Class Notes (837,539)
LFS 252 (5)
Lecture

Chapter567.docx

5 Pages
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Department
Land & Food Systems
Course
LFS 252
Professor
Erin Friesen
Semester
Summer

Description
Chapter 5 Modeling Variation with Probability Why Randomness  make sure the collected samples represent the whole population Randomness: no predictable pattern occurs e.g. dice, heads or tails Probability: theoretical and empirical Theoretical probability: long run relative frequencies based on theory e.g. flipped coin: eventually the probability will be 50% and 50% - Probability will be in the between of 0 and 1 - Complement: that an event doesn’t happened – represent other events - Equally likely outcomes: each event is equally to occur e.g. each number on dice will have an equal chance to occur Probability rules: 1. And: the probability of both events occur 2. Or: the probability of at least one of the events must occur 3. Exclusive event: the probability of both events occur is 0 Conditional probability: Event A and B are associated Independent events: event A and B are independent-> B doesn’t influence A Empirical probability: short run relative frequencies - Laws of large numbers: large numbers of trials -> empirical approach to theoretical probability Sample space: a list that contains all possible( and equal likely) outcomes is called the sample space Cha 6 Modeling Random - Random variables can be discrete ( no decimal, can be listed or counted )or continuous (over a range) - Use a probability model to predict the likely hood of event  Normal model  Binomial model Chap 7 Sampling Sample: make decision toward to the population - Samples have to be: 1. part of the whole population 2. randomize: make sure on the average- make sure represent the whole population, avoid bias) 3. sample size: makes difference in sampling Population : a group of objects being studied Parameter: numerical value that characterize some aspents of the population e.g. probability Census: a survey in which every member of the population is measure  may be too expensive  takes time  destructive ( in order to destroy sth to get the data) nature:  ask someone to drink 100 cans of beer  kill all the animals, etc. Statistical inference: drawing conclusions about a population on the basis of observing only a small subset of the populations  involve uncertainly( use terms like: predict, maybe) Sampling bias: occur becoz the sample doesn’t represent of the sample 1. voluntary-response bias  online survey: only have the strong feeling will fill the survey  only provide the info of the people doing this survey  not scientific pool 2. non- response bias: people fail to answer a question of respond to a survey  provide wrong answers, people, with no matter what reasons, choose to provide wrong answers or no response Measurement bias: asking questions that do not produce a true answer - people may over estimate or underestimate - questions ( too much info) that guide people to answer the Q  has to be aware of the phrasing of Q too. - double barreled Qs: e.g. are you satisfied with UBC and ur faculty? How to know there is bias? - Only Small group of samples to reply the survey- bias - Whe
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