Summarize this article in one sentence.
In the fourth column of your data table, you will calculate and record the error of each predicted value. Specifically, subtract the predicted value (y′{\displaystyle y^{\prime }}) from the actual observed value (y{\displaystyle y}). For the data in the sample set, these calculations are as follows:  y(x)−y′(x){\displaystyle y(x)-y^{\prime }(x)} y(1)−y′(1)=2−2.8=−0.8{\displaystyle y(1)-y^{\prime }(1)=2-2.8=-0.8} y(2)−y′(2)=4−3.4=0.6{\displaystyle y(2)-y^{\prime }(2)=4-3.4=0.6} y(3)−y′(3)=5−4=1{\displaystyle y(3)-y^{\prime }(3)=5-4=1} y(4)−y′(4)=4−4.6=−0.6{\displaystyle y(4)-y^{\prime }(4)=4-4.6=-0.6} y(5)−y′(5)=5−5.2=−0.2{\displaystyle y(5)-y^{\prime }(5)=5-5.2=-0.2} Take each value in the fourth column and square it by multiplying it by itself. Fill in these results in the final column of your data table. For the sample data set, these calculations are as follows:  −0.82=0.64{\displaystyle -0.8^{2}=0.64} 0.62=0.36{\displaystyle 0.6^{2}=0.36} 12=1.0{\displaystyle 1^{2}=1.0} −0.6=0.36{\displaystyle -0.6=0.36} −0.2=0.04{\displaystyle -0.2=0.04} The statistical value known as the sum of squared errors (SSE) is a useful step in finding standard deviation, variance and other measurements. To find the SSE from your data table, add the values in the fifth column of your data table. For this sample data set, this calculation is as follows: 0.64+0.36+1.0+0.36+0.04=2.4{\displaystyle 0.64+0.36+1.0+0.36+0.04=2.4} The Standard Error of the Estimate is the square root of the average of the SSE. It is generally represented with the Greek letter σ{\displaystyle \sigma }. Therefore, the first calculation is to divide the SSE score by the number of measured data points. Then, find the square root of that result.  If the measured data represents an entire population, then you will find the average by dividing by N, the number of data points. However, if you are working with a smaller sample set of the population, then substitute N-2 in the denominator. For the sample data set in this article, we can assume that it is a sample set and not a population, just because there are only 5 data values. Therefore, calculate the Standard Error of the Estimate as follows:  σ=2.45−2{\displaystyle \sigma ={\sqrt {\frac {2.4}{5-2}}}} σ=2.43{\displaystyle \sigma ={\sqrt {\frac {2.4}{3}}}} σ=0.8{\displaystyle \sigma ={\sqrt {0.8}}} σ=0.894{\displaystyle \sigma =0.894} The Standard Error of the Estimate is a statistical figure that tells you how well your measured data relates to a theoretical straight line, the line of regression. A score of 0 would mean a perfect match, that every measured data point fell directly on the line. Widely scattered data will have a much higher score. With this small sample set, the standard error score of 0.894 is quite low and represents well organized data results.

Summary:
Calculate the error of each predicted value. Calculate the squares of the errors. Find the sum of the squared errors (SSE). Finalize your calculations. Interpret your result.