# Week 4
## Day 1 - Weekly project presentation and Statistics
My Group Project: TED Talk
**1. Probability distribution**
* Probability distribution: likelihood of and event or outcome. It tell us which outcomes are more likely to happen, which outcomes are less likely to happen
* Event: an outcome or a collection of outcomes of a random experiment

**Two type of Event:**
* Dependent: flipping coin
* Independent: taking a ball from a bag of balls, teams spinning wheel

**Foure types of distribution**
* Uniform Distribution: finite number of value are equally likely to be observed. Example: flipping coin, rolling dice
* Binomal Distribution: Discrete probality of a sequence of experiments where each experiment product a binary outcome. Example: Number of customer like/dislike the new product
* Possion Distribution: Discrete probability distribution of the number of events occuring in a given time period. Example: Number of cars on the street at any given time.
* Normal Distribution: Continious probability distribution that follow bell curve and is symmetric about the mean. Example: Age, Height, Income distribution.

**Central Limit Theore**

You have a population with mean (μ) and std (σ), no matter the distribution shape is.
When you take sufficiently large random samples from the population

=> The distribution of the sample means will be approximately normally distributed.

=> The spread of the sampling distribution is related to the spread of the values in the population

=> Sample’s distribution is less spread than the population’s. Bigger sample leads to a smaller spread because of the formula

**2. Inference Statistics**
**Hypothesis Testing**
* A way to test the results of a survey or experiment to see if the results are meaningful.
** Step of Hypotheis testing:**
* Define null hypothesis and alternative hypothesis
* Set Significance level
* Select Statistical test
* Accept / Reject Null hypothesis

**Hypothesis Statement:**
* Null Hypothesis (H0): hypothesis that no change happens.
* Alternative Hypothesis (H1): Hypothesis that is opposite of H0

**Statistical Tests:**
* Z-Test
* T-test
* ANOVA
* Chi-Squared

# Day 2 - More Pandas exercise and Geograph

* Advance Pandas Exercise
* Python Binning
* Plotly
* GeoGraph

# Day 3 - Streamlit
* Doing some more exercise
* Cool framework to make data-driven application

# Day 4 - Google Data Studio
* Visualize COVID dataset using Google Data Studio

# Day 5 - Module Test
Doing module test