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Plot the data in a coordinate plane. Draw a line that passes near to all the given points and find its equation. To estimate a value, use the equation of the trend line.
Scatter Plot and Example Trend Line:
Example Number of Inventors in 2006: ≈ 14 786
Example Number of Inventors in 2015: ≈ 5444
When analyzing data using a scatter plot, there are three general outcomes for the correlation of the data: positive correlation, negative correlation, or no correlation.
| Observation | Type of Correlation |
|---|---|
| As x increases, y increases. | Positive |
| As x increases, y decreases. | Negative |
| There is no visible pattern. | No |
By treating the table as a set of points, let's graph the years versus the number of inventors applying for patents on a scatter plot. We will restrict our graph to Quadrant I because we cannot have a negative number of people or a negative year. Do you see any trends?
It looks like there is some kind of correlation. Let's draw a line of fit, or trend line, to help us identify the type of correlation. To do so, we will draw a line that appears to fit the data closely.
Notice that if we choose any other pair of the points to draw the line, it will not be as close to all the points as the line above is.
To start, let's find the slope of the trend line using the Slope Formula. m= y_2- y_1/x_2- x_1 In this formula, ( x_1, y_1) and ( x_2, y_2) are two points on the line. From the graph above and the given table, we can see that the trend line passes through the points ( 1999, 22 052) and ( 2007, 13 748). We can substitute them into the formula to find our slope m.
Substitute ( 1999,22 052) & ( 2007,13 748)
Subtract terms
Calculate quotient
Now that we know the slope of the trend line, we can find its equation by using the point-slope form of a line. y- y_1= m(x- x_1) Let's substitute m= - 1038 and ( x_1, y_1)=( 1999, 22 052) into the equation above.
Substitute m= - 1038, x_1= 1999, y_1= 22 052
Distribute - 1038
LHS+22 052=RHS+22 052
We will use interpolation to estimate the number of inventors applying for patents in 2006. Interpolation is used when you are estimating a value that falls within the known data set. To do that, we will replace x=2006 into the equation of the trend line.
x= 2006
Multiply
Subtract terms
Therefore, according to the given data, we can say that in 2006, approximately 14 786 inventors applied for patents.
We will use extrapolation to predict the number of inventors applying for patents in 2015. Extrapolation is used when the value is outside the range of known values. As before, we have to substitute x=2015 into the equation of the trend line.
x= 2015
Multiply
Subtract terms
We could say that according to the given data, approximately 5444 inventors applied for patents in 2015. Note that we can pick different trend lines for the same set of data, so the estimates and predictions may vary.