“Receive physical documents from students, forward documents to committee, notify students service of outcomes of academic admissibility” are classified as ____________ step." (1分) Non-Value Adding (NVA) Data-driven decision making (DDDM) Value Adding (VA) Business Value Adding (BVA)

“Receive physical documents from students, forward documents to committee, notify students service of outcomes of academic admissibility”可以被归类为“非价值增加（Non-Value Adding, NVA）”步骤。这是因为这些步骤不会直接增加产品或服务的价值，而只是为了确保申请材料的完整性、准确性和合规性，并将这些材料传递给决策委员会进行评估。通知学生该决策的结果也是一个必要的步骤，但它并没有直接增加产品或服务的价值。

相关问题

用代码解决这个问题The program committee of the school programming contests, which are often held at the Ural State University, is a big, joyful, and united team. In fact, they are so united that the time spent together at the university is not enough for them, so they often visit each other at their homes. In addition, they are quite athletic and like walking. Once the guardian of the traditions of the sports programming at the Ural State University decided that the members of the program committee spent too much time walking from home to home. They could have spent that time inventing and preparing new problems instead. To prove that, he wanted to calculate the average distance that the members of the program committee walked when they visited each other. The guardian took a map of Yekaterinburg, marked the houses of all the members of the program committee there, and wrote down their coordinates. However, there were so many coordinates that he wasn't able to solve that problem and asked for your help. The city of Yekaterinburg is a rectangle with the sides parallel to the coordinate axes. All the streets stretch from east to west or from north to south through the whole city, from one end to the other. The house of each member of the program committee is located strictly at the intersection of two orthogonal streets. It is known that all the members of the program committee walk only along the streets, because it is more pleasant to walk on sidewalks than on small courtyard paths. Of course, when walking from one house to another, they always choose the shortest way. All the members of the program committee visit each other equally often. Input The first line contains the number n of members of the program committee (2 ≤ n ≤ 105). The i-th of the following n lines contains space-separated coordinates xi, yi of the house of the i-th member of the program committee (1 ≤ xi, yi ≤ 106). All coordinates are integers. Output Output the average distance, rounded down to an integer, that a member of the program committee walks from his house to the house of his colleague.

Sure, I can help you with that. Here's one possible solution in Python:

import math

n = int(input())
x_coords = []
y_coords = []

for i in range(n):
    x, y = map(int, input().split())
    x_coords.append(x)
    y_coords.append(y)

x_coords.sort()
y_coords.sort()

x_median = x_coords[n // 2]
y_median = y_coords[n // 2]

total_distance = 0

for i in range(n):
    total_distance += abs(x_coords[i] - x_median) + abs(y_coords[i] - y_median)

average_distance = total_distance // n

print(average_distance)

The idea here is to find the median x-coordinate and the median y-coordinate of all the houses, since these will be the coordinates of the "center" of the city. Then, we calculate the total distance each member of the program committee will have to walk to get to their colleague's house, by adding up the Manhattan distances between their own coordinate and the median coordinates. Finally, we divide the total distance by the number of committee members to get the average distance, rounded down to an integer. Note that we don't actually need to calculate the square root of the distances, since we only care about the integer value.

I hope this helps! Let me know if you have any questions.

he file athlete_events.csv contains 271116 rows and 15 columns. This is a historical dataset on the modern Olympic Games, including all the Games from Athens 1896 to Rio 2016. Each row corresponds to an athlete competing in an Olympic event (athlete events). The columns are explained in the following table: ID: Unique number for each athlete Name: Athlete's name Sex: M or F Age: Integer Height: in centimeters Weight: In kilograms Team: Team name NOC: National Olympic Committee 3-letter code Games: Year and season Year: integer Season: Summer or Winter City: Host city Sport: Sport Event: detailed event Medal: Gold, Silver, Bronze, or NA Please pick any column(s) in the data set to draw a figure from which you can draw meaningful conclusions. Download the athlete_events.csv file

I suggest we draw a histogram to analyze the distribution of age among athletes in the dataset.

To do so, we can use the `matplotlib` library in Python. Here's the code to read the data from the CSV file and plot the histogram:

import pandas as pd
import matplotlib.pyplot as plt

# Read the data from the CSV file
data = pd.read_csv("athlete_events.csv")

# Filter out rows with missing age values
data = data[data['Age'].notna()]

# Plot the histogram
plt.hist(data['Age'], bins=20, edgecolor='black')

# Add labels and title
plt.xlabel('Age')
plt.ylabel('Number of Athletes')
plt.title('Distribution of Age Among Olympic Athletes')

# Show the plot
plt.show()

This code reads the data from the CSV file and filters out rows where the age value is missing. It then plots a histogram of the age values using 20 bins and adds labels and a title. Finally, it shows the plot.

The resulting histogram will show us the distribution of age among athletes in the dataset, which can help us draw meaningful conclusions about the demographics of Olympic athletes over time.