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torisob [31]
3 years ago
6

When doctors prescribe medicine, they must consider how the drug’s effectiveness declines as time passes. If each hour a drug is

only 90% as effective as the previous hour, at some point the patient will have to be given another dose in order for the drug to remain effective. If the initial dose was 500 mg and the drug was administered 6 hours ago, how long will it take for the initial dose to reach a level of 150 mg?
Mathematics
1 answer:
Dima020 [189]3 years ago
4 0

Answer:

<u>The total time for the initial dose of 500 mg to reach a level of 150 mg is approximately 11 hours and 27 minutes </u>

Step-by-step explanation:

Initial dose = 500 mg

Let's calculate the effectiveness of the drug hour by hour, this way:

After 1 hour: 90% of 500 = 450 mg

After 2 hours: 90% of 450 = 405 mg

After 3 hours: 90% of 405 = 364.5 mg

After 4 hours: 90% of 364.5 = 328.05 mg

After 5 hours: 90% of 328.05 = 295.25 mg

After 6 hours: 90% of 295.25 = 265.73 mg

After 7 hours: 90% of 265.73 = 239.16 mg

After 8 hours: 90% of 239.16 = 215.24 mg

After 9 hours: 90% of 215.24 = 193.72 mg

After 10 hours: 90% of 193.72 = 174.35 mg

After 11 hours: 90% of 174.35 = 156.92 mg

After 12 hours: 90% of 156.92 = 141.23 mg

Difference between 11 and 12 hours: 156.92 - 141.23 = 15.7 mg

Difference per minute between 11 and 12 hours: 15.7/60 = 0.262 mg

Time to reach a level of 150 mg in minutes after 11 hours = (156.92-150)/0.262

Time to reach a level of 150 mg in minutes after 11 hours = 26.4

<u>The total time for the initial dose of 500 mg to reach a level of 150 mg is approximately 11 hours and 27 minutes </u>

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z=\frac{0.293 -0.23}{\sqrt{\frac{0.23(1-0.23)}{594}}}=3.649  

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It's important to refresh the p value method or p value approach . "This method is about determining "likely" or "unlikely" by determining the probability assuming the null hypothesis were true of observing a more extreme test statistic in the direction of the alternative hypothesis than the one observed". Or in other words is just a method to have an statistical decision to fail to reject or reject the null hypothesis.  

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