When analyzing data sets, such as data for human heights or for human weights, a common step is to adjust the data. This adjustment can be done by normalizing to values between 0 and 1, or throwing away outliers. For this program, adjust the values by dividing all values by the largest value. The input begins with an integer indicating the number of floating-point values that follow. Output each floating-point value with two digits after the decimal point, which can be achieved as follows: print('{:.2f}'.format(your_value)) what would be the code?

When analyzing data sets, such as data for human heights or for human weights, a common step is to adjust the data. This adjustment can be done by normalizing to values between 0 and 1, or throwing away outliers. For this program, adjust the values by dividing all values by the largest value. The input begins with an integer indicating the number of floating-point values that follow. Output each floating-point value with two digits after the decimal point, which can be achieved as follows: print('{:.2f}'.format(your_value)) what would be the code?

C++ Programming: From Problem Analysis to Program Design

8th Edition

ISBN:9781337102087

Author:D. S. Malik

Publisher:D. S. Malik

Chapter5: Control Structures Ii (repetition)

Section: Chapter Questions

Problem 14PE

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When analyzing data sets, such as data for human heights or for human weights, a common step is to adjust the data. This adjustment can be done by normalizing to values between 0 and 1, or throwing away outliers. For this program, adjust the values by dividing all values by the largest value. The input begins with an integer indicating the number of floating-point values that follow. Output each floating-point value with two digits after the decimal point, which can be achieved as follows:print('{:.2f}'.format(your_value)) Ex: If the input is: 5 30.0 50.0 10.0 100.0 65.0 the output is: 0.30 0.50 0.10 1.00 0.65 python
When analyzing data sets, such as data for human heights or for human weights, a common step is to adjust the data. This adjustment can be done by normalizing to values between 0 and 1, or throwing away outliers. For this program, adjust the values by dividing all values by the largest value. The input begins with an integer indicating the number of floating-point values that follow. Output each floating-point value with two digits after the decimal point, which can be achieved as follows:print('{:.2f}'.format(your_value))
When analyzing data sets, such as data for human heights or for human weights, a common step is to adjust the data. This can be done by normalizing to values between 0 and 1, or throwing away outliers. For this program, adjust the values by subtracting the smallest value from all the values. The input begins with an integer indicating the number of integers that follow. Ex: If the input is: 5 30 50 10 70 65 the output is: 20 40 0 60 55 The 5 indicates that there are five values in the list, namely 30, 50, 10, 70, and 65. 10 is the smallest value in the list, so is subtracted from each value in the list. For coding simplicity, follow every output value by a space, including the last one. 272772.1571588
Solve in python: When analyzing data sets, such as data for human heights or for human weights, a common step is to adjust the data. This adjustment can be done by normalizing to values between 0 and 1, or throwing away outliers. For this program, adjust the values by dividing all values by the largest value. The input begins with an integer indicating the number of floating-point values that follow. Output each floating-point value with two digits after the decimal point, which can be achieved as follows: print(f'{your_value:.2f}') Ex: If the input is: 5 30.0 50.0 10.0 100.0 65.0 the output is: 0.30 0.50 0.10 1.00 0.65 The 5 indicates that there are five floating-point values in the list, namely 30.0, 50.0, 10.0, 100.0, and 65.0. 100.0 is the largest value in the list, so each value is divided by 100.0.
When analyzing data sets, such as data for human heights or for human weights, a common step is to adjust the data. This adjustment can be done by normalizing to values between 0 and 1, or throwing away outliers. For this python program, adjust the values by dividing all values by the largest value. The input begins with an integer indicating the number of floating-point values that follow. Output each floating-point value with two digits after the decimal point, which can be achieved as follows:print('{:.2f}'.format(your_value))
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