Relationship between variables

independent variable

predictor

Explanatory variable

Dependent Variable

Outcome, result

Response variable

Bivariate Relationships

two variables

Categorical x categorical

Bar chart

Categorical x quantitative

Box plot

Quan x Quan

Scatterplot with trend line

x- axis

IV

y-axis

DV

Scatterplot Direction

1. Positive

2. Negative

3. Neither

Upper left- lower right

negative

Lower left-upper right

Positive

Strength of association

How much does it cluster?

The degree of clustering can be measured using

Correlation Coefficients

Linearity

Straight line relationship: A cloud of points stretched out in a generally consistent straight form.

Correlation

The pattern in the scatter plot looks straight, and the positive association is clear, but how strong is that association?

What does correlation do?

Measures and describes the strength and direction of a relationship between 2 quantitative variables.

Pearsons Correlation ®

• Measures the direction and strength of linear relationship between two quantitative variables.

• How much do data points move together on a linear line.

Direction

1. Positive correlation

2. Negative correlation

Positive correlation

When 2 variables tend to move in the same direction.

Negative correlation

When two variables tend to move in opposite directions.

Strength

+1= perfect positive correlation

0= no correlation

-1= perfect negative correlation

Weak correlation

loosely scattered (near 0)

Moderate Correlation

a clear trend exist, noticeable variation.

Strong correlation

Closely aligned (near +1 or -1).

Slope

Correlation coefficient only tells you how closely your data fits on a line.

Confounding variable

A hidden factor that affects both the (IV) and (DV), creating a false impression of causality.

Association

any relationship between two variables.