The Regression Equation Quiz

PDF page 656: Data rarely fit a straight line exactly. Usually, you must be satisfied with rough predictions. Typically, you have a set of data with a scatter plot that appear to fit a straight line. This is called a line of best fit or least-squares regression line.

PDF page 656: If you know a person’s pinky (smallest) finger length, do you think you could predict that person’s height? Collect data from your class (pinky finger length, in inches). The independent variable, x, is pinky finger length and the dependent variable, y, is height. For each set of data, plot the points on graph paper. Make your graph big enough and use a ruler. Then, by eye, draw a line that appears to fit the data. For your line, pick two convenient points and use them to find the slope of the line. Find the y-intercept of the line by extending your line so it crosses the y-axis. Using the slopes and the y-intercepts, write your equation of best fit. Do you think everyone will have the same equation? Why or why not? According to your equation, what is the predicted height for a pinky length of 2.5 inches?

PDF page 656: If you know a person’s pinky (smallest) finger length, do you think you could predict that person’s height? Collect data from your class (pinky finger length, in inches). The independent variable, x, is pinky finger length and the dependent variable, y, is height. For each set of data, plot the points on graph paper. Make your graph big enough and use a ruler. Then, by eye, draw a line that appears to fit the data. For your line, pick two convenient points and use them to find the slope of the line. Find the y-intercept of the line by extending your line so it crosses the y-axis. Using the slopes and the y-intercepts, write your equation of best fit. Do you think everyone will have the same equation? Why or why not? According to your equation, what is the predicted height for a pinky length of 2.5 inches?

PDF page 656: A random sample of 11 statistics students produced the data in Table 12.1, where x is the third exam score out of 80 and y is the final exam score out of 200. Can you predict the final exam score of a random student if you know the third exam score?

PDF page 660: The term y0 – ŷ0 = ε0 is called the error or residual. It is not an error in the sense of a mistake. The absolute value of a residual measures the vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line, or it measures how far the estimate is from the actual data value. If the observed data point lies above the line, the residual is positive and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative and the line overestimates that actual data value for y. In Figure 12.6, y0 – ŷ0 = ε0 is the residual for the point shown. Here the point lies above the line and the residual is positive. ε = the Greek letter epsilon For each data point, you can calculate the residuals or errors, yi – ŷi = εi for i = 1, 2, 3, . . . , 11. Each |ε| is a vertical distance. For the example about the third exam scores and the final exam scores for the 11 statistics students, there are 11 data points. Therefore, there are 11 ε values. If you square each ε and add them, you get the sum of ε squared from i = 1 to i = 11, as shown below.

PDF page 660: The term y0 – ŷ0 = ε0 is called the error or residual. It is not an error in the sense of a mistake. The absolute value of a residual measures the vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line, or it measures how far the estimate is from the actual data value. If the observed data point lies above the line, the residual is positive and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative and the line overestimates that actual data value for y. In Figure 12.6, y0 – ŷ0 = ε0 is the residual for the point shown. Here the point lies above the line and the residual is positive. ε = the Greek letter epsilon For each data point, you can calculate the residuals or errors, yi – ŷi = εi for i = 1, 2, 3, . . . , 11. Each |ε| is a vertical distance. For the example about the third exam scores and the final exam scores for the 11 statistics students, there are 11 data points. Therefore, there are 11 ε values. If you square each ε and add them, you get the sum of ε squared from i = 1 to i = 11, as shown below.

PDF page 660: This is called the sum of squared errors (SSE). Using calculus, you can determine the values of a and b that make the SSE a minimum. When you make the SSE a minimum, you have determined the points that are on the line of best fit. It turns out that the line of best fit has the equation

PDF page 660: This is called the sum of squared errors (SSE). Using calculus, you can determine the values of a and b that make the SSE a minimum. When you make the SSE a minimum, you have determined the points that are on the line of best fit. It turns out that the line of best fit has the equation

PDF page 660: The process of fitting the best-fit line is called linear regression. We assume that the data are scattered about a straight line. To find that line, we minimize the sum of the squared errors (SSE), or make it as small as possible. Any other line you might choose would have a higher SSE than the best-fit line. This best-fit line is called the least-squares regression line.

PDF page 660: The sample means of the x values and the y values are and , respectively. The best-fit line always passes through the point . The slope (b) can be written as

PDF page 660: The process of fitting the best-fit line is called linear regression. We assume that the data are scattered about a straight line. To find that line, we minimize the sum of the squared errors (SSE), or make it as small as possible. Any other line you might choose would have a higher SSE than the best-fit line. This best-fit line is called the least-squares regression line.

PDF page 663: The equation of the best-fit line is ŷ = –173.51 + 4.83x. 2 The two items at the bottom are r = .43969 and r = .663. For now, just note where to find these values; we examine them in the next two sections. Graphing the Scatter Plot and Regression Line

PDF page 661: The y-intercept, a, of the line describes where the plot line crosses the y-axis. The y-intercept of the best-fit line tells us the best value of the relationship when x is zero. In some cases, it does not make sense to figure out what y is when x =