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Canada (509,690)
Administration (2,694)
ADM2304 (69)
Tony Quon (67)
Lecture 1

ADM2304 Lecture 1: 1
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Department
Administration
Course
ADM2304
Professor
Tony Quon
Semester
Fall

Description
REMEMBER! At Least 1: 1 P(0) At Least 2: 1 P(0) P(1) 2 or More: 1 P(0) P(1) More Than 2: 1 P(0) P(1) P (2) Less Than 2: P(0) + P(1) 2 or Less: P(0) + P(1) + P(2) types of data: > categorical data (qualitative), such as vehicle type > numerical data (quantitative) a. discrete (whole s, eggs) b. continuous (any , size of box) > Increasing sample size does not necessarily overcome bias unless the sample is representative of the population. > Need the sample to be large enough so that we can be fairly condent that it reects the true population > we have a population parameter (i.e., proportion of a population who would buy a given product) that we wish to estimate based on a sample statistic (i.e., proportion of the sample who would buy the product) We needs stats to be. a. Unbiased no tendency to underestimate or overestimate the parameter the long run average of the statistic over all possible samples is actually the parameter of interest e.g, Mean height of NBA players (the statistic) as an estimate for the average height of the population (the parameter) we cant determine height based only on NBA players e.g, Mean income of a sample of households in a single neighbourhood (the statistic) as an estimate of the mean income of the average income of households in Ottawa (the parameter) only based on one neighbourhood e.g, Proportion of a random sample of Canadians who would vote Conservative (the statistic) as an estimate of the proportion of Canadians who will vote Conservative at the next election (the parameter) assuming they will vote conservative again, your selecting conservatives and not just a random sample of people who dont have a party afliation b. Low variability doesnt vary dramatically from one sample to the next e.g, Average age of 100 incoming Ottawa university students (the statistic) as an estimate of the average age of incoming university students in Canada (the parameter) 100 is a small number of students and so theres high variability here (can be a wide variety of incomes here) e.g, Average income of a random sample of 100 Canadian tax returns (the statistic) as an
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