Suppose you eat two hamburgers for lunch. The marginal benefit of the first burger is ________ of the second burger

Refer to Figure 17-4. Consider the shift in the short-run Phillips curves shown in the above graph. This shift may be explained by

A) an increase in the expected rate of inflation from 4.0 to 5.5 percent. B) an increase in the natural rate of unemployment from 5.0 to 6.2 percent. C) either an increase in the natural rate of unemployment from 5.0 to 6.2 percent or an increase in the expected rate of inflation from 4.0 to 5.5 percent. D) None of the above is correct.

Statistical inference was a concept that was not too difficult to understand when using cross-sectional data

For example, it is obvious that a population mean is not the same as a sample mean (take weight of students at your college/university as an example). With a bit of thought, it also became clear that the sample mean had a distribution. This meant that there was uncertainty regarding the population mean given the sample information, and that you had to consider confidence intervals when making statements about the population mean. The same concept carried over into the two-dimensional analysis of a simple regression: knowing the height-weight relationship for a sample of students, for example, allowed you to make statements about the population height-weight relationship. In other words, it was easy to understand the relationship between a sample and a population in cross-sections. But what about time-series? Why should you be allowed to make statistical inference about some population, given a sample at hand (using quarterly data from 1962-2010, for example)? Write an essay explaining the relationship between a sample and a population when using time series. What will be an ideal response?