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) |
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?
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