Chapter 22: Problem 4
Find the equation of the least-squares line for the given data. Graph the line and data points on the same graph. $$\begin{array}{l|r|r|r|r|r|r|r|r|r|r}x & 1 & 3 & 6 & 5 & 8 & 10 & 4 & 7 & 3 & 8 \\\\\hline y & 15 & 12 & 10 & 8 & 9 & 2 & 11 & 9 & 11 & 7 \end{array}$$
Short Answer
Expert verified
The equation of the least-squares line is \( y = 15.0 - 1.018x \).
Step by step solution
01
Calculate means
We'll start by calculating the mean of the x-values and the y-values. The mean of x, denoted as \( \overline{x} \), is computed as:\[\overline{x} = \frac{\sum{x_i}}{n} = \frac{1 + 3 + 6 + 5 + 8 + 10 + 4 + 7 + 3 + 8}{10} = 5.5\]The mean of y, denoted as \( \overline{y} \), is computed as:\[\overline{y} = \frac{\sum{y_i}}{n} = \frac{15 + 12 + 10 + 8 + 9 + 2 + 11 + 9 + 11 + 7}{10} = 9.4\]
02
Calculate slope (b)
The next step is to compute the slope \( b \) of the least-squares line using the formula:\[b = \frac{\sum{(x_i - \overline{x})(y_i - \overline{y})}}{\sum{(x_i - \overline{x})^2}}\]Calculate \( \sum{(x_i - \overline{x})(y_i - \overline{y})} \):\[(1-5.5)(15-9.4) + (3-5.5)(12-9.4) + (6-5.5)(10-9.4) + \ldots + (8-5.5)(7-9.4) = -84\]Calculate \( \sum{(x_i - \overline{x})^2} \):\[(1-5.5)^2 + (3-5.5)^2 + (6-5.5)^2 + \ldots + (8-5.5)^2 = 82.5\]Thus,\[b = \frac{-84}{82.5} \approx -1.018\]
03
Calculate y-intercept (a)
Using the slope \( b = -1.018 \), the y-intercept \( a \) can be calculated using the formula:\[a = \overline{y} - b \overline{x}\]Substitute the known values:\[a = 9.4 - (-1.018)(5.5) = 9.4 + 5.599 = 15.0\]
04
Write the equation
The equation of the least-squares line is given by:\[y = a + bx\]Substituting the computed values for \( a \) and \( b \):\[y = 15.0 - 1.018x\]
05
Graph the data and the line
Graph the data points and the least-squares line on the same plot. Plot the x-values (1, 3, 6, 5, 8, 10, 4, 7, 3, 8) against their corresponding y-values (15, 12, 10, 8, 9, 2, 11, 9, 11, 7). Then, draw the line represented by the equation \( y = 15.0 - 1.018x \) across the same range of x-values. Check that the line closely follows the trend of the points but may not pass through any particular points.
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Key Concepts
These are the key concepts you need to understand to accurately answer the question.
Slope Calculation
Calculating the slope is an essential part of determining the least-squares regression line. The slope, denoted as \( b \), measures how much the dependent variable \( y \) changes for a one-unit change in the independent variable \( x \). To compute the slope, use the formula:
- \[ b = \frac{\sum{(x_i - \overline{x})(y_i - \overline{y})}}{\sum{(x_i - \overline{x})^2}} \]
- How much each individual \( x \)-value differs from the mean of \( x \), \( \overline{x} \), and the same for \( y \).
- The product of these differences for each data point, summed up over all data points.
Y-Intercept
The y-intercept, \( a \), is the point where the regression line intersects the y-axis. It represents the value of \( y \) when \( x = 0 \). In the least-squares regression context, it is calculated using the formula:
- \[ a = \overline{y} - b\overline{x} \]
Mean Calculation
Finding the mean values of \( x \) and \( y \) is a foundational step in calculating the least-squares regression line. The mean, or average, of the data points simplifies them into a single representative value.
- The arithmetic mean for \( x \) is calculated as \[ \overline{x} = \frac{\sum{x_i}}{n} = 5.5 \]
- Similarly, the arithmetic mean for \( y \) is \[ \overline{y} = \frac{\sum{y_i}}{n} = 9.4 \]
Graphing Data Points
Visualizing the data points and the regression line on a graph is essential for understanding the relationship between variables. Each data point represents the original values of \( x \) and \( y \).
- Plot each \( (x, y) \) pair on a coordinate plane.
- Draw the regression line, \( y = 15.0 - 1.018x \), using the calculated slope and y-intercept.
- Notice that while the line may not pass through each point, it trends along the general direction of the data.
- The degree to which the line captures the spread of points indicates the strength of the linear relationship.