This document provides a cheat sheet on key concepts and terminology related to experimental design and statistics. It defines factors, levels, runs, responses, and other common experimental design terminology. It also outlines different types of experiments, principles of good experimental design, different data types, common statistical measures and graphs, and more. The cheat sheet is intended to help users understand the basic building blocks and concepts needed to design and analyze experiments.

1. (Cheat Sheet)
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Ǧ Š‘™•–Š‡ ƒ—•‡ƒ†‡ˆˆ‡ – ”‡Žƒ–‹‘•Š‹’„‡–™‡‡ƒ†Ǥ
Ǧ ‡Ž’•†‡–‡”‹‡–Š‡’”‘’‡”•‡––‹‰• ȋŽ‡˜‡Ž•Ȍˆ‘”‘—”‹’—–•ȋȌ‹‘”†‡”–‘‘’–‹‹œ‡‘—”‘—–’—–ȋȌǤ
Key Terminology:
Ǧ ƒ –‘”•ȋšȌȂ Š‡‹†‡’‡†‡–˜ƒ”‹ƒ„Ž‡•„‡‹‰—•‡†ȋ‡Ǥ‰Ǥ–‡’‡”ƒ–—”‡ȌǤ
Ǧ ‡˜‡Ž•Ȃ Š‡˜ƒ”‹‘—••‡––‹‰•ˆ‘”–Š‡ˆƒ –‘”•ȋ‡Ǥ‰Ǥ͵ͲͲιǡͷͲͲιȌǤ
Ǧ —Ȃ •‡–‘ˆ‡š’‡”‹‡–ƒŽ ‘†‹–‹‘•Ǥȋš’‡”‹‡–•Šƒ˜‡—Ž–‹’Ž‡ǤȌ
Ǧ ‡•’‘•‡ȋ›ȌȂ Š‡”‡•—Ž–ˆ”‘ƒ‡š’‡”‹‡–ƒŽ”—ȋ‡Ǥ‰Ǥƒ–‡”‹ƒŽ•–”‡‰–ŠȌǤ
Ǧ ‡’Ž‹ ƒ–‹‘Ȃ Š‡”‡’‡–‹–‹‘‘ˆ‡š’‡”‹‡–ƒŽ”—•ǤȋŠƒŽŽ‡‰‡•–Š‡”‡•—Ž–ǤȌ
Common Types of Experiments:
Ǧ —ŽŽ ƒ –‘”‹ƒŽ•Ȃ —•‡ʹǦͷ‹’—–˜ƒ”‹ƒ„Ž‡•™‹–ŠƒŽŽ ‘„‹ƒ–‹‘•‘ˆŽ‡˜‡Ž•ȋ‘”•‡––‹‰•ȌǤ
Ǧ ”ƒ –‹‘ƒŽ ƒ –‘”‹ƒŽ•Ȃ —•‡ͶǦͳͷ‹’—–˜ƒ”‹ƒ„Ž‡•ƒ†ƒˆ”ƒ –‹‘‘ˆ ‘„‹ƒ–‹‘•Ǥ
General Notation for Full Factorial Design (2k):
Ǧ α͓‘ˆ‹’—–˜ƒ”‹ƒ„Ž‡•
Ǧ ʹα͓‘ˆŽ‡˜‡Ž•—•‡†ˆ‘”‡ƒ Šˆƒ –‘”
Principles of Good Experimental Design:
- ƒ†‘‹œƒ–‹‘ ‘ˆ”—•–‘”‡‘˜‡„‹ƒ•ƒ†•’”‡ƒ†‘‹•‡
Ǧ ‡’Ž‹ ƒ–‹‘ ‘ˆ–Š‡‡š’‡”‹‡––‘ ŠƒŽŽ‡‰‡‘”•–”‡‰–Š‡–Š‡˜ƒŽ‹†‹–›‘ˆ”‡•—Ž–•Ǥ
Ǧ ‘‹–‘”‹‰‘ˆ‘‹•‡.
- ‘Ž†‹‰‘–Š‡”ˆƒ –‘”• ‘•–ƒ–. ȋŠ‘•‡–Šƒ–ƒ”‡‘–ƒˆ‘ —•‘–Š‡‡š’‡”‹‡–ǤȌ
Data Types:
- Attribute Data – Qualitative:
ȗ‡š–ƒ–ƒȂ ‡Ǥ‰Ǥ›‡•Ȁ‘ǡ’ƒ••Ȁˆƒ‹Žǡƒ’’”‘˜‡Ȁ”‡Œ‡ –ǥ
- Variable Data – Quantitative:
ȗ‹• ”‡–‡Ȃ ‘—–‡†—„‡”•Ȃ ‡Ǥ‰Ǥ͓‘ˆ†‡ˆ‡ –•ȋ͹ͶȌǡ͓‘ˆ —•–‘‡””‡–—”•ȋͳ͵Ȍ
ȗ‘–‹—‘—•Ȃ †‡ ‹ƒŽ—„‡”•Ȃ ‡Ǥ‰Ǥ–‹‡ȋͳʹǣʹͶǣͷͻȌǡ‘‡›ȋ̈́ͳ͹ǤͶ͵ͷͶȌǡ’”‡••—”‡ȋʹͷǤͶͶͷ͵ͶŽ„•ǤȌ
Types of Statistics:
Ǧ ‡• ”‹’–‹˜‡–ƒ–•Ȃ •‡†–‘†‡• ”‹„‡ƒ†•—ƒ”‹œ‡†ƒ–ƒǤ
Ǧ ˆ‡”‡–‹ƒŽ–ƒ–•Ȃ ”ƒ™‹‰ ‘ Ž—•‹‘•ƒ„‘—–ƒ’‘’—Žƒ–‹‘ǡ™Š‡•ƒ’Ž‡†ƒ–ƒ‹•—•‡†Ǥ
ȗ•™‡‰ƒ–Š‡”†ƒ–ƒǡ™‡™‘”™‹–Š•ƒ’Ž‡•Ǥ
ȗ‡‡‡† ‘ˆ‹†‡ ‡–Šƒ–‘—”•ƒ’Ž‡”‡’”‡•‡–•–Š‡’‘’—Žƒ–‹‘Ǥ
Measures of Central Tendency:
Ǧ ‡ƒȂ Š‡ƒ˜‡”ƒ‰‡Ǥ
Ǧ ‡†‹ƒȂ Š‡‹††Ž‡˜ƒŽ—‡Ǥ
Ǧ ‘†‡Ȃ Š‡‘•–ˆ”‡“—‡–Ž›‘ —””‹‰˜ƒŽ—‡Ǥ
Ǧ ”‹‡†‡ƒȂ ‘’”‘‹•‡„‡–™‡‡–Š‡‡ƒƒ†‡†‹ƒǡ”‡‘˜‡••‘‡‘—–Ž‹‡”•–Š‡ƒ˜‡”ƒ‰‡•Ǥ
Measures of Variation:
Ǧ ƒ‰‡Ȃ ‹ˆˆ‡”‡ ‡„‡–™‡‡–Š‡Žƒ”‰‡•–ƒ†•ƒŽŽ‡•–˜ƒŽ—‡Ǥ
Ǧ –‡”“—ƒ”–‹Ž‡ƒ‰‡Ȃ ‹ˆˆ‡”‡ ‡„‡–™‡‡–Š‡͹ͷ–Šƒ†ʹͷ–Š’‡” ‡–‹Ž‡Ǥ
Ǧ –ƒ†ƒ”†‡˜‹ƒ–‹‘Ȃ ˜‡”ƒ‰‡†‡˜‹ƒ–‹‘‘ˆ˜ƒŽ—‡•ˆ”‘–Š‡‡ƒǤ
Ǧ ƒ”‹ƒ ‡Ȃ ˜‡”ƒ‰‡•“—ƒ”‡††‡˜‹ƒ–‹‘‘ˆ˜ƒŽ—‡•ˆ”‘–Š‡‡ƒǤ
Basic Graphs:
Ǧ ‹•–‘‰”ƒȂ •Š‘™• ‡–”ƒŽ–‡†‡ ›ƒ†˜ƒ”‹ƒ–‹‘™‹–Š‹ƒsingle †‹•–”‹„—–‹‘Ǥ
Ǧ ‘–’Ž‘– Ȃ •‹‹Žƒ”–‘ƒŠ‹•–‘‰”ƒǡ„—–•Š‘™•‡ƒ Š˜ƒŽ—‡ƒ•ƒ‹†‹˜‹†—ƒŽ’‘‹–Ǥ
Ǧ ‘š’Ž‘–Ȃ •Š‘™• ‡–”ƒŽ–‡†‡ ›ƒ†ƒ˜ƒ”‹ƒ–‹‘™‹–Š‹several †‹•–”‹„—–‹‘•ǡ‘–Œ—•–‘‡Ǥ
Ǧ ‹‡Ǧ‡”‹‡•Ž‘–Ȃ •Š‘™• ”‹–‹ ƒŽ“—ƒŽ‹–›‡ƒ•—”‡‡–•‘˜‡”–‹‡Ǥ
Ǧ ƒ––‡”’Ž‘–Ȃ •Š‘™•–Š‡”‡Žƒ–‹‘•Š‹’„‡–™‡‡–™‘˜ƒ”‹ƒ„Ž‡•Ǥ
Data Measurement Scales:
Ǧ ‘‹ƒŽȂ ƒ‘–„‡‘”†‡”‡†Ǣ‘ƒ”‹–Š‡–‹ ƒ„‡’‡”ˆ‘”‡†Ǥ‡Ǥ‰Ǥ ‹–›ȋ‡–”‘‹–ǡŽ‡˜‡Žƒ†ǡ‡ƒ––Ž‡ȌǤ
Ǧ ”†‹ƒŽȂ ƒ„‡‘”†‡”‡†Ǣ†‹ˆˆ‡”‡ ‡•„‡–™‡‡˜ƒŽ—‡•‡ƒ‹‰Ž‡••Ǥ‡Ǥ‰Ǥ–ƒ•–‡ȋ„ƒ†ǡ‘ƒ›ǡ‰‘‘†ȌǤ
Ǧ –‡”˜ƒŽȂ ƒ„‡‘”†‡”‡†Ǣ†‹ˆˆ‡”‡ ‡•„‡–™‡‡˜ƒŽ—‡•‡ƒ‹‰ˆ—Žȋ‘–”ƒ–‹‘•ȌǤ‡Ǥ‰Ǥ–‡’ȋͲ‘ǡͳͲ‘ ǡʹͲ‘ȌǤ
Ǧ ƒ–‹‘Ȃ ƒ„‡‘”†‡”‡†Ǣ”ƒ–‹‘•‡ƒ‹‰ˆ—ŽǢœ‡”‘‹†‹ ƒ–‡•ƒƒ„•‡ ‡Ǥ‡Ǥ‰Ǥ™‡‹‰Š–ȋͲ‰ǡʹͷ‰ǡͷͲ‰ȌǤ
Types of Sampling Measurement Errors:
Ǧ ƒ’Ž‹‰””‘”Ȃ ‹ˆˆ‡”‡ ‡•ƒ‘‰•ƒ’Ž‡•†”ƒ™ƒ–”ƒ†‘ȋDzŽ— ‘ˆ–Š‡†”ƒ™dzȌǤ
Ǧ ƒ’Ž‹‰‹ƒ•Ȃ Žƒ ‘ˆ”ƒ†‘•ƒ’Ž‡•ȋ‡Ǥ‰ǤŠ‡‹‰Š–‘ˆ„ƒ•‡–„ƒŽŽ’Žƒ›‡”•‘Ž›ȌǤ
Ǧ ‡ƒ•—”‡‡–””‘”Ȃ ••—‡•™‹–Š‘—”‡ƒ•—”‡‡–•›•–‡•Ǥ
Ǧ ‡ƒ•—”‡‡– ˜ƒŽ‹†‹–›Ȃ ‘–‡ƒ•—”‹‰™Šƒ–‹–‹•‹–‡†‡†ȋ‡Ǥ‰Ǥ–‡’‡”ƒ–—”‡‡ƒ”ƒˆ—”ƒ ‡ȌǤ
‡Ž’•ƒ•™‡”ǣ“Is the sample a fair representation of the population?”
Hypotheses:
Ǧ —ŽŽ ›’‘–Š‡•‹• ȋ ‘ȌȂ ƒ••—‡•†‹ˆˆ‡”‡ ‡•ȋ–Š‡•ƒ‡Ȍǡp-value εͲǤͲͷ
Ǧ Ž–‡”ƒ–‹˜‡ ›’‘–Š‡•‹• ȋ ƒȌȂ •–ƒ–‡•–Š‡”‡‹•ƒ†‹ˆˆ‡”‡ ‡ǡp-value δͲǤͲͷ
Tests for Normal Data (“t-tests”):
Ǧ ͳǦƒ’Ž‡–Ǧ‡•–Ȃ •–—†›‘‡•ƒ’Ž‡ǯ• ‡ƒƒ‰ƒ‹•–ƒ–ƒ”‰‡–Ǥ
Ǧ ʹǦƒ’Ž‡–Ǧ‡•–Ȃ •–—†›‡ƒ•ˆ”‘–™‘†‹ˆˆ‡”‡–•ƒ’Ž‡•Ǥ
Ǧ ‡•–Ȃ •–—†›‡ƒ•ˆ”‘‘”‡–Šƒ–™‘•ƒ’Ž‡•Ǥ
Ǧ ƒ‹”‡†–Ǧ‡•–Ȃ •–—†›’ƒ‹”‡††ƒ–ƒ ȋ‡Ǥ‰Ǥ•ƒ‡’ƒ”–„‡ˆ‘”‡Ȁƒˆ–‡”‹’”‘˜‡‡–ȌǤ
Normal vs Non-Normal Data
- ›’‘–Š‡•‹•–‡•–•™‹–Š†ƒ–ƒ—•‡–Š‡mean ˆ‘” ‡–”ƒŽ–‡†‡ ›
- ›’‘–Š‡•‹•–‡•–•™‹–ŠǦ†ƒ–ƒ—•‡–Š‡median ˆ‘” ‡–”ƒŽ–‡†‡ ›

2. What is Analytics?
Types of Analytics
R Descriptive Analytics What happened?
R Predictive Analytics What might happen?
R Prescriptive Analytics What should we do?
Lifecycle of Analytics (“CRISP-DM”)
R Business Understanding Define the business problem.
R Data Understanding Identify available data and gaps in data.
R Data Preparation Clean and prepare the data.
R Modeling Build predictive models.
R Evaluation Evaluate how the models perform.
R Deployment Start using the chosen model.
BUSINESS ANALYTICS (Cheat Sheet)
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data
data
data
business
insight
Microsoft Excel
Allows you to explore/analyze smaller data sets
Tableau Desktop (or Power BI)
Allows you to visualize your data with dashboards
Python Language (or R)
Allows you to build models to make predictions
Structured Query Language (SQL)
Allows you to communicate and interact with databases
BIG DATA
BASIC CONCEPTS POPULAR TOOLS
Big data is so large that “it requires the use of new technical architectures …
to enable insights that unlock new sources of business value.” (McKinsey)
3 V’s of Big Data (Defining Characteristics)
Volume Velocity Variety
CAREERS IN ANALYTICS
Common Job Titles
R Business Analyst
R Business Intel. Analyst
R Analytics Manger
R Data Analyst
R Data Scientist *
R …
* Most people feel this job is more technical.
Most job postings ask about software, so:
R Select a tool from above
R Download a free trial.
R Get a pizza!
R Spend a weekend to learn.
R State you have “Experience with…” on your resume.

3. (Cheat Sheet)
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Ȃ ‘”–›‘—””‡•—Ž–•Ǥ
Command Description
SELECT —‡”›‹ˆ‘”ƒ–‹‘‹ƒ†ƒ–ƒ„ƒ•‡Ǥ
INSERT ††”‡ ‘”†•–‘ƒ†ƒ–ƒ„ƒ•‡Ǥ
UPDATE ’†ƒ–‡‹ˆ‘”ƒ–‹‘‹ƒ†ƒ–ƒ„ƒ•‡Ǥ
DELETE ‡Ž‡–‡‹ˆ‘”ƒ–‹‘‹ƒ†ƒ–ƒ„ƒ•‡Ǥ
Category Objective Examples
ƒ–ƒ‡ˆ‹‹–‹‘ƒ‰—ƒ‰‡ȋȌ ‡ˆ‹‡–Š‡†ƒ–ƒ„ƒ•‡Ǥ ǡǡ
ƒ–ƒƒ‹’—Žƒ–‹‘ƒ‰—ƒ‰‡ȋȌ ƒ‹’—Žƒ–‡–Š‡†ƒ–ƒǤ ǡ ǡ
ƒ–ƒ‘–”‘Žƒ‰—ƒ‰‡ȋȌ ƒƒ‰‡’‡”‹••‹‘•Ǥ ǡ
”ƒ•ƒ –‹‘‘–”‘Žƒ‰—ƒ‰‡ȋȌ ƒƒ‰‡–”ƒ•ƒ –‹‘•Ǥ ǡ
ƒ’‹–ƒŽ‹œ‡ ‘ƒ†•ˆ‘””‡ƒ†ƒ„‹Ž‹–›Ȃ ǡ ǡ‡– Ǥ
•‡‡™Ž‹‡•ƒ†‹†‡–‹‰ˆ‘””‡ƒ†ƒ„‹Ž‹–›Ȃ ƒ˜‘‹†Ž‘‰•–ƒ–‡‡–•‘‘‡Ž‹‡
ˆ’‘••‹„Ž‡ǡ—•‡‘Ž›•–ƒ†ƒ”†ˆ— –‹‘•‹•–‡ƒ†‘ˆ˜‡†‘”ˆ— –‹‘•ˆ‘”’‘”–ƒ„‹Ž‹–›
‘•‹†‡”‹ ‘”’‘”ƒ–‹‰ ‘‡–•–‘’”‘˜‹†‡‘”‡ ‘–‡š–
‰‡‡”ƒŽǡ—•‡•‹‰Ž‡“—‘–‡•ˆ‘”˜ƒŽ—‡•ƒ††‘—„Ž‡“—‘–‡•ˆ‘”–ƒ„Ž‡•ƒ† ‘Ž—•Ǥ
‡‡„‡”ǡ–Š‡”‡‹‰Š–„‡‘”‡–Šƒ‘‡™ƒ›–‘ƒ ‘’Ž‹•Šƒ“—‡”›
‡Ž‡ –‘Ž›–Š‡ ‘Ž—•›‘—‡‡†Ǥ —‡”›‹‰—‡ ‡••ƒ”›†ƒ–ƒ ‘•—‡•”‡•‘—” ‡•Ǥ
•‡ –‘•ƒ’Ž‡“—‡”›”‡•—Ž–•Ǥ‘—ƒ›ˆ‹†–Š‡“—‡”›‡‡†•”‡ˆ‹‡‡–Ǥ
•‡™‹Ž† ƒ”†•ƒ––Š‡‡†‘ˆƒ’Š”ƒ•‡‘Ž›Ǥ ‹Ž† ƒ”†•‰‡‡”ƒ–‡™‹†‡•‡ƒ” Š‡•Ǥ
˜‘‹† ‹ˆ’‘••‹„Ž‡ǤŠ‹• ƒ”‡“—‹”‡Ž‘–•‘ˆ’”‘ ‡••‹‰’‘™‡”Ǥ
—Žƒ”‰‡“—‡”‹‡•†—”‹‰‘ˆˆǦ’‡ƒŠ‘—”•ǤŠ‹•‹‹‹œ‡•‹’ƒ ––‘‘–Š‡”•Ǥ
–ƒ„Ž‡̴ƒ‡
‘Ž—ͳ α˜ƒŽ—‡ͳǡ ‘Ž—ʹα ˜ƒŽ—‡ʹǥ
‘†‹–‹‘Ǣ
–ƒ„Ž‡̴ƒ‡ ȋ ‘Ž—ͳǡ ‘Ž—ʹǡ ‘Ž—͵ǡǤǤǤȌ
ȋ˜ƒŽ—‡ͳǡ ˜ƒŽ—‡ʹǡ ˜ƒŽ—‡͵ǡǤǤǤȌǢ
‘Ž—ͳǡ ‘Ž—ʹǡǥ
–ƒ„Ž‡
ORDER BY ‘Ž—ͳǡ ‘Ž—ʹǥASC ȁDESC Ǣ
‘Ž—ͳǡ ‘Ž—ʹǥ
–ƒ„Ž‡Ǣ
Ȃ •‡”–•‡™”‡ ‘”†•‹–‘ƒ‡š‹•–‹‰–ƒ„Ž‡Ǥ
Ȃ ‘†‹ˆ›‡š‹•–‹‰”‡ ‘”†•‹ƒ–ƒ„Ž‡Ǥ
‘Ž—ͳǡSUM(column2)
–ƒ„Ž‡
GROUP BY ‘Ž—ͳǢ
Ȃ —ƒ”‹œ‡•›‘—””‡•—Ž–•‹–‘‰”‘—’•Ǥ