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Found in this investigation that caffeine levels drastically rise in the first hour and then rapidly

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Introduction

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) ...read more.

Middle

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. ...read more.

Conclusion

This shows that the line of best fit was well placed. I found in this investigation that caffeine levels drastically rise in the first hour and then rapidly fall, but stabilize after 5 hours. I was able to predict values relatively accurately using my functions. These functions are very useful when trying to find the caffeine level in between an hour as the table does not give you that data. When trying to find a fixed hour it would be better to use the data given as the function would very rarely be 100% accurate. ?? ?? ?? ?? Mohammed Ishfaq ...read more.

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