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
The carbon cycle. Numbers are storage as 1015 g or fluxes as 1015 g per year (data from Schlesinger
Nitrogen fluxes in Sycamore Creek, Arizona (data from Grimm 1988).
Recognizing data clusters
Hubbard’s Predication of Peak in U.S Oil Production with Actual Data
Discrete random variables- Probability Distribution Function
data