To compute the least-squares regression line for predicting the right foot temperature from the left foot temperature, we need to use the formula for the regression line given by:
\[ y = mx + b \]
where:
- \(y\) is the predicted temperature of the right foot,
- \(x\) is the temperature of the left foot,
- \(m\) is the slope of the line,
- \(b\) is the y-intercept.
Step 1: Gather the data
Given:
- Left foot temperatures (\(X\)): 80, 75, 85, 88, 89, 78, 87, 88
- Right foot temperatures (\(Y\)): 80, 80, 85, 86, 87, 78, 82, 89
Step 2: Calculate means of \(X\) and \(Y\)
- Calculate \(\bar{X}\) and \(\bar{Y}\):
\[ \bar{X} = \frac{(80 + 75 + 85 + 88 + 89 + 78 + 87 + 88)}{8} = \frac{ 80 + 75 + 85 + 88 + 89 + 78 + 87 + 88}{8} = \frac{ 80 + 75 + 85 + 88 + 89 + 78 + 87 + 88}{8} = \frac{ 680}{8} = 85 \]
\[ \bar{Y} = \frac{(80 + 80 + 85 + 86 + 87 + 78 + 82 + 89)}{8} = \frac{ 80 + 80 + 85 + 86 + 87 + 78 + 82 + 89}{8} = \frac{ 678}{8} = 84.75 \]
Step 3: Calculate the slope \(m\)
- Use the formula:
\[ m = \frac{\sum{(X_i - \bar{X})(Y_i - \bar{Y})}}{\sum{(X_i - \bar{X})^2}} \]
- Calculate \(\sum{(X_i - \bar{X})(Y_i - \bar{Y})}\) and \(\sum{(X_i - \bar{X})^2}\):
- For each \(i\), calculate \(X_i - \bar{X}\), \(Y_i - \bar{Y}\), \((X_i - \bar{X})(Y_i - \bar{Y})\), and \((X_i - \bar{X})^2\).
| \(X_i\) | \(Y_i\) | \(X_i - \bar{X}\) | \(Y_i - \bar{Y}\) | \((X_i - \bar{X})(Y_i - \bar{Y})\) | \((X_i - \bar{X})^2\) | |---------|---------|--------------------|--------------------|------------------------------------|-----------------------| | 80 | 80 | -5 | -4.75 | 23.75 | 25 | | 75 | 80 | -10 | -4.75 | 47.5 | 100 | | 85 | 85 | 0 | 0.25 | 0 | 0 | | 88 | 86 | 3 | 1.25 | 3.75 | 9 | | 89 | 87 | 4 | 2.25 | 9 | 16 | | 78 | 78 | -7 | -6.75 | 47.25 | 49 | | 87 | 82 | 2 | -2.75 | -5.5 | 4 | | 88 | 89 | 3 | 4.25 | 12.75 | 9 |
-
\(\sum{(X_i - \bar{X})(Y_i - \bar{Y})} = 23.75 + 47.5 + 0 + 3.75 + 9 - 5.5 + 12.75 = 91.5\)
-
\(\sum{(X_i - \bar{X})^2} = 25 + 100 + 0 + 9 + 16 + 49 + 4 + 9 = 212\)
- Calculate \(m\):
\[ m = \frac{91.5}{212} \approx 0.4311 \]
Step 4: Calculate the y-intercept \(b\)
- Use the formula:
\[ b = \bar{Y} - m\bar{X} \]
\[ b = 84.75 - (0.4311 \times 85) \approx 84.75 - 36.6385 \approx 48.1115 \]
Step 5: Write the regression line
The least-squares regression line to predict the right foot temperature \(Y\) from the left foot temperature \(X\) is:
\[ Y = 0.4311X + 48.1115 \]
Final Result
Thus, rounding to four decimal places, the least-squares regression line is:
\[ Y = 0.4311X + 48.1115 \]