sl-regression-quality 0.2.2

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

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 = pd.read_csv('Data_carne_new.csv') # your data
df = pd.read_csv('pruebaResolu.csv') # your data

alpha = 0.05 # significance level
dL = 1.055 # dL

# Total:
x=dataset.iloc[:27,1:2].values
y=dataset.iloc[:27,2:3].values
y_res = df.iloc[:,3:6].values
#--------------------------------------------------------------------------------
# 3) Run analysis
regression_quality(x,y,alpha,dL,y_res)