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by: Colin Fritz

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# SCO 2550 Chapter 6 Notes SCO 2550

Colin Fritz
U of M

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These notes cover sampling distributions and the central limit theorem.
COURSE
Business Statistics: Data Sources, Presentation, and Analysis
PROF.
Kedong Chen
TYPE
Class Notes
PAGES
2
WORDS
CONCEPTS
SCO, 2550, sampling distribution
KARMA
25 ?

## Popular in Business Statistics: Data Sources, Presentation, and Analysis

This 2 page Class Notes was uploaded by Colin Fritz on Sunday October 9, 2016. The Class Notes belongs to SCO 2550 at University of Minnesota taught by Kedong Chen in Fall 2016. Since its upload, it has received 3 views. For similar materials see Business Statistics: Data Sources, Presentation, and Analysis in Business statistics at University of Minnesota.

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Date Created: 10/09/16
SCO 2550 Chapter 6 Colin Fritz Sampling Distribution Vocab: Target parameter – the unknown population parameter that we are interested in estimating Point estimator – a rule or formula that tells us how to use the sample data to calculate a single number that can be used as an estimate of the target parameter Interval estimator/ Confidence interval – formula to tell how to use the sample data to calculate an interval that estimates the target parameter Confidence coefficient - the probability that a randomly selected confidence interval encloses the population parameter/ the confidence level is also just the confidence coefficient as a percentage Statistical Inference – a statement about the overall true population parameters based on information that comes from a random sample  Sampling distribution of a statistic It’s made of all values of the sample statistic from all possible samples of a size n from the population This is the probability of the samples statistic There are 3 steps to get sampling distribution of the statistic ��� ̅ 1. Draw all possible samples of size n from the population 2. Calculate the sample statistic ��� ̅ for each sample 3. Put the sample statistics into a distribution (You can then describe the behavior of all the possible ��� ̅’s). Central limit theorem ��� ̅ (3 parts) Location (mean) – mean of the sampling distribution statistic ��� ̅ for all n Variability (standard deviation) – standard deviation of the sampling distribution statistic ��� ̅ Shape – For a known sigma the shape of the sampling distribution of the statistic will be normal if the population is normal Central limit theorem p (3 parts) Location (mean) – mean of the sampling distribution statistic p . This is the expected value of the sample proportions and it equals the population proportion p is a Minimal Variance Unbiased Estimator for p for all n Variability (standard deviation) – standard deviation of the sampling distribution statisticp σ = p 1−p ) ^p √ n Shape – The shape is normal if the n of the statistic is 15 or greater

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