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Cup of coffee investigation To find these lines of best fit, I experimented with suitable gradient on omnigraph. I found the gradients that fitted the data the best and used them on my graph. It is very common to find an odd value that might not fit the line of best fit. I had to use 4 different gradients in order for almost all of the data to be near a line of best fit. comparison of data, prediction smoker who drinks coffee Time hours Data Prediction of data from graph Error Percentage error % (to 3sf) 0 130 130 0 0 1 108 109 1 0.93 2 89 91 2 2.25 3 74 74 0 0 4 61 55 6 9.84 5 50 48 2 4 6 41 42 1 2.44 7 34 35 1 2.94 8 28 28 0 0 9 23 22 1 4.35 10 19 15 4 21.1 11 16 13 3 18.8 12 13 13 0 0 13 11 13 2 18.2 14 9 13 4 44.4 15 7 4 3 42.9 16 6 4 2 33.3 17 5 4 1 20 18 4 4 0 0 19 3 4 1 33.3 20 3 4 1 33.3 21 2 4 2 100 21 2 4 2 100 23 1 4 3 300 24 1 4 3 300 By substituting the percentage of data in for the value of Y, I predicted how long it took for the caffeine levels in the body to reduce to: 80%, 50% and 40% of the peak value. I predicted that it took: * Prediction for 80% 80% of 130 = 104. 104=-19x+130 19x=130-104 19x=26 x=1.37(to 2d.p) 1.37 as an hour= 1 hour 22 minutes * Prediction for 50% 50%...

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