**
Introduction to Statistics **
"Welcome back to School! Wish everyone in the new semester

More happiness! More Health! More Bright Future!"

-- Dr K
** • How Hot is Statistics/Big Data now: **
Statistics/Big Data is like teenage sex

Everyone talks about it

Nobody really knows how to do it

Everyone thinks everyone else is doing it

So everyone claims they are doing it....
For the textbooks of statistics, there are usually two classic textbooks:

1.

Elementary Statistics, 3rd CA Edition, by Triola, Pearson.

2.

Elementary Statistics: A Step By Step Approach 10th Edition
by Allan G. Bluman.

The following are the amazon links for the textbook and student solution manual.
You may consider buying together with other classmates if it's expensive.

**Download Lecture Notes**
**• **
Lecture01: Introduction, Motivation to Stats
**• **
Lecture02: Measure of Central Tendency, Variance, Measure of Position
**• **
Homework #1: from section 3.1, 3.2, 3.3 Central Tendency, Variance
**• **
Homework #1 Solution: from section 3.1, 3.2, 3.3
**• **
Lecture03: Addition rule of probability,Multiplication rule,Independent Events
**• **
Homework #2: Multiplication rule
**• **
Homework #2 Solution
**• **
Hw #3:permutation, combination
**• **
Hw #3:Solution
**• **
Lecture04: Counting rules,Permutation, Combination
**• **
Please Print out: Exam 1 formula sheet
**• **
Lecture05: probability distribution, binomial distribution
**• **
Hw #4: probability distribution
**• **
Hw #4 detail Solution
**• **
Lecture06: Normal distribution, approximation to normal distribution
**• **
Lecture07: Central Limit theorem, approximation to binomial distribution
**• **
Print out: Normal Distribution Table
**• **
Homework #5: Normal Distribution
**• **
HW #5: More detail
**• **
Homework #6: Central Limit Theorem
**• **
Hw #6: solution
**• **
Please print out: Exam 2 formula sheet
**• **
Lecture08: Confidence Interval for the mean
**• **
T Table
**• **
Homework #7: Confidence Interval from 7.1-7.3 solution
**• **
Hw #7: solution more details
**• **
Example: Margin of error in presidential election
**• **
Lecture09: Introduction to Hypothesis testing
**• **
Lecture10: 5 Steps for Hypothesis testing about the mean
**• **
Homework #8: Z test for Hypothesis testing with Solution
**• **
Homework #9: T test with solution
**• **
Items for Exam 3: formula sheet
**• **
T Table
**• **
Z Table
**Shopping Tips:** Don't buy stuff for cheap, eventually it will cost you more.

**• ** **How can you get some cash back when you shop online**?
Say, you want to buy some grocery from target online(3% cash back), or buy a ipad from apple store(1% cash back),
this website

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meanwhile, you can get some cash back(2%-20%) for the purchase.

That means,
when you want to buy something online, don't go to their site directly, but go to this site

Mr. Rebates first,
then find the website on

Mr. Rebates that you want to go , buy stuff through that link, then you will get some cash back from the purchase.
This can help you earn a few hundred dollars per year easily.

**Exam with solutions**
We provide the comprehensive final exams, and the detailed solutions with step by step explaination, plus the final grading rules!

**•**
Final Exam 1
Final 1 solutions
**•**
Final Exam 2
Final 2 solutions
**•**
Final Exam 3
Final 3 solutions
Final 4 solutions

**•** Practice Exam Review 1a
Practice Exam Review 1b
Practice Exam Review 2a

Practice Exam Review 2b
Practice Exam Review 3a
Practice Exam Review 3b

Here are 2 more useful and powerful big data applications:

• If you want to find the

**household income** around your neighborhood, or

**monthly rent, education level, race distribution, same-sex couple** around your neighborhood,
you can go to

the NYTimes webiste.

• If you want to find out whether there are

**Sex Crime Offenders** around your neighborhood,
you can go to

the MeganLaw Government webiste.

In case if you really want to invest and polish your data scientist/statistician skillsets, you can get the
following "

7-week free training called **Data Incubator**"

The Data Incubator is an intensive 7 week fellowship that prepares the best scientists and engineers with advanced degrees to work as data scientists and quants. It identifies fellows who already have the 90% difficult-to-learn skills and equips them with the last 10%: the tools and technology stack that make them self-sufficient, productive contributors. The program is free for fellows. Employers only pay a tuition fee if they successfully hire.

** Related links: **

** **
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