Summary of the book Stats: Data and Models Global edition 4th edition. Can be used for the course Probability for the first year (second module) of the program International Business Administration at the University of Twente. Contains chapters 5 (5.3 and 5.4), 13, 14, 15, 16 and 17. Can also use b...
Hoofstukken 5 (5.3 and 5.4), 13, 14, 15, 16 and 17
June 16, 2020
7
2019/2020
Summary
Subjects
university of twente universiteit twente international business administration iba 1st year iba 2nd module iba probability stats data and calf models velleman bock international business global edition isbn 10 1292101636 isbn 13 9781292101637
Stats: Data and Models
Probability
Global Edition
4th edition
De Veaux, Velleman and Bock
,Index
- Chapter 5 (5.3 and 5.4)
- Chapter 13
- Chapter 14
- Chapter 15
- Chapter 16
- Chapter 17
, Chapter 5 (5.3 and 5.4)
Z-score = an indication of how unusual a value is, tells how far it is from the mean
Normal models = appropriate for distributions whose shapes are unimodal and roughly
symmetric N = (, )
Parameters = numbers that we choose to help specify the model, they are part of the model,
they don’t come from the data (always use Greek
Statistics = summaries of data (usually written with Latin letters)
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Z = y− ❑
Standard Normal model/standard Normal distribution = the normal model with mean 0 and
standard deviation 1
Normality assumption = the assumption that the distribution is normal
Nearly Normal condition = the shape of the data’s distribution is unimodal and symmetric
check by making a histogram
68-95-99.7 rule
- 68% of the values fall within 1 standard deviation of the mean
- 95% of the values fall within 2 standard deviations of the mean
- 99.7% (almost all) of the values fall within 3 standard deviations of the mean
Normal percentiles = shown in the Z-score table
Chapter 13
Random phenomenon = a situation in which we know what outcomes can possibly occur,
but we don’t know which particular outcome will happen
Trial = each occasion upon which we observe a random phenomenon
(Trial) outcome = the value of the random phenomenon
Event = combined outcomes
Sample space = the collection of all possible outcomes S
Law of Large Numbers (LLN) = as we repeat a random process over and over, the proportion
of times that an event occurs does settle down to one number = the probability of the event
Assumption on LLN
1. The random phenomenon must not change, the outcomes must be the same
2. The event must be independent
Independence = the outcome of one trial doesn’t affect the outcomes of the other
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