Correlation and linear regression

Question 1

What is correlation used for?

Answer 1

to test for association between variables

Question 2

What is meant by regression?

Answer 2

Once correlation between two variables has been shown regression can be used to predict values of other dependent variables from independent variables.

Question 3

The degree of correlation is summarised by what

Answer 3

the correlation coefficient (r)
(indicates how closely the points lie to a line drawn through the plotted data)

Question 4

Describe the correlation between 2 variables if r=1, r=0 and r=-1

Answer 4

r = 1 - strong positive correlation (e.g. systolic blood pressure always increases with age)

r = 0 - no correlation

r = - 1 - strong negative correlation (e.g. systolic blood pressure always decreases with age)

Question 5

What is the name given to the correlation coefficient when using parametric variables vs the name given when analysing non-parametric variables

Answer 5

parametric variables = Pearson’s correlation coefficient (r)

non parametric variables = Spearman’s correlation coefficient (p)

Question 6

what is linear regression?

Answer 6

linear regression may be used to predict how much one variable changes when a second variable is changed

Question 7

A linear regression equation may appear as follows:
y = a + bx
What do each of the above represent?

Answer 7

y = the variable being calculated
a = the intercept value, when x = 0
b = the slope of the line or regression coefficient. (how much y changes for a given change in x)
x = the second variable