1. Bivariate Data

2. Univariate vs. Bivariate
Involves a single variable
Dealing with describing the variable
Information gathered is about the distribution (range or mean)
Bivariate
Involves two variables
Dealing with causes or relationships
Purpose is to explain
Represented in a scatterplot

3. Scatterplot
A type of diagram using Cartesian coordinates to display values for two variables for a set of data
Data is displayed as a collection of points, each having the value of one variable determining the position on the horizontal axis and the value of the other variable determining the position on the vertical axis
When looking at scatter plots, we will look for direction, form, strength and outliers

4. Direction A lot like slope Positive relationships
If one variable increases the other variable increases
If one variable decrease the other variable decreases
Ex: As the temperature increases the amount of people at the beach will increase. As the temperature decreases the amount of people at the beach decreases.

5. Direction Negative relationships
As one variable increases, the other variable decreases.
Ex: As the amount of time you spend exercising increases, the less time it takes you to run a mile.

6. Direction It is possible to have no association or direction at all.
It occurs when the scatter plot looks like a random splattering of dots.

7. Form
Determines if there is a straight line (linear) relationship or curved
A straight line relationship will appear as a cloud or swarm of points stretched out in a consistent straight form

8. Form
Quadratic
Exponential

9. Form
Logarithmic

10. Strength
Determines how strong a relationship between two variables is.
At one extreme, the points appear to follow a straight stream
At the other extreme, the points appear as a vague cloud with no pattern

11. Outliers Do not follow the pattern
They may be in either the x-direction, y-direction or both directions

12. Variables Predictor variable Response variable
Also called explanatory variable
Variable that can be used to predict the value of another variable (independent variable)
Response variable
Variable that depends on the predictor variable (dependent variable)
Predictor variables are independent of response variables
For example:
The amount of people at the beach depends on the temperature.
Predictor variable: temperature
Response variable: population at the beach