2. Find the regression line, the line that best fits your data. When writing the equation of this line, use the words of your variables, not x and y. Draw at least five of the vertical distances between observations and the regression line. (See page 159 in our text.) . Print your scatterplot again, this time with the regression line. What is the correlation coefficient for your regression line? What does this r-value mean in the context of your two variables? . What is the slope of your regression line? Interpret this slope in the context of your variables. What is the y-intercept of your regression line? Interpret the y-intercept in the context of your variables. If the y-intercept is meaningless, explain why. Mention any unusual points on your graph. Explain why they are unusual in the context of your data. What is the value of r2? What does this tell you about your data? is sometimes called the "best fit" line because, in this sense, it provides the best fit to the data. 180 170 160 Weight (pounds) gggg 110 100 61 62 63 64 65 66 67 68 Height (inches) To find this best fit line, we need to find the slope and the intercept. The slope, b, of the regression line is the ratio of the standard deviations of the two variables, multiplied by the correlation coefficient: Formula 4.2a: The slope, b = Sy Once we have found the slope, we can find the intercept. Finding the intercept, a, requires that we first find the means of the variables, x and y: Formula 4.2b: The intercept, a = y - bx Now put these quantities into the equation of a line, and the regression line is given by Formula 4.2c: The regression line, Predicted y = a + bx