Correlation: How the independent variable and dependent variable relate to one another.
Positive: As x increases, y increases, and as x decreases, y decreases.
Negative: As x increases, y decreases, and as x decreases, y increases.
No Correlation: x and y do not relate to one another.
Causation: When the independent variable has a direct impact on the dependent variable. ***Remember - Correlation (the fact the x and y values relate) does not imply Causation (the fact that one caused the other).
Scatter Plot: A type of graph that uses dots to represent bivariate data.
Line of Best Fit: (Linear Regression/Trend Line) A line on a graph that shows the general direction that the data on a scatter plot is going.
Correlation Coefficient: The numerical value to how strongly the independent variable (x-value) and dependent variable (y-value) are related. The closer the value is to +1 or -1, the stronger the correlation.
Positve 1 - Perfect positive correlation
O - no correlation at all
Negative 1 - Perfect negative correlation
Midpoint: The middle of a line segment.
Formula to the right
Endpoint: The end of a line segment. Each line segment has two.