Which choice most effectively uses data from the graph to illustrate …

Source Texts

Text

Researchers used anonymized location data from the US and Cote d'Ivoire to document people's daily patterns of mobility, using these results to test the efficacy of the researchers' predictive computer model. In each country, unidirectional cycles among two, three, or four locations were empirically the most common pattern types; the graph shows each of these pattern types as a proportion of all pattern instances found for that country (e.g, the measured value for CI 3 in the graph, 0.12, indicates that the three-location pattern constituted 12% of all pattern instances in the Cote d'Ivoire data). The researchers ran their model twice under different assumptions, concluding that emphasizing the salience of local population density over personal preferences generally yielded the best results.

Which choice most effectively uses data from the graph to illustrate the researchers' conclusion?

Under the assumption that density is more salient than preferences, the US 2 and CI 2 proportions are approximately 0.65 and 0.85, respectively significantly higher than the values predicted under the other assumption and thus farther than those predictions from the measured values.

Under the assumption that preferences are more salient than density, the US 2 and CI 2 proportions were predicted to be approximately 0.45 and.35, respectively, both below the measured values, whereas under the other assumption, the model overestimated the proportion for US 2 and overestimated that for CI 2

Under the assumption that preferences are more salient than density, the two-location patterns (US 2 and CI 2) were predicted to be most frequent in the data even though neither proportion was projected to exceed 0.5, well below the proportion predicted under the other assumption.

Under the assumption that preferences are more salient than density, the US 2 and CI 2 proportions were predicted to be in the range of 0.3 to 0.5, placing them farther from the measured values than were those predicted under the other assumption.