learn_measurement_error

source

learn_measurement_error(n=4000, beta=1.0, noise_x=1.0, seed=0)

Show classical measurement-error attenuation: noise in the regressor biases OLS to zero.

Simulates y = beta*x_true + e but regresses on a noisy observation x_obs = x_true + u. The OLS slope is attenuated toward zero by the reliability ratio var(x_true) / (var(x_true) + var(u)).

Parameters

Name	Type	Description	Default
n	int	Sample size.	`4000`
beta	float	True slope on the (unobserved) `x_true`.	`1.0`
noise_x	float	Standard deviation of the measurement noise added to `x_true` (drives the attenuation).	`1.0`
seed	int	Random seed.	`0`

Returns

Name	Type	Description
	SandboxResult	`df` (naive vs true slope), `fig`, `summary` and `topic`.

Examples

This sandbox simulates its own data, so the call needs no DataFrame:

import expdpy as ex

res = ex.learn_measurement_error()
res.fig