Answer to Question 1
C
By using the log-odds transformation, it is possible to logistic regression for binary dependent variables.
Answer to Question 2
Hypothesis testing using sample observations is based on probability theory. Because we cannot make any statement about a sample with complete certainty, there is always the chance that an error will be made. The researcher using sampling runs the risk of committing two types of errors. A Type I error occurs when a condition that is true in the population is rejected based on statistical observations. When a researcher sets an acceptable significance level a priori (), he or she is determining how much tolerance he or she has for a Type I error. When testing for relationships, a Type I error occurs when the researcher concludes a relationship exists when in fact one does not exist. A Type II error is the probability of failing to reject a false null hypothesis. Alternatively, one can think of this as failing to identify a hypothesized difference using a sample result when one really does exist in the population. This incorrect decision is called beta (), Type I errors generally are considered more serious than Type II errors. Thus, more emphasis is placed on determining the significance level, , than in determining
Recognizing data clusters
A physician uses a portable electronic tablet to enter patient data while in the examination room.
Data management questions
Probability Values of Chi-Square Analysis
Linkage Data from 39 Families with Cystic Fibrosis
Ribosome structure and tRNA-binding sites interpreted from cryo-EM–generated data