In binary regression, probability data for predicting binary ...

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In binary regression, probability data for predicting binary variables is transformed using
 a. an exponential transformation c. the logarithm of the odds
  b. a geometric transformation d. dummy variables

Question 2

Compare and contrast Type I errors and Type II errors and explain which one is of more concern to researchers.

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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

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