How do I calculate y for every value of x if the slope a set to 10 and the intercept b to 0?

Question

I have a small set of data . I used python3 to read it and created a scatter plot . My next task is to set the slope a to 10 and the intercept b to 0 and calculate y for every value of x . The task says I should not use any existing linear regression functions. I have been stuck for some time on this. How can I do that?

Answer 1

If your slope is already set to 10, I don't see why you need to use Linear Regression. I hope I'm not missing anything from your task.

However, keeping that aside if you need to get a list in python with all elements multiplied by your slope a then you can use a list comprehension to find this new list in the following way:

y_computed = [item*a for item in x]

Answer 2

You can literally just draw a line with a constant slope (10) on the same plot, then calculate the the predicted y-value based on that line "estimate" (you can also find the error of the estimate if you want). That be done using the following:

import numpy as np
from matplotlib import pyplot as plt


def const_line(x):
     y = 10 * x + 0  # Just to illustrate that the intercept is zero
     return y


x = np.linspace(0, 1)
y = const_line(x)
plt.plot(x, y, c='m')
plt.show()

# Find the y-values for each sample point in your data:
for x in data:
     const_line(x)