如何在线性回归方程中找到结果的平均值
How to find the average of results in a linear regression equation
我有方程式,我被要求求出 2010 年到 2015 年 x 的平均值。我开始了一个循环以首先获取 2010-2015 年的值,但我仍然不知道如何获得平均值这些值。以下是我目前所拥有的:
a = -22562.8
b = 11.24
i = 2010
while i <=2015:
sum_estimated_riders = (a + (i * b)) * 100000
print(sum_estimated_riders)
i = i + 1
你每次都覆盖sum_estimated_riders
。相反,在循环之前将其初始化为 0
并在循环内添加到它。然后除以迭代次数。
a = -22562.8
b = 11.24
i = 2010
sum_estimated_riders = 0
num_years = 0
while i <=2015:
sum_estimated_riders += (a + (i * b)) * 100000
num_years += 1
i = i + 1
mean_estimated_riders = sum_estimated_riders / num_years
print(mean_estimated_riders)
或者,您可以为每年创建一个 estimated_riders
列表。然后,使用sum()
计算总和并除以列表的长度。
estimated_riders = []
while i <= 2015:
estimated_riders.append((a + (i * b)) * 100000)
mean_estimated_riders = sum(estimated_riders) / len(estimated_riders)
或者,作为列表理解:
estimated_riders = [(a + (i * b)) * 100000 for i in range(2010, 2016)] # 2016 because range() excludes the end
mean_estimated_riders = sum(estimated_riders) / len(estimated_riders)
您可以为此使用 numpy.mean()
制作一个列表,将每个值附加到它,然后取平均值。
import numpy as np
estimated_riders = []
a = -22562.8
b = 11.24
i = 2010
while i <=2015:
sum_estimated_riders = (a + (i * b)) * 100000
estimated_rides.append(sum_estimated_riders)
i = i + 1
avg = np.mean(estimated_riders)
print(avg)
我有方程式,我被要求求出 2010 年到 2015 年 x 的平均值。我开始了一个循环以首先获取 2010-2015 年的值,但我仍然不知道如何获得平均值这些值。以下是我目前所拥有的:
a = -22562.8
b = 11.24
i = 2010
while i <=2015:
sum_estimated_riders = (a + (i * b)) * 100000
print(sum_estimated_riders)
i = i + 1
你每次都覆盖sum_estimated_riders
。相反,在循环之前将其初始化为 0
并在循环内添加到它。然后除以迭代次数。
a = -22562.8
b = 11.24
i = 2010
sum_estimated_riders = 0
num_years = 0
while i <=2015:
sum_estimated_riders += (a + (i * b)) * 100000
num_years += 1
i = i + 1
mean_estimated_riders = sum_estimated_riders / num_years
print(mean_estimated_riders)
或者,您可以为每年创建一个 estimated_riders
列表。然后,使用sum()
计算总和并除以列表的长度。
estimated_riders = []
while i <= 2015:
estimated_riders.append((a + (i * b)) * 100000)
mean_estimated_riders = sum(estimated_riders) / len(estimated_riders)
或者,作为列表理解:
estimated_riders = [(a + (i * b)) * 100000 for i in range(2010, 2016)] # 2016 because range() excludes the end
mean_estimated_riders = sum(estimated_riders) / len(estimated_riders)
您可以为此使用 numpy.mean() 制作一个列表,将每个值附加到它,然后取平均值。
import numpy as np
estimated_riders = []
a = -22562.8
b = 11.24
i = 2010
while i <=2015:
sum_estimated_riders = (a + (i * b)) * 100000
estimated_rides.append(sum_estimated_riders)
i = i + 1
avg = np.mean(estimated_riders)
print(avg)