College students often find general chemistry to be a very challenging rite of passage on their way to degrees in various science, technology, and mathematics disciplines. As teachers, we make efforts to simultaneously patch gaps in students' prior knowledge and instill valuable learning strategies and sound study habits. In this paper, we describe effective metacognitive learning strategies for students in general chemistry courses. Many students experience difficulty because they are focused on memorizing facts and formulas instead of understanding concepts and developing problem-solving skills. However, students can be successful if they are taught how to shift their efforts from low-level to higher-order thinking. We present outcomes from a 50 min lecture on learning strategies presented to a population of nearly 700 science major first-year students after the first examination. The average final grade for the students who attended the lecture was a full letter grade higher than that of those who were absent, while the performance on the first examination was not statistically significantly different for the two groups. Student survey response data indicated that the students who attended the lecture changed their behavior as a result of gaining new information about learning. Statistical analysis of the results was performed using the ANCOVA approach.

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Endorectal MRI may effectively stratify patients with prior negative prostatic biopsy into low, moderate and high risk groups for a malignant prostatic neoplasm, and may improve our ability to identify prostatic tumor foci prospectively.

A monte carlo study was conducted to examine the performance of several strategies for estimating the squared cross-validity coefficient of a sample regression equation in the context of best subset regression. Data were simulated for populations and experimental designs likely to be encountered in practice. The results indicated that a formula presented by Stein (1960) could be expected to yield estimates as good as or better than cross-validation, or several other formula estimators, for the populations considered. Further, the results suggest that sample size may play a much greater role in validity estimation in subset selection than is true in situations where selection has not occurred.

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