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  • Sampling Distribution
Behavior of Sample Mean (1 of 3)
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Apply the sampling distribution of the sample mean as summarized by the Central Limit Theorem (when appropriate). In particular, be able to identify unusual samples from a given population.

Subject:
Statistics and Probability
Material Type:
Module
Provider:
Carnegie Mellon University
Provider Set:
Open Learning Initiative
Date Added:
02/28/2018
Statistics Course Content
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Introductory statistics course developed through the Ohio Department of Higher Education OER Innovation Grant. The course is part of the Ohio Transfer Module and is also named TMM010. For more information about credit transfer between Ohio colleges and universities please visit: www.ohiohighered.org/transfer.Team LeadKameswarrao Casukhela                     Ohio State University – LimaContent ContributorsEmily Dennett                                       Central Ohio Technical CollegeSara Rollo                                            North Central State CollegeNicholas Shay                                      Central Ohio Technical CollegeChan Siriphokha                                   Clark State Community CollegeLibrarianJoy Gao                                                Ohio Wesleyan UniversityReview TeamAlice Taylor                                           University of Rio GrandeJim Cottrill                                             Ohio Dominican University

Subject:
Mathematics
Statistics and Probability
Material Type:
Full Course
Provider:
Ohio Open Ed Collaborative
Date Added:
04/17/2018
Statistics Course Content, Sampling Distributions of Sample Mean and Sample Proportion, Sampling Distributions and Central Limit Theorem
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Ideally a census will be able to provide answers to many questions about a population. However, a census is impractical in many ways. So we need to rely on information drawn from a carefully chosen random sample of individuals/objects from the population. Such information may include sample statistics - proportion, mean, median, standard deviation, correlation, distribution, etc. The downside of the sampling approach is that the information we get is bound to change when we take a different sample. Then how can we ensure that we can make reliable inference about the population using only the sample information we got from our sample? The answer lies in the sampling distribution of the statistic which allows us, under certain assumptions, to make predictions about its values. These predictions, in turn, can be compared with the actual values obtained in the sample.Learning Objectives:Sampling Distribution of the Sample MeanSampling Distribution of the Sample ProportionCentral Limit Theorem, its assumptions and conclusion. Textbook Material -  Chapter 7 – The Central Limit Theorem – Pages 395 – 401, 405 – 413Suggested Exercises – Chapter 7 – Odds 61 – 71, 76 – 93

Subject:
Statistics and Probability
Material Type:
Module
Date Added:
07/03/2018