stackloss

How To Create a Barplot

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How To Create a Stacked Barplot

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How To Create a Pie Chart

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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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Boxplot

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Correlation Coefficient

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Cumulative Frequency Histogram

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Dotplot

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Hollow Histogram

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Numerical Summaries

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Pie Chart

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Plot

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Ratio

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Regression

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Stem and Leaf Plots

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Visual Summaries

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Submitted by pmagunia on February 26, 2017 - 11:28 AM
<iframe src="https://embed.picostat.com/stackloss.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-54086.csv 297 bytes
Documentation

Brownlee's Stack Loss Plant Data

Operational data of a plant for the oxidation of ammonia to nitric acid.

Usage

stackloss

stack.x
stack.loss

Format

stackloss is a data frame with 21 observations on 4 variables.

[,1] Air Flow Flow of cooling air
[,2] Water Temp Cooling Water Inlet Temperature
[,3] Acid Conc. Concentration of acid [per 1000, minus 500]
[,4] stack.loss Stack loss

For compatibility with S-PLUS, the data sets stack.x, a matrix with the first three (independent) variables of the data frame, and stack.loss, the numeric vector giving the fourth (dependent) variable, are provided as well.

Details

“Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.)

Air Flow represents the rate of operation of the plant. Water Temp is the temperature of cooling water circulated through coils in the absorption tower. Acid Conc. is the concentration of the acid circulating, minus 50, times 10: that is, 89 corresponds to 58.9 per cent acid. stack.loss (the dependent variable) is 10 times the percentage of the ingoing ammonia to the plant that escapes from the absorption column unabsorbed; that is, an (inverse) measure of the over-all efficiency of the plant.

Source

Brownlee, K. A. (1960, 2nd ed. 1965) Statistical Theory and Methodology in Science and Engineering. New York: Wiley. pp. 491–500.

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

Dodge, Y. (1996) The guinea pig of multiple regression. In: Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber's 60th Birthday, 1996, Lecture Notes in Statistics 109, Springer-Verlag, New York.

Examples

require(stats)
summary(lm.stack <- lm(stack.loss ~ stack.x))
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