1 = low; 10 = high), age on the Model Summary table on SPSS compares…?

a. The simplest model (b = 0) with Model 1 (which included just naughty list rating)

b. Model 1 (which included just naughty list rating) and Model 2 (which includes naughty list rating AND age)

A

b. Model 1 (which included just naughty list rating) and Model 2 (which includes naughty list rating AND age)

Tells us about the explanatory power of age, after the effects of naughty list rating are controlled for

58
Q

How much overall variance in the DV is explained by predictor 2 after the effects of predictor 1 are controlled for

How do we measure this?

A

Refer to R Square Change

59
Q

Provides a measure of how much the model has improved the prediction of y, relative to the level of inaccuracy of the model, after the predictive power of Step 1 variables have been partialled out

This is known as…?

A

F change

60
Q

What does F change measure?

A

How much the model has improved the prediction of y, relative to the level of inaccuracy of the model, after the predictive power of Step 1 variables have been partialled out

YEAR 2 Stats (10 decks)

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