Suppose there are 1.000 identical firms producing diamonds. Let the total cost function for each firm be given by C(q, w) = q2 + wq, where q is the firm's output level and w Ls the wage rate of diamond cutters. If w = 10, what will be the firm's (short-run) supply curve? What is the industry's supply curve? How many diamonds will be produced at a price of $20 each? How many more diamonds would be produced at a price of $21? Suppose the wage of diamond cutters depend on the total quantity of diamonds produced. and suppose the form of this relationship is given by w = 0.002Q where Q represents total industry output, which is 1,000 times the output of the typical firm. In this situation, show that the firm's marginal cost (and short-run supply) curve depend on Q. What is the industry supply curve (in the long-run)? How much will be produced at a price of $20? How much more will be produced at a price of $21? What do you conclude about he shape of the short-run supply curve?

Reliability and validity reliability and validity are two important considerations that must be made with any type of data collection. reliability refers to the ability to consistently produce a given result. in the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways. unfortunately, being consistent in measurement does not necessarily mean that you have measured something correctly. to illustrate this concept, consider a kitchen scale that would be used to measure the weight of cereal that you eat in the morning. if the scale is not properly calibrated, it may consistently under- or overestimate the amount of cereal that’s being measured. while the scale is highly reliable in producing consistent results (e.g., the same amount of cereal poured onto the scale produces the same reading each time), those results are incorrect. this is where validity comes into play. validity refers to the extent to which a given instrument or tool accurately measures what it’s supposed to measure. while any valid measure is by necessity reliable, the reverse is not necessarily true. researchers strive to use instruments that are both highly reliable and valid.