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In the previous chapter we have seen how to describe a sample in order to produce potentially interesting hypotheses about its population. Some of the descriptions we have seen are based on graphical representations that are easily interpreted by humans, while others are based on parameters that summarize important properties of the sample distribution. In this chapter we will see how to infer predictions about a population. To this end we will explore the relationship between sample parameters and population parameters and we will propose some methods, both theoretical and computational, to assess the quality of parameter estimates from a sample.
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Suppose that we draw all possible samples of a given size from a given population. Suppose further that we compute the mean for each sample. The probability distribution of this statistic is called the mean sampling distribution.
Censoring is a condition in which the value of observation is only partially known.
go back to reference M.I. Jordan. Are you a Bayesian or a frequentist? [Video Lecture]. Published: Nov. 2, 2009, Recorded: September 2009. Retrieved from: http://videolectures.net/mlss09uk_jordan_bfway/ M.I. Jordan. Are you a Bayesian or a frequentist? [Video Lecture]. Published: Nov. 2, 2009, Recorded: September 2009. Retrieved from: http://videolectures.net/mlss09uk_jordan_bfway/
go back to reference B. Efron, R.J. Tibshirani, An introduction to the bootstrap (CRC press, 1994) B. Efron, R.J. Tibshirani, An introduction to the bootstrap (CRC press, 1994)
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