问题
I am using the regression slope as follows to calculate the steepness (slope) of the trend.
Scenario 1:
For example, consider I am using sales figures (x-axis: 1, 4, 6, 8, 10, 15) for 6 days (y-axis).
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
X = [[1], [4], [6], [8], [10], [15]]
y = [1, 2, 3, 4, 5, 6]
regressor.fit(X, y)
print(regressor.coef_)
This gives me 0.37709497
Scenario 2:
When I run the same program for a different sale figure (e.g., 1, 2, 3, 4, 5, 6) I get the results as 1.
However, you can see that sales is much productive in scenario 1, but not in scenario 2. However, the slope I get for scenario 2 is higher than scenario 1.
Therefore, I am not sure if the regression slope captures what I require. Is there any other approach I can use instead to calculate the sleepness of the trend slope.
I am happy to provide more details if needed.
回答1:
I believe the problem is your variables are switched. If you want to track sales performance over time, you should perform the regression the other way around. You can invert the slopes you've calculated to get the correct values, which will show higher sales performance in case 1.
1 / 0.377 = 2.65
Here is a visualization of your data:
import matplotlib.pyplot as plt
days = [1,2,3,4,5,6]
sales1 = [1,4,6,8,10,15]
sales2 = [1,2,3,4,5,6]
df = pd.DataFrame({'days': days, 'sales1': sales1, 'sales2': sales2})
df = df.set_index('days')
df.plot(marker='o', linestyle='--')
来源：https://stackoverflow.com/questions/55274024/how-to-calculate-the-steepness-of-a-trend-in-python