Answer:
Linear regression looks at the relationship between two variables, X and Y.
The regression line is the "best" line though some data you that you or someone else has collected.
You want to find the best slope and the best y intercept to be able to plot the line that will allow you to predict Y given a value of X.
This is usually done by minimizing the sum of the squares.
Regression Equation is y = a + bx
Slope(b) = (NΣXY - (ΣX)(ΣY)) / (NΣX2 - (ΣX)2)
Intercept(a) = (ΣY - b(ΣX)) / N
In the equation above:
X and Y are the variables given as an ordered pair (X,Y)
b = The slope of the regression line
a = The intercept point of the regression line and the y axis.
N = Number of values or elements
X = First Score
Y = Second Score
ΣXY = Sum of the product of first and Second Scores
ΣX = Sum of First Scores
ΣY = Sum of Second Scores
ΣX2 = Sum of square First Scores
Once you find the slope and the intercept, you plot it the same way you plot any other line!