How can one convert a proportion into a percentage?
A proportion is a value between 0 and 1, whereas a percentage is a value between 0 and 100, measured in %. One can convert a proportion into a percentage by multiplying the value of the proportion by 100. e.g. 0.15 * 100 = 15% A percentage can be converted to a proportion in the opposite way; divide the percentage by 100. e.g. 75% / 100 = 0.75
How and why might a firm use price discrimination?
Price discrimination is an innovative form of pricing in which the firm can charge different prices to different consumers for the same good or service. There 3 different degrees of price discrimination. First-degree discrimination splits the market into each individual consumer and charges the consumer the maximum they would pay. In this way, the producer gains all of the consumer surpluses that would have been present had they not practiced price discrimination. Remember that the consumer surplus is the difference between what consumers are willing to pay, and what they actually pay. Second-degree price discrimination involves charging consumers who purchase more a lower price. Third-degree price discrimination uses groups to split the market up. These groups must have distinctly different price elasticities of demand and must be identifiable to prevent arbitrage. An example of this would be cinema tickets, where the groups chosen are by age; adults are more price inelastic so are charged a higher price, whereas teens might pay a lower price as their demand will be more price elastic. The cinema has realized that they can increase their profits by reducing the price of entry for some consumers such that they are now willing to pay.
What is the difference between a t-statistic and the P-value?
A t-statistic is a value standardized to the standard error of your sample. It is used to test whether a sample mean (alternative hypothesis) is different to a population mean (null hypothesis). We can find the t -statistic by dividing the difference between the null and alternative hypothesized values by the standard error. The p-value works using the same logic. It gives the probability of calculating a sample mean equal to / greater than (or less than, if the value is negative) the population means.