sl-regression-quality 0.3.3

simple linear regression quality

Simple Linear Regression:

An analysis of the quality of the regression is carried out

Table of Contents

• Simple Linear Regression Assumptions
• Simple linear regression Quality
• Installation
• Code Example

Simple Linear Regression Assumptions:

1. Outlier: The term anomaly indicates that there is data that deviates significantly from the rest.
2. Normality: refers to the normal distribution of errors or residuals.
3. Homoscedasticity: is another simple linear regression assumption and indicates whether the variance of the residuals is the same across different groups in the database.
4. Independence: refers to the absence of temporal correlation between residuals.
5. Linearity: is associated with the presence of a constant change of the variable to be predicted with respect to the predictor.

Simple linear regression Quality

Database structure

The first columns of the database correspond to the repetitions performed for variable x. Once all repetitions for variable x are completed, the repetitions for variable y are recorded. The following image shows an example of how to organize the data before using the "regression_quality" program. The database includes three repetitions for variable "x" and four for variable "y."

Installation

Instructions on how to install the project. For example:

pip install sl-regression-quality

Code Example

For instance, the following code can be executed in Google Colab. Simply copy and paste it into a new Colab notebook.

#--------------------------------------------------------------------------------
# 1) Load libraries:
import pandas as pd
from sl_regression_quality.main_routine import regression_quality
from sl_regression_quality.load_data import load_csv

#--------------------------------------------------------------------------------
# 2) Load data
#dataset = load_csv('data_x_y_example.csv') # example data (uncomment line)
#dataset = pd.read_csv('Data_y_full.csv') # example for your data (uncomment line)


number_repetitions_x = 3 # number of repetitions in x
alpha = 0.05 # significance level
dL = 1.055 # dL
dU = 1.211 # dU

#--------------------------------------------------------------------------------
# 3) Run analysis
regression_quality(dataset,number_repetitions_x,alpha,dL,dU)