BOD

Boxplot

Submitted by pmagunia on April 22, 2018 - 3:07 PM

Select any column or the entire dataset option for its boxplot.

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Correlation Coefficient

Submitted by pmagunia on April 22, 2018 - 3:08 PM

Select any two columns or the entire dataset option to compute the correlation coefficient matrix.

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Cumulative Frequency Histogram

Submitted by pmagunia on April 22, 2018 - 3:09 PM

Select any column to plot its cumulative frequency histogram.

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Dotplot

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Hollow Histogram

Submitted by pmagunia on April 22, 2018 - 3:10 PM

Select any two columns to plot them simultaneously using a histogram.

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Mean

Submitted by pmagunia on April 22, 2018 - 3:11 PM

Select any column to compute the arithmetic mean.

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Pie Chart

Submitted by pmagunia on April 22, 2018 - 3:11 PM

Select any column to create its pie chart.

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Plot

Submitted by pmagunia on April 22, 2018 - 3:07 PM

Select any two columns to plot.

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Regression

Submitted by pmagunia on April 22, 2018 - 3:12 PM

Select any two columns for a simple regression analysis. The first column selected will be the independent variable.

Stem and Leaf Plots

Submitted by pmagunia on April 22, 2018 - 3:12 PM

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Summary

Submitted by pmagunia on April 22, 2018 - 2:51 PM

Select any column to compute its mean, variance, and also other summary statistics.

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Visual Summaries

Submitted by pmagunia on April 22, 2018 - 3:13 PM

Select any column for various visual summaries.

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Submitted by pmagunia on February 26, 2017 - 11:28 AM
Attachment Size
dataset-93773.csv 53 bytes
Documentation

Biochemical Oxygen Demand

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)

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