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27 women with a diagnosis of inoperable or metastatic cancer were followed for a number of years while they were on continuous treatment with a drug called trastuzumab. The time (in months) that each patient remained in remission was recorded. (Remission is the decrease or disappearance of signs and symptoms of cancer.) Here are the data:

88.8, 98.8, 17.8, 28.7, 15.4, 96.6, 97.3, 21.3, 85.8, 47.7, 58.4, 19.7, 33.7, 82.4, 50.2, 90.4, 17.5, 73.2, 61.2, 16.8, 31.3, 35.2, 11.7, 10.9, 24.4, 93.7, 47.4

Answer the following questions using R:

(a) Calculate the mean, median, and standard deviation of the remission time.

(b) Construct the box-whisker plot of remission time.

(c) Does the remission time seem to be normally distributed? Justify your answer using a quantile-quantile plot.

(d) Calculate the mean, median, and standard deviation of the square root of the remission time. Is there a link between these values and the values found in (a)?

Answer :

To analyze the re-emission time data in R, calculate mean, median, and standard deviation, construct a box-whisker plot, determine normal distribution using a Q-Q plot, and compare statistics with square root data.

To calculate the mean, median, and standard deviation of the re-emission time, you can use R programming language. In R, you can input the data and use built-in functions such as mean(), median(), and sd() to calculate the desired values. To construct the box-whisker plot, you can use the boxplot() function.

To determine if the re-emission time is normally distributed, you can create a quantile-quantile (Q-Q) plot with norm().

Lastly, to calculate the mean, median, and standard deviation of the square root of the remission time, you need to take the square root of each observation and then apply the respective functions.

You can compare these values with the ones obtained in part (a) to analyze the link between the two sets of values.

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