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App. Stats: added subsections

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henrydatei 2019-02-22 16:27:06 +00:00
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Erasmus/Applied statistics/Applied Statistics.pdf
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Erasmus/Applied statistics/Applied Statistics.tex
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\title{\textbf{Applied statistics (spring term 2019)}} \title{\textbf{Applied statistics (spring term 2019)}}
\author{readers: \person{Nikolai Bode} and \person{Ksenia Shalonova}} \author{readers: \person{Nikolai Bode} and \person{Ksenia Shalonova}}
\date{written by \person{Henry Haustein}}
\begin{document} \begin{document}
\pagenumbering{roman} \pagenumbering{roman}

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Erasmus/Applied statistics/TeX_files/Estimating_parameters_1.tex
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\subsection{Confidence and tolerance intervals}
In statistical analysis we want to estimate a population from a \begriff{random sample}. This is called \begriff{interference} about the parameter. Random samples are used to provide information about parameters in an underlying \begriff{population distribution}. Rather than estimating the full shape of the underlying distribution, we usually focus on one or two parameters. In statistical analysis we want to estimate a population from a \begriff{random sample}. This is called \begriff{interference} about the parameter. Random samples are used to provide information about parameters in an underlying \begriff{population distribution}. Rather than estimating the full shape of the underlying distribution, we usually focus on one or two parameters.
We want the error distribution to be centered on zero. Such an estimator is called \begriff{unbiased}. An biased estimator tends to have negative/positive errors, i.e. it usually underestimates/overestimates the parameter that is being estimated. We want the error distribution to be centered on zero. Such an estimator is called \begriff{unbiased}. An biased estimator tends to have negative/positive errors, i.e. it usually underestimates/overestimates the parameter that is being estimated.