semvec/pypi-packages huggingface dataset:
SL-regression-quality 0.1.1
simple linear regression quality
License: MIT

# Simple Linear Regression:
An analysis of the quality of the regression is carried out



<p align="center">
    <img src="https://raw.githubusercontent.com/aplatag/project_SL_regression_quality/main/images/RlinealMW.jpeg" alt="methodology" width="500" >
</p>


## Table of Contents
- [Simple Linear Regression Assumptions](#Simple-Linear-Regression-Assumptions)
- [Simple linear regression Quality](#Simple-linear-regression-Quality)
- [Installation](#installation)
- [Code Example](#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:
```bash

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.
```bash

#--------------------------------------------------------------------------------
# 1) Load libraries:
import pandas as pd 
from main_routine import sl_regression_quality

#--------------------------------------------------------------------------------
# 2) Load data
dataset = pd.read_csv('../data/Data_carne_new.csv')
df = pd.read_csv('../data/pruebaResolu.csv')
alpha = 0.05
dL = 1.055

# 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
sl_regression_quality(x,y,alpha,dL,y_res)

```
pytedee-async 0.2.20
A Tedee Lock Client package
License: MIT
Requires Python: >=3.9

# Python Tedee Async Client Package

This is a Tedee Lock Client package. It is an async implementation of [joerg65's original package](https://github.com/joerg65/pytedee.git).

## Install:
### From pip
```
pip install pytedee-async
```

### Locally
```python
pipenv install -e .
# or
python3 setup.py install
```

## Try it out
- Generate personal key. Instructions: https://tedee-tedee-api-doc.readthedocs-hosted.com/en/latest/howtos/authenticate.html#personal-access-key
  Minimal scopes required for enable integration are:
    - Devices.Read
    - Lock.Operate
- with `example.py`: Create a file `config.json` next to `example.py`:
```json
  {
  "personalToken": "<your token>"
  }
```
cd into the directory of those to files and run
```
python example.py
```

- Initiate an instance of `TedeeClient`
```python
from pytedee_async import TedeeClient

pk = "<your PersonalKey>"
# through init
client = TedeeClient(pk) # is initialized with no locks
client.get_locks() # get the locks

# through classmethod
# will initialize directly with all locks you have
client = await TedeeClient.create(pk)
```

- the locks are avialable in a dictionary `client.locks_dict` with the key of the dict being the serial number of each lock, or in a list `client.locks`