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There were two parts I didn't understand: One, how was the equation for revenue derived? Is it common knowledge that Revenue, as a function of price, is equival

• icegreentea 14y ago
  If you're in excel, R^2 values is just a couple check boxes away. The actual math is: If you have a scatter of data (x, y), then you can get a measure of the variability of your data by taking the sum of squares of (y - avg(y)) for all your data points. Call this V Further, if your doing say a simple linear regression, then for every x, you can have an f(x) which is suppose to be an 'estimate' of y. So then for all our given data points, we can find an error, which is basically the distance of y from the fitted line (y-f(x) basically). Then we can take the sum of squares of those errors get what is sometimes called our residual sum of squares. Call this W. The R^2 value is defined as 1 - (R/W). So we can see that for very good fits of data, R^2 will be near 1, and for poor fits of data, R^2 will be near 0. Edit: I guess to throw another bit at you. Often in microeconomics, you assume that as long as you can meet a demand at a certain price, then you will in fact sell however much is demanded at that price. So as an lemonade stand, if there is a demand for 100 cups of lemonade at 50 cents a cup, and you have 100 cups of lemonade and are also willing to sell at 50 cents a cup, then you WILL sell 100 cups. In which case, the revenue = price * demand kind of falls out by itself.