Stacey, Brian (2015): Sampling for Variance in a Population.

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Abstract
Determining confidence intervals for a population µ estimate from a sample ¯x with a either a known or an unknown population σ use similar methods. The more data one has to begin with the better will be the estimate, so knowing the standard deviation of the population will provide a better estimate of the mean. In our case we have no data to begin with so must collect the data to build our estimates. This eliminates the possibility of estimating the confidence interval of µ with a known σ, which is the better of the estimates, and has a narrower range. The data collection and calculation of s and ¯x will be necessary in our case. Once those two estimators are calculated, finding the confidence interval is an easy task.
Item Type:  MPRA Paper 

Original Title:  Sampling for Variance in a Population 
Language:  English 
Keywords:  Sampling, Statistics 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C10  General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General 
Item ID:  69381 
Depositing User:  Brian Stacey 
Date Deposited:  10 Feb 2016 05:55 
Last Modified:  26 Sep 2019 22:02 
References:  Chiang, A. (1984). Fundamental methods of mathematical economics (3rd ed.). New York: McGrawHill. FortmannRoe, S. (2012, June 1). Bias and Variance. Retrieved March 15, 2015, from http://scott.fortmannroe.com/docs/BiasVariance.html Gutierrez, D. (2014, October 22). Ask a Data Scientist: The Bias vs. Variance Tradeoff – inside BIGDATA. Retrieved March 15, 2015, from http://insidebigdata.com/2014/10/22/askdatascientistbiasvsvariancetradeoff/ HallsMoore, M. (2015, February 25). The BiasVariance Tradeoff in Statistical Machine Learning  The Regression Setting. Retrieved March 15, 2015, from http://www.quantstart.com/articles/TheBiasVarianceTradeoffinStatisticalMachineLearningTheRegressionSetting 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/69381 