{"id":11969,"date":"2018-07-10t12:17:42","date_gmt":"2018-07-10t19:17:42","guid":{"rendered":"\/\/www.catharsisit.com\/hs\/?p=11969"},"modified":"2018-07-09t22:17:52","modified_gmt":"2018-07-10t05:17:52","slug":"ap-calculus-review-average-value-functions","status":"publish","type":"post","link":"\/\/www.catharsisit.com\/hs\/ap\/ap-calculus-review-average-value-functions\/","title":{"rendered":"ap calculus review: average value of functions"},"content":{"rendered":"
the average of a set of data is typically defined as the sum of the values divided by the number of data points. but what if you have infinitely many data points? what is the average value<\/em> of a function? read on to find out!<\/p>\n suppose f<\/em> is a continuous function defined over an interval [a<\/em>, b<\/em>]. in particular f<\/em>(x<\/em>) exists at every one of the infinitely-many points x<\/em> between and including a<\/em> and b<\/em>. so, if you’re looking for the average value of f<\/em> on that interval, it won’t do any good to try adding up those infinitely-many data points.<\/p>\n instead, the way to tame the infinity is to use calculus. specifically, we define the average value of a function f<\/em> as the following definite integral.<\/p>\n but where does the integral formula for average come from?<\/p>\n the key is sampling<\/strong>. if you included enough of the function values, say a thousand, a million, or even more, then that should approximate the average of all infinitely-many points!<\/p>\n let’s illustrate with the following example.<\/p>\n estimate the average value of the function f<\/em>(x<\/em>) = \u221a(x<\/em>) + 1 over the interval [1, 3].<\/p>\n since we’re just estimating, let’s pick four sample points. (the more sample points you pick, the better your estimated average will be.)<\/p>\n divide the interval [1, 3] into four equal subintervals, and let’s agree to choose the midpoint of each subinterval. then plug those midpoints into f<\/em> to find the sample values.<\/p>\naverage value of functions<\/h2>\n
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the theory behind the formula<\/h3>\n