1. Regression Project
By Lauren Whitsell

2. Scatter Plot This scatter plot shows data for the US Annual Wages.
The equation generates this line, which was an r value of .96. That means the line is extremely close to the data, which means the data rises in a linear fashion.

3. Equation
A negative y-intercept means that the slope used to be more shallow, indicating that the rise of wages is greater than it has been in past years, because people cannot earn negative wages. The wages must have been a shallower line, growing slowly steeper in earlier years, and increasing faster approaching the modern era.

4. Predictions
Using the equation: , I predicted these points on the scatterplot: X Y
1988.5
1989.5
1991.5

5. Conclusion The data rises in a linear fashion based on the equation.
The r-value reveals a predictable data set.
The slope reveals data that slopped differently at a different point in time.
The scatter plot reveals data without irregularities.
Therefore, wages in the US rise predictably. Economists and government officials can use this data to predict the how people will spend money.