Plotting regression line with log y scale-CodePudding

I have two plots I want to show (the original data and then its regression line). Whenever I run this code, the regression line doesn't run through the data at all-- I think this has to do with plotting the original data on a log-scale for the y axis (I tried including this when running polyfit, but I'm still having issues).

a = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
b = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])

plt.scatter(a, b)
plt.yscale('log')
slope, intercept = np.polyfit(a, np.log(b), 1)
plt.plot(a, (slope*a) intercept)
plt.show()

CodePudding user response：

You are fitting log(b) = slope * a intercept, which is equivalent to b = np.exp(slope*a intercept).

In matploltib, you either have to make the plot using a linear scale, whith log(b) as a variable:

import numpy as np
import matplotlib.pyplot as plt

a = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
b = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])

slope, intercept = np.polyfit(a, np.log(b), 1)

plt.figure()
plt.scatter(a, np.log(b))
plt.plot(a, (slope*a) intercept)
plt.show()

In this case, you do not use plt.yscale('log') as your axis is already scaled with respect to log(b).

On the other hand, you can plot the linear variables with a logarithmic scale:

import numpy as np
import matplotlib.pyplot as plt

a = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
b = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])

slope, intercept = np.polyfit(a, np.log(b), 1)

plt.figure()
plt.yscale('log')
plt.scatter(a, b)
plt.plot(a, np.exp((slope*a) intercept))
plt.show()

CodePudding user response：

import numpy as np
import matplotlib.pyplot as plt

def regression(m,x,b):
    return m * x   b

a = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
b = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])

slope, intercept = np.polyfit(a, np.log(b), 1)

plt.figure()
plt.scatter(a, np.log(b))
plt.plot(a, regression(a,slope,intercept))
plt.show()