# <center><i class="fa fa-edit"></i> Smart Dispenser: Understand Possible Methods of Data Analysis </center>
###### tags: `Internship`
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**Goal:**
To gain a basic understanding of the data analysis techniques. Focus on any tests relevant to the current Smart Dispenser Project data analysis.
- [x] Overview
- [x] Statistical Assumptions
- [x] Regression Tests
- [x] Comparison Tests
- [x] Correlation Testa
- [x] Nonparametric Tests

**Resources:**
[Smart Dispenser Project](https://hackmd.io/@RayCheng/HJk_YpRou)
[新興國中開始畫圖數據觀察](/IQjmnYUHSyCnOfTo_CQd-Q)
[MongoDB 常用Query指令](/b2Y7lgR1RaeQyMHp9RGHdQ)
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:::spoiler **Expand Catalog**
[TOC]
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## Overview
- Dynamically propose recommendations for people to analyze and improve their experience
- Users: can further analyze the drinking habits of each member and make suggestions to improve health
- Managers: can provide recommendations for improving the UX design

## Data Analysis Methods
### Statistical Assumptions
1. **Independence of observations**: The observations or variables are not related (independent variables).
2. **Homogeneity of variance**: the variance is similar among all groups that are being compared. Otherwise it will limit the test’s effectiveness.
3. **Normality of data**: the data follows a normal distribution or bell curve (ONLY APPLIES to quantitative data)

### Regression Tests
- Test for **cause and effect**


### Comparison Tests
- Test for **differences among group means**
- t-test: for two groups
- ANOVA or MANOVA: for more than two groups


### Correlation Tests
- Tests for **possible correlations** among variables with cause and effect


### Nonparametric Test
- Do not make as many assumptions about the data
- Useful when one or more of the common statistical assumptions are violated
- Results are not as strong