R Dataset / Package datasets / BOD

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How To Compute the Mean

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How To Create a Plot

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How to Compute the Median

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<iframe src="https://embed.picostat.com/r-dataset-package-datasets-bod.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-83423.csv 53 bytes
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
Dataset Help

On this Picostat.com statistics page, you will find information about the BOD data set which pertains to Biochemical Oxygen Demand . The BOD data set is found in the datasets R package. You can load the BOD data set in R by issuing the following command at the console data("BOD"). This will load the data into a variable called BOD. If R says the BOD data set is not found, you can try installing the package by issuing this command install.packages("datasets") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the BOD R data set. The size of this file is about 53 bytes.

Documentation

Biochemical Oxygen Demand

Description

The BOD data frame has 6 rows and 2 columns giving the biochemical oxygen demand versus time in an evaluation of water quality.

Usage

BOD

Format

This data frame contains the following columns:

Time

A numeric vector giving the time of the measurement (days).

demand

A numeric vector giving the biochemical oxygen demand (mg/l).

Source

Bates, D.M. and Watts, D.G. (1988), Nonlinear Regression Analysis and Its Applications, Wiley, Appendix A1.4.

Originally from Marske (1967), Biochemical Oxygen Demand Data Interpretation Using Sum of Squares Surface M.Sc. Thesis, University of Wisconsin – Madison.

Examples

require(stats)
# simplest form of fitting a first-order model to these data
fm1 <- nls(demand ~ A*(1-exp(-exp(lrc)*Time)), data = BOD,
   start = c(A = 20, lrc = log(.35)))
coef(fm1)
fm1
# using the plinear algorithm
fm2 <- nls(demand ~ (1-exp(-exp(lrc)*Time)), data = BOD,
   start = c(lrc = log(.35)), algorithm = "plinear", trace = TRUE)
# using a self-starting model
fm3 <- nls(demand ~ SSasympOrig(Time, A, lrc), data = BOD)
summary(fm3)
--

Dataset imported from https://www.r-project.org.

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