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In statistics, an effect size is a value measuring the strength of the relationship between two variables in a population, or a sample-based estimate of that quantity. It can refer to the value of a statistic calculated from a sample of data, the value of one parameter for a hypothetical population, or to the equation that operationalizes how statistics or parameters lead to the effect size ...
A group of 20 students spends between 0 and 6 hours studying for an exam. How does the number of hours spent studying affect the probability of the student passing the exam? The reason for using logistic regression for this problem is that the values of the dependent variable, pass and fail, while represented by "1" and "0", are not cardinal ...
The Student Hour is approximately 12 hours of class or contact time, approximately 1/10 of the Carnegie Unit (as explained below). As it is used today, a Student Hour is the equivalent of one hour (50 minutes) of lecture time for a single student per week over the course of a semester, usually 14 to 16 weeks.
For example, if the normal schedule for a quarter is defined as 411.25 hours ([35 hours per week × (52 weeks per year – 5 weeks' regulatory vacation)] / 4), then someone working 100 hours during that quarter represents 100/411.25 = 0.24 FTE. Two employees working in total 400 hours during that same quarterly period represent 0.97 FTE.
For an approximately normal data set, the values within one standard deviation of the mean account for about 68% of the set; while within two standard deviations account for about 95%; and within three standard deviations account for about 99.7%.
A 5.5 constitutes a pass, whereas 5.4 and below constitute a fail. If no decimal places are used, 6 and up is a pass and 5 and below is a fail; however, in this case of grading in full numbers there exists sometimes "6-", which would officially translate to 5.75, but can be interpreted here as "barely, but just good enough".
Pearson's correlation coefficient is the covariance of the two variables divided by the product of their standard deviations. The form of the definition involves a "product moment", that is, the mean (the first moment about the origin) of the product of the mean-adjusted random variables; hence the modifier product-moment in the name.
Suppose the answer is 0.02 (i.e., 2%). Then, the probability that the bacterium dies between 5 hours and 5.001 hours should be about 0.002, since this time interval is one-tenth as long as the previous. The probability that the bacterium dies between 5 hours and 5.0001 hours should be about 0.0002, and so on.