Correlation & Linear regression Flashcards

1
Q

Correlation definition

A

the strength of association between two variables

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

How can correlation be summarised?

A

Graphically - scatterplots

Numerically - correlation coefficients

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

What is a correlation coefficient (r)

A

numbers that quantify the strength of association between two variables

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

what do different ‘r’ values show

A

r > 0 - variables increase together

r < 0 - one up, one down

r = 0 - no association

r = 1/-1 - perfect association

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

When is Pearson correlation coefficient used?

A

for linear association

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

When is spearman correlation coefficient used?

A

for non-linear association

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

What does monotonic mean?

A

either a scatterplot relationship is never positive or never negative

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

When can neither Pearson or Spearman be used?

A

when the relationship is non-monotonic

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

What is coefficient of determination? (R squared)

A

the proportion of the variation in one variable that is explained by another variable

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

How is R squared calculated?

A

by multiplying Pearson correlation coefficient by itself

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

What do R squared values show?

A

0 = no variation is explained

1 = all variation is explained

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

What is linear regression used to do?

A

estimate a mathematical equation that describes the linear relationship between a quantitative outcome and a quantitative predictor

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

Which axis is the outcome plotted on?

A

y axis (vertical)

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

Which axis is the predictor plotted on?

A

x axis (horizontal)

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

What is the linear regression equation?

A

Outcome = a + b x predictor

a = mean expected outcome when predictor is 0

b = slope (outcome increase due to predictor variation)

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

What are residuals

A

errors from the predictor line - actual values difference to predicted value

17
Q

What are the 4 basic assumptions when using

A
  1. outcome is quantitative
  2. linear relationship (scatterplot)
  3. residuals are normally distributed (histogram)
  4. constant variance