Adata scientist is building a model for a business to evaluate various metrics about the products and outlook. in looking at the data, she decides that a product should be continued if it sold 9500 over the previous year. in addition, the product is considered "popular" if it receives 200 mentions by the local press over the past year.

in selecting a product at random from the company’s web page, let c be the likelihood that this product reached the 9500 thresholds the past year. let p be the likelihood that the product received the 200 or more mentions by the local press.

the analyst determines that p(c) = 0.307, p(p) = 0.194, and the probability that a product has sold 9500 items, and was ‘popular’ is 0.084. what is the probability that a randomly selected product either sold the requisite 9500 items or that it is ‘popular’?

part b: where would the analyst have come up with the probability values for p(c) and p(p)?

Design an er diagram for keeping track of information about votes taken in the u.s. house of representatives during the current two-year congressional session. the database needs to keep track of each u.s. state's name (e.g., texas', new york', california') and include the region of the state (whose domain is {northeast', midwest', southeast', southwest', west'}). each congress person in the house of representatives is described by his or her name, plus the district represented, the start date when the congressperson was first elected, and the political party to which he or she belongs (whose domain is {republican', democrat', 'independent', other'}). the database keeps track of each bill (i.e., proposed law), including the bill name, the date of vote on the bill, whether the bill passed or failed (whose domain is {yes', no'}), and the sponsor (the congressperson(s) who sponsored - that is, proposed - the bill). the database also keeps track of how each congressperson voted on each bill (domain of vote attribute is {yes', no', abstain', absent'}). additional information: