Main Commitment: I will read books about Statistics at least 30 minutes per day for the next 100 days.

Start Date: 2018-02-04

A companion project to 100 Days of Reading Paper.

## Rules

1. I will read books about Statistics at least 30 minutes every day.
2. I will tweet my progress every day, with the hashtag #100DaysOfReading #100DaysOfX #statistics #DataScience and note which day of the challenge I’m on.
3. I will track my progress here and push to GitHub.
4. I will only skip a day if something important comes up. And when I resume, I won’t count the day I skipped as one of my 100 days.
5. I will encourage and support at least two people each day in the #100DaysOfReading challenge on Twitter. I can read at most 5 tweets about #100DaysOfReading each day. Less is more. Don’t spend more than enough time on the social networking website.

3 Options

• Like tweets
• (optional) Looking at their projects and giving them feedback (no more than 10 minutes per day)

• Don’t skip two days in a row, and try not to skip more than 1 day in 2 weeks.

## Motivations

I forgot a lot of knowledge.

This time, I wwill read books again but not force myself to remember everything. The goal is to write down notes and save on the website http://notebook.yingjiehu.com

Searching notebook is my friend, not searching Google.

## LOG

### Day 1: 2018-02-04 Sunday

Today’s Progress (achievements and frustrations):

• Page 1-8

Thoughts and Emotions

I like the book. It is easy to read. The disadvantage is that it is toooooooo thick.

Tomorrow’s plan

### Day 2: 2018-02-06 Tuesday

Today’s Progress (achievements and frustrations):

• page 8-9

Thoughts and Emotions

• Some terminology names are different from other statistics books.

• $Y_i$: the value of the response variable in the $i$th trial

It used the word “trial”. I rarely meet the word in other statistics books.

• Some Math symbols conventions are different.

1. mean of $\varepsilon_i$

• this book: $E{\varepsilon_i}$
• other books: $E[\varepsilon_i]$
2. variance of $\varepsilon_i$

• this book: $\sigma^2{\varepsilon_i}$
• other books: $\sigma^2_{\varepsilon_i}$
3. covariance of $\varepsilon_i$ and $\varepsilon_j$

• this book: $\sigma{\varepsilon_i, \varepsilon_j}$
• other books: cov($\varepsilon_i, \varepsilon_j$)

Tomorrow’s plan

### Day 3: 2018-02-07 Wednesday

Today’s Progress (achievements and frustrations):

• page 10-11

Thoughts and Emotions

Most of the time spent on taking notes on page 10: Important Features of Model.

In short, “Important Features of Model” can be summarized with one line formula: $Y_i \stackrel{iid}{\sim} N(\beta_0+\beta_1X_i, \sigma^2)$

I understand that why the book used verbose language to describe this: the book is for the beginners. Again, different statistics books use different terms. It becomes readers’ responsibility to connect all of them. It is hard though.

### Day 4: 2018-03-22 Thursday

Today’s Progress (achievements and frustrations):

• page 12-16

Thoughts and Emotions

Easy to understand.

### Day 5: 2018-06-15 Friday

Today’s Progress (achievements and frustrations):

1 hour study

Thoughts and Emotions

Udacity gives different definition terms with the academic. I never saw these terms before. I decided not to memorize it because of lack of use in real life.

Tomorrow’s plan

• Study 1 hour