Statistics 101

A comprehensive guide to statistics—with information on collecting, measuring, analyzing, and presenting statistical data—continuing the popular 101 series.

Data is everywhere. In the age of the internet and social media, we’re responsible for consuming, evaluating, and analyzing data on a daily basis. From understanding the percentage probability that it will rain later today, to evaluating your risk of a health problem, or the fluctuations in the stock market, statistics impact our lives in a variety of ways, and are vital to a variety of careers and fields of practice.

Unfortunately, most statistics text books just make us want to take a snooze, but with Statistics 101, you’ll learn the basics of statistics in a way that is both easy-to-understand and apply. From learning the theory of probability and different kinds of distribution concepts, to identifying data patterns and graphing and presenting precise findings, this essential guide can help turn statistical math from scary and complicated, to easy and fun.

Whether you are a student looking to supplement your learning, a worker hoping to better understand how statistics works for your job, or a lifelong learner looking to improve your grasp of the world, Statistics 101 has you covered.

Table of Contents

1. THE BASICS OF STATISTICS
2. HOW STATISTICS ARE USED
3. KEY POINTS OF STATISTICAL ANALYTICS
4. MIXING UP THE TEST
5. KNOWING THE QUALIT Y OF YOUR DATA
6. MODEL ING RISK, MEASURING SAMPLES, AND PREDICTING
7. FREQUENCY DISTRIBUTIONS
8. DOT PLOTS, BAR CHARTS, HISTOGRAMS, FREQUENCY POLYGONS
9. MORE WAYS TO SEE NUMBERS-BASED DATA
10. THE MEAN, THE MEDIAN, AND THE MODE
11. THE RANGE AND INTERQUARTILE RANGE
12. MEAN DEVIATIONS AND VARIATIONS
13. THE LAW OF LARGE NUMBERS
14. EMPIRICAL PROBABILITY AND SUBJECTI VE PROBABILITY
15. YES OR NO
16. BASICS OF PROBABILITY DISTRIBUTIONS
17. ANALYZING PROBABILITY DISTRIBUTIONS
18. THE ROLL OF THE DICE
19. NORMAL DISTRIBUTION
20. THE CENTRAL L IMIT THEOREM
21. OUTLIERS ON THE BELL CURVE
22. L IMITED AND UNL IMITED DATA
23. VARIANCE AS A MEASURE OF RISK
24. SIZE MAT TERS
25. MEASURING DISTRIBUTION
26. WHAT ARE CONFIDENCE INTERVALS?
27. MEASURING CONFIDENCE INTERVALS
28. THE BASICS OF HYPOTHESIS TESTING
29. TAKING IT TO THE NEXT LEVEL
30. MEASURING LARGE SAMPLE POPULATION PROPORTIONS
31. THE HYPOTHESIS TEST
32. PAT TERNS IN DATA
33. PREDICT ING THE FUTURE
34. THE T-DISTRIBUT ION
35. GROUPS OF DATA
36. TESTS FOR TWO POPULATIONS
37. STATISTICS IN ACADEMIC RESEARCH
38. GET TING GOOD DATA
39. A REGRESSION EXAMPLE
40. WHAT REGRESSION DATA TABLES TELL US
41. DETERMINING THE CAUSES
42. CHI-SQUARE DISTRIBUT ION
43. ANOVA BASICS
44. ANOVA AT WORK
45. QUANTI TATI VE RESEARCH DESIGN
46. QUALIT Y OF THE DATA
47. QUANTIT Y AND SOURCING OF THE DATA
48. APPROPRIATE SURVEY DESIGN
49. THE ETHICS OF STAT IST ICS
50. BIG DATA, SUPERCOMPUTERS, AND ARTIFICIAL INTELLIGENCE

About the Author

David Borman has been involved in the financial markets and trading since 1999. He has professionally worked at Deutsche Bank, Merrill Lynch, TCM Custom House, Morgan Stanley, and Phillip Capital. He has been exposed to the trading and day trading of mutual funds, stocks, ETFs, Leveraged ETFs, Commodities, and Derivatives. He has worked right alongside the Risk Management Desk of a Singapore Based Futures Commission Merchant, where fifty million dollar margin calls were a daily occurrence. Within his own account, he has traded extensively using ETFs, precious metals, and currencies. He holds a BS in finance from Southern Illinois University, and a masters in accounting from DePaul University, and is working on his PhD in financial management from Northcentral University. When not trading, David finds the time shop for treasures at local antique shops. He is the author of Day Trading 101.