Linear Regression

1. When one event effects the outcome of another. Correlation between two variables is not enough evidence to prove causation, there could be additional variables or unknown information to consider
2. When the r variable is between 1 and 0.5 or -1 and -0.5
4. Represents the y-value when the x-value is 0
5. A line that can effectively portray the trend of a scatterplot
6. When two events appear to be connected but are not caused by each other
10. Trend of the line is a positive increase
12. Trend of the line is a negative decrease
14. When the r variable is between 0.5 and 0 or -0.5 and 0
15. Using a model to make a prediction about a value outside the range of known values

1. coefficient Indicates how closely the line of fit models the data. It is represented by the variable r, and is between -1 and 1
3. Being able to observe a graph and draw a line that represents the trend
7. Randomly scattered plots with zero trend
8. Models the data in a scatterplot
9. Using a model to estimate values within the range of known values
11. A graph of data points in two variables
13. Trend of the line that represents the change in y for every 1 change in x