Use the following to answer the questions below:
Students in a small statistics course wanted to investigate if forearm length (in cm) was useful for predicting foot length (in cm). The data they collected are displayed in the provided scatterplot (with regression), and the computer output from the analysis is provided.
Use three decimal places when reporting the results from any calculations, unless otherwise specified.
The regression equation is Foot (cm) = 9.22 + 0.574 Forearm (cm)
Predictor | Coef | SE Coef | T | P |
Constant | 9.216 | 4.521 | 2.04 | 0.066 |
Forearm (cm) | 0.5735 | 0.1578 | 3.63 | 0.004 |
Source | DF | SS | MS | F | P |
Regression | 1 | 44.315 | 44.315 | 13.20 | 0.004 |
Residual Error | 11 | 36.916 | 3.356 | | |
Total | 12 | 81.231 | | | |
Predicted Values for New Observations
Forearm (cm) | Fit | SE Fit | 95% CI | 95% PI |
28 | 25.274 | 0.513 | (24.144, 26.403) | (21.086, 29.461) |
![A scatterplot with a regression line shows the relationship between forearm and foot. The horizontal axis is labeled, Forearm (centimeters) and has markings from 22 to 36 in increments of 2. The vertical axis is labeled, Foot (centimeters) and has markings from 22 to 31 in increments of 1. A regression line starts from (23, 22.3), increases toward the right, and ends at (36, 30). The dots are randomly scattered throughout the graph, such that a few dots lie above the regression line, a few dots lie below the regression line, and a few dots lie on the regression line. The concentration of the dots is more between the points 26 and 30 on the horizontal axis and between the points, 23 and 25 on the vertical axis. The dots are plotted as follows: (23, 23), (24, 23), (26, 25), (27, 24), (28, 23), (29, 26), (29, 28), (29.5, 26), (29.5, 27), (30, 23), (32, 31), and (36, 29). All values are approximate.](data:image/png;base64, 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)
When conducting inference for the population slope, it is most common to test if the population slope is different from zero. However, there are other situations where a different test might be more interesting. For instance, it is often said that the length of the forearm is roughly the same as the length of the foot (see, for example, the movie
Pretty Woman). What population slope is implied by this statement, and what would the hypotheses for testing the accuracy of this claim look like?