# Statistics For Data Science with Python — Statistic Distribution (6/10)

Normal Distribution

### Scipy — Python for Data Science

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833456918/mFAc7FPy-.jpeg)

### Numpy— Python for Data Science

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833458479/bH15FtOJm.png)

### Type of Distributions

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833460057/XUortIt7m.jpeg)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833461909/yOjsMGIon.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833463343/ZTCAX78Qp.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833464778/qpv-SdkoF.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833466322/bQDQUMDqj.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833467803/RIoguDhW8.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833469247/MD00WmHCF.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833470586/S1wkZKX_M.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833471942/0llUieVTw.png)

### Statistics Skew

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833473260/BDQ8q5MGH.jpeg)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833474902/qJv313ipK.jpeg)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833480675/HeL_jx3Rk.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833482352/4cRrD_edg.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833483770/rTUgZ8vw-.jpeg)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833485102/3qgDiH13n.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833486415/z3oaSke3P.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833487816/QwWfCLiR_.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833489274/NJcouB9H7.png)

### Distribuição Normal Padronizada

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833490589/Kbf3QOJL7.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833492154/d-7BxSze_.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833493889/JyN3xa19L.png)

**Z-Score**

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833495196/YNmpqV1ei.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833496712/FNRZiO6lN.gif)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833497982/bMWREhVHe.gif)

### Teorema Central do Limite

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833503023/xxv6O7xQY.jpeg)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833504187/VcKGp-aV7.jpeg)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833505523/WzOBG9vBy.jpeg)

### SCIPY.Stats

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833506940/sau3JQJBU.jpeg)

### Naïve Bayes e distribuições

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833508757/9DY7PcTq9.jpeg)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833510134/7PYRJhUHX.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833511416/Zd0-rLuUw.jpeg)

### Conversão Atributos Categóricos => Numéricos Discreto

[**Google Colaboratory**  
*Edit description*colab.research.google.com](https://colab.research.google.com/drive/11ls3eL-LkHWOytWPvPC4Gv_xBFFNM8Ww#scrollTo=rkz8kpdZnRym&line=1&uniqifier=1 "https://colab.research.google.com/drive/11ls3eL-LkHWOytWPvPC4Gv_xBFFNM8Ww#scrollTo=rkz8kpdZnRym&line=1&uniqifier=1")[](https://colab.research.google.com/drive/11ls3eL-LkHWOytWPvPC4Gv_xBFFNM8Ww#scrollTo=rkz8kpdZnRym&line=1&uniqifier=1)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833513233/WGuMuXNMx.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833515502/5VA3FY_36.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833516874/VrMmp3esB.png)

### Aprendizagem Baseada em Distâncias — KNN

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833518302/Zkb8W3UOUY.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833519557/16FT_UlNK.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833520820/UdKWkrBMQ.png)

### Linear Regression

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833523162/BLjH0g6cn.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833524454/5EVBBirca.jpeg)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833525848/9hda-vyzZ.png)

### Skewed Data with Machine Learning

[**Transforming Skewed Data**  
towardsdatascience.com](https://towardsdatascience.com/transforming-skewed-data-73da4c2d0d16 "https://towardsdatascience.com/transforming-skewed-data-73da4c2d0d16")[](https://towardsdatascience.com/transforming-skewed-data-73da4c2d0d16)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833527537/TDuLIn2gQ.png)

### Neural Networks Initiators

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833529894/0Gc85uT3q.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833531942/b0rpDA-sX.gif)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833533691/IIZLlQfSB.gif)

### Initializers

*   [https://keras.io/api/layers/initializers](https://keras.io/api/layers/initializers/)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833535791/cSyiw1Eyp.png)

### Normality Tests

\- Parametric statistics: the data is in some distribution, usually the normal distribution.  
\- Non-parametric statistics: data is in another (or unknown) distribution  
\- If the data is “normal”, we use parametric statistics. Otherwise, we use non-parametric statistics.

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833537311/X-_IWYjJt.jpeg)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833539255/eaDX6sgQH.jpeg)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833540795/LGRbh2BGy.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833542364/BUpKPk_M9.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833544029/MvNr2hKJY.png)

### Shapiro-Wilk Test

Shapiro-Wilk Test  
p-value is used to interpret the statistical test.  
p <= alpha: rejects hypothesis, not normal  
p > alpha: don’t reject the hypothesis, it’s normal

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833545471/7N9b_Fwh4M.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662833547045/gixpI2XTn.png)

### Python Notebook Colab