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Most statistical programs stick to their traditions when calculating quantiles probably to maintain backwards computability with older versions. Several excellent software packages are available for the statistical analysis of reference intervals 5 , 19 - 23 but they are not as commonly used as they should e. Increased availability of low-cost computing machinery and resampling methods has made distribution independent computer intensive methods accessible in routine work All statistical methods are based on assumptions which fundamentally decide the conclusion that can be made from the data.

Parametric methods were initially described in the first half of the twentieth century when methods for performing extensive calculations were primarily manual and semi-automated at best. Using distribution functions to describe natural phenomena including research data, substantially simplified the calculations required for statistical analysis.

Without these known statistical distribution functions it would have been impossible to use advanced statistical methods in practice. The wide availability of low-cost computing power in the ies substantially changed the options for working with the data as they turn out irrespective of theoretical distributions. A seminal work on resampling methods by Jones in 26 was followed by the very influential works by Efron et al.

A simple method based on ranked results was initially used 27 , It is used here in order to eliminate the need for the user to keep track of the number of reference values in the reference sample. You can speed up the calculation by giving the number of data points in your particular data set, e.

The increased speed is caused by the fact that Excel then does not need to repeatedly determine the number of reference values you have entered.


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Drag the small quadrant in the lower right corner of cell C1 to column AG83 thereby creating 30 samples, each of 83 random observations from the original observations. Extending the columns to the right you can increase the number of samples e. Calculate the percentiles, e. EXC I1:I83;0. EXC function, available only in — or later versions of MS Office is used in order to implement a recommended method for percentile value interpolation This means that random samples of at least 39 observations are needed to calculate the 2. Lastly calculate the median of the samples estimates of the population 0.

Detailed instructions to create the calculation sheet are presented in table 1. An example showing 30 random samples drawn from the 83 reference values shown in Figure 2. The 83 reference values are shown in the column on the far left column A marked with a blue box. The computer time needed for re-calculating samples of 83 observations each from a sample of 83 observations was less than 1 second.

26: Resampling methods (bootstrapping)

Resampling methods are free from the assumption that the observations are distributed according to a certain theoretical distribution, but importantly assume that the underlying population distribution is practically the same as that in a particular sample. This means that a sufficient number of observations is needed in the sample to make sure that it represents the population. Coscun et al. Their results confirm that 40 reference samples represent a prudent minimum.

At least but preferably or more 31 resamples are generally recommended which is supported by the results of the present study. Bjerner et al. Its widespread availability and versatility constitutes its major advantages enabling the users to perform powerful descriptive, inferential graphical and statistical analysis using even methods not yet programmed in statistical packages.

Download Introduction To Statistics Using Resampling Methods And Microsoft Office Excel 2005

The program has, especially in its earlier versions been criticized for its mathematical 33 , statistical 34 and graphical properties. Several of the limitations have been overcome in the most recent versions of Excel 35 , since Resampling methods are recommended by IFCC and are widely available in software for handling reference values 5 , 19 - 24 but not as yet as yet a part of the CLSI guideline Resampling methods are appropriate when the distribution of data from the reference samples is non-Gaussian and in case the number of reference samples are in order of at least At least random samples with replacement should be taken from the reference samples.

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National Center for Biotechnology Information , U. Journal List Biochem Med Zagreb v. Biochem Med Zagreb. Published online Oct Elvar Theodorsson. Author information Article notes Copyright and License information Disclaimer. Corresponding author: es. Received May 18; Accepted Jul Copyright notice. This article has been cited by other articles in PMC. Key words: reference interval, resampling method, Microsoft Excel, bootstrap method, biostatistics. Description of the task Reference intervals 1 , 2 are amongst the essential tools for interpreting laboratory results.

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Download Introduction To Statistics Using Resampling Methods And Microsoft Office Excel

Figure 1. Different methods for calculating reference intervals Estimating reference intervals means dealing with uncertainties and probabilities. Figure 2. Percentiles A proper start when calculating reference intervals is to estimate the observation s corresponding to 2. Subsequent randomly drawn reference values Drag the small quadrant in the lower right corner of cell C1 to column AG83 thereby creating 30 samples, each of 83 random observations from the original observations.

Calculating the 0. Calculate the medians of the random sample estimates of the population percentiles Lastly calculate the median of the samples estimates of the population 0. Looks like you are currently in Finland but have requested a page in the United States site. Would you like to change to the United States site? Phillip I.

Download Introduction To Statistics Using Resampling Methods And Microsoft Office Excel 2005

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    The split should be made at random. The downside is that when we use only a portion of the sample, the resulting estimates are less precise. In Exercises 7. There are several different ways to arrange for the division. Wher- ever a value greater than 0. Exercise 7.

    Can total coliform levels be predicted on the basis of month, oxygen level, and temperature? TotColi 30, 22, 16, 18, 32, 40, 50, 34, 32, 32, 34, 18, 16, 19, 65, 54, 32, 59, 45, 27, 88, 32, 78, 45, 68, 14, 54, 22, 25, 32, 22, 17, 87, 17, 46, 23, 10, 19, 38, 22, 12, 26, 8, 8, 11, 19, 45, 78, 6, 9, 87, 6, 23, 28, 0, 0, 43, 8, 23, 19, 0, 5, 28, 19, 14, 32, 12, 17, 33, 21, 18, 5, 22, 13, 19, 27, 30, 28, 16, 6, 21, 27, 58, 45 Exercise 7. Note: As conditions and relationships do change over time, any method of prediction should be revalidated frequently.

    For example, suppose we had used observations from January to January to construct our original model and held back more recent data from January to June to validate it. Discuss the possible advantages and disadvantages of discarding these data. Cross-Validation with the Bootstrap Recall that the purpose of bootstrapping is to simulate the taking of repeated samples from the original population and to save money and time by not having to repeat the entire sampling procedure from scratch.

    As Exercise 7. Bootstrap repeatedly from the data provided in Exercises 7. Are some variables common to all the models? Should all these terms be included in our model? Which ones should be neglected?

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    We need to consider alternate approaches. For example, 1. Leaves simple? Leaves needle-shaped?