# Z Scores & Correlation Z Scores An Example

Preparing to load PDF file. please wait...

0 of 0
100%

## Transcript Of Z Scores & Correlation Z Scores An Example

Z Scores & Correlation
Greg C Elvers
1
Z Scores
A z score is a way of standardizing the scale of two distributions When the scales have been standardize, it is easier to compare scores on one distribution to scores on the other distribution
2
An Example
You scored 80 on exam 1 and 75 on exam 2. On which exam did you do better? The answer may or may not be that you did better on exam 2 In order to decide on which exam you did better, you must also know the mean and standard deviation of the exams
3
An Example
The mean and standard deviation of Exam 1 were 85 and 5, respectively The mean and standard deviation of Exam 2 were 70 and 5, respectively So, you scored below the mean on exam 1 and above the mean on exam 2 On which exam did you do better?
4

_______________________________________ _______________________________________ _______________________________________ _______________________________________
_______________________________________ _______________________________________ _______________________________________ _______________________________________
_______________________________________ _______________________________________ _______________________________________ _______________________________________
_______________________________________ _______________________________________ _______________________________________ _______________________________________

Z Scores

A z score is defined as the deviate score (the observed score minus the mean) divided by the standard deviation
It tells us how far a score is from the mean in units of the standard deviation

z = (X - X) s 5

An Example

You have a z score of -1 on the first exam
Your score was one standard deviation below the mean on exam 1
You have a z score of 1 on the second exam
Your score was one standard deviation above the mean on exam 2
You did better on exam 2

( ) z = X - X = (80 − 85) = −1

s

5

( ) z = X - X = (75 − 70) = 1

s

5

6

Important Properties of Z Scores

The mean of a distribution of z scores is always 0
The standard deviation of a distribution of z scores is always 1
The sum of the squared z scores always equals N

µZ = 0 σz =1
åz2 = N 7

µz = 0

å çèæ Xσ− µ ÷øö
N =0

1 σ

å

(

X

µ

)

N =0

1 σ

X

å

µ

)

N

=0

1 σ

X

N

µ

)

N

=0

1 σ

çæ ç

å

X

N

åX
N

÷ö ÷

è N ø=0

1 σ

X

å

X

)

N

=0

10 σN = 0

0=0

Proofs

åz2 = N

å çæ

X

µ

2
÷ö

=

N

èsø

å (X s−2µ)2 = N

1 s2

å

(X

µ )2

=

N

1
å (X − µ)2

å (X

− µ)2

=

N

N

å

N
(X −

µ )2

å

(X

µ )2

=

N

N=N

σz =1

σ2z = 1

å σ2z = (zN− µz )2 = 1

µz = 0

å σ2z =

z2 N =1

åz2 = N

σ2z = NN = 1 8

_______________________________________ _______________________________________ _______________________________________ _______________________________________
_______________________________________ _______________________________________ _______________________________________ _______________________________________
_______________________________________ _______________________________________ _______________________________________ _______________________________________
_______________________________________ _______________________________________ _______________________________________ _______________________________________

Z scores and Pearson’s r
Pearson’s r is defined as:
r = åzx zy N

9

What the Formula Means
The z scores in the formula simply standardize the unit of measure in both distributions The product of the z scores is maximized when the largest zx is paired with the largest zy

10

r = 1

Because of the unit standardization, when there is a perfect correlation zx = zy Then zxzy = zx2 = zy2

r

=

å

z

2 x

=

N

=1

NN

11

r = 0
When r = 0, large zx can be paired with large or small zy Furthermore, positive zx can be paired with either positive or negative zy The sum of zxzy will tend to 0 Thus, r will tend to 0
12

_______________________________________ _______________________________________ _______________________________________ _______________________________________
_______________________________________ _______________________________________ _______________________________________ _______________________________________
_______________________________________ _______________________________________ _______________________________________ _______________________________________
_______________________________________ _______________________________________ _______________________________________ _______________________________________

Computational Formula for r

( ) ( ) r = å XY - (å XN)(åY)

é
êåX2 −

åX

2ùé
úêå Y2 −

åY

2ù ú

ê

N úê

ë

ûë

û

13

Coefficient of Determination
The coefficient of determination is the proportion of variance in one variable that is explainable by variation in the other variable It tells us how well we can predict the value of one variable given the value of another

14

Coefficient of Determination
When there is a perfect correlation between two variables, then all the variation in one variable can be explained by variation in the other variable Thus the coefficient of determination must equal 1

15

Coefficient of Determination
When there is no relation between two variables, then none of the variation in one variable can be explained by variation in the other variable Thus the coefficient of determination must equal 0

16

_______________________________________ _______________________________________ _______________________________________ _______________________________________
_______________________________________ _______________________________________ _______________________________________ _______________________________________
_______________________________________ _______________________________________ _______________________________________ _______________________________________
_______________________________________ _______________________________________ _______________________________________ _______________________________________

Coefficient of Determination

The coefficient of determination is defined as r2
When r = 1 or r = -1, r2 = 1, as it should be
When r = 0, r2 = 0, as it should be

r*r

1.0

0.5

0.0 -1.0 -0.5 0.0 r

0.5 1.0
17

Coefficient of Nondetermination
The coefficient of nondetermination is the amount of variation in one variable that is not explainable by the variation in the other variable The coefficient of nondetermination equals (1 - r2)

18

Correlation and Causation

Correlation does not show causation

Just because two variables are correlated (even perfectly correlated) does not imply that changes in one variable cause the changes in the other variable

E.g., even if drinking and GPA are correlated,

we do not know if people drink more because

their GPA is low (drink to alleviate stress) or if

drinking causes one’s GPA to be low (less

study time) or neither of these

19

Correlation and Causation
There is always a chance that the variation in both variables is due to the variation in some third variable r = 0.95 for number of storks sighted in Oldenburg Germany and the population of Oldenburg from 1930 to 1936
Storks do not cause babies Babies do not cause storks What is the third variable that causes both? 20

_______________________________________ _______________________________________ _______________________________________ _______________________________________
_______________________________________ _______________________________________ _______________________________________ _______________________________________
_______________________________________ _______________________________________ _______________________________________ _______________________________________
_______________________________________ _______________________________________ _______________________________________ _______________________________________

Special Correlation Coefficients

Scale
Nominal
Ordinal Interval or Ratio

Symbol
rphi (phi coefficient) rb (biserial r)
rt (tetrachoric) rs (Spearman r)
τ (Kendall’s tau) Pearson r Multiple r

Used With
2 dichotomous variables
1 dichotomous variable with underlying continuity; one variable can take on more than 2 values 2 dichotomous variables with underlying continuity Ranked data (both variables at least ordinal) Ranked data
Both variables interval or ratio More than 2 interval or ratio21 scaled variables

_______________________________________ _______________________________________ _______________________________________ _______________________________________
VariationDeterminationDeviationCoefficientVariables