Type:	Package
Title:	Supplemental Functions and Datasets for "Handbook of Regression Methods"
Version:	0.1.4
Date:	2025-05-17
Depends:	R (≥ 3.5.0)
Imports:	ggplot2, MASS, orthopolynom, quantmod, rsm, stats4
Description:	Supplement for the book "Handbook of Regression Methods" by D. S. Young. Some datasets used in the book are included and documented. Wrapper functions are included that simplify the examples in the textbook, such as code for constructing a regressogram and expanding ANOVA tables to reflect the total sum of squares.
URL:	https://github.com/dsy109/HoRM
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
Packaged:	2025-05-16 18:33:18 UTC; derekyoung
Author:	Derek S. Young [aut, cre]
Maintainer:	Derek S. Young <derek.young@uky.edu>
Repository:	CRAN
Date/Publication:	2025-05-16 19:20:02 UTC

Supplemental Functions and Datasets for "Handbook of Regression Methods"

Description

Various wrapper functions and datasets to supplement examples for the book "Handbook of Regression Methods" by D. S. Young.

Details

Author(s)

Derek S. Young, Ph.D.

Maintainer: Derek S. Young <derek.young@uky.edu>

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

Canadian Auto Insurance Dataset

Description

This dataset is from The Statistical Unit of the Canadian Underwriters' Association collated automobile insurance policies (policy years 1956 and 1957) for private passenger automobile liability for non-farmers in Canada excluding those in the province of Saskatchewan.

Usage

data(Auto)

Format

This data frame consists of 20 categories (rows) and 6 variables (columns):

• Merit Merit rating of policyholder.

• Class Class rating of policyholder.

• Insured Earned car years under the policy.

• Premiums Earned premium at present rates (in 1000's of Canadian dollars).

• Claims Number of claims incurred.

• Cost Incurred losses (in 1000's of Canadian dollars).

Source

Bailey, R. A. and Simon, L. J. (1960), Two Studies in Automobile Insurance Ratemaking, ASTIN Bulletin, 1, 192–217.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

Blood Alcohol Concentration Dataset

Description

This dataset is from a study to compare the blood alcohol concentration (BAC) of subjects using two different methods.

Usage

data(BAC)

Format

This data frame consists of 2 variables measured on 15 subjects:

• breath BAC obtained using the Breathalyzer Model 5000.

• labtest BAC based on a breath estimate in a laboratory.

Source

Krishnamoorthy, K., Kulkarni, P. M., and Mathew, T. (2001), Multiple Use One-Sided Hypotheses Testing in Univariate Linear Calibration, Journal of Statistical Planning and Inference, 93, 211–223.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

Summary of Goodness-of-Fit Tests

Description

A function that reports the Pearson statistic, the deviance statistic, and their respective p-values for goodness-of-fit testing based on a linear regression fit (lm) or a generalized linear regression fit (glm).

Usage

GOF.tests(out)

Arguments

Value

GOF.tests returns a data frame with rows corresponding to the goodness-of-fit test and columns corresponding to the respective test statistic and p-value.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

See Also

glm, lm

Examples

 
## Goodness-of-fit tests for the logistic regression fit to the
## menarche dataset.

data(menarche, package = "MASS")

glm.out = glm(cbind(Menarche, Total - Menarche) ~ Age, 
              family = binomial, data = menarche)
GOF.tests(glm.out)

Gamma-Ray Burst Dataset

Description

This dataset consists of measurements on gamma-ray bursts, which are short, intense flashes of gamma-ray radiation that occur at (seemingly) random times and locations in space.

Usage

data(GRB)

Format

This data frame consists of 63 measurements of the following 2 variables:

• TIME Time of measurement (in seconds).

• FLUX X-ray flux measurement (in units of 10^(-11) erg/cm2/s, 2-10 keV).

Source

Blustin, A. J., Band, D., Barthelmy, S., Boyd, P., Capalbi, M., Holland, S. T., Marshall, F. E., Mason, K. O., Perri, M., Poole, T., Roming, P., Rosen, S., Schady, P., Still, M., Zhang, B., Angelini, L., Barbier, L., Beardmore, A., Breeveld, A., Burrows, D. N., Cummings, J. R., Canizzo, J., Campana, S., Chester M. M., Chincarini, G., Cominsky, L. R., Cucchiara, A., de Pasquale, M., Fenimore, E. E., Gehrels, N., Giommi, P., Goad, M., Gronwall, C., Grupe, D., Hill, J. E., Hinshaw, D., Hunsberger, S., Hurley K. C., Ivanushkina, M., Kennea, J. A., Krimm, H. A., Kumar, P., Landsman, W., La Parola, V., Markwardt, C. B., McGowan, K., Meszaros, P., Mineo, T., Moretti, A., Morgan, A., Nousek, J., O'Brien, P. T., Osborne, J. P., Page, K., Page, M. J., Palmer, D. M., Parsons, A. M., Rhoads, J., Romano, P., Sakamoto, T., Sato, G., Tagliaferri, G., Tueller, J., Wells, A. A. and White, N. E. (2006), Swift Panchromatic Observations of the Bright Gamma-Ray Burst GRB 050525a, The Astrophysical Journal, 637, 901–913.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

James Bond Dataset

Description

This dataset consists of various metrics pertaining to the officially-produced James Bond films.

Usage

data(JamesBond)

Format

This data frame consists of 18 variables measured on the 24 films:

• Year Year of the film's theatrical release.

• Movie Title of the movie.

• Bond Actor who played James Bond.

• US_Gross The film's U.S. gross (in U.S. dollars).

• US_Adj The film's 2013-adjusted U.S. gross (in 1000's of U.S. dollars).

• World_Gross The film's worldwide gross (in U.S. dollars).

• World_Adj The film's 2013-adjusted worldwide gross (in 1000's of U.S. dollars).

• Budget The film's budget (in U.S. dollars).

• Budget_Adj The film's 2013-adjusted budget (in 1000's of U.S. dollars).

• Film_Length Length of the theatrical release.

• Avg_User_IMDB The average user rating on IMDB (www.imdb.com).

• Avg_User_Rtn_Tom The average user rating on Rotten Tomatoes (www.rottentomatoes.com).

• Conquests The number of "conquests" by Bond in the film.

• Martinis The number of martinis Bond drank in the film.

• BJB The number of times Bond stated "Bond. James Bond." in the movie.

• Kills_Bond The number of people killed by Bond.

• Kills_Others The number of people killed in the film by people other than Bond.

• Top_100 An indicator where a value of 1 means the title song within the top 100 on the UK Singles Chart and the U.S. Billboard Hot 100 and a value of 0 means it did not.

Source

Young, D. S. (2014), Bond. James Bond. A Statistical Look at Cinema's Most Famous Spy, CHANCE, 27(2), 21–27.

Young, D. S. (2019), Bond. James Bond. A Statistical Look at Cinema's Most Famous Spy (The Best of CHANCE Issue), Chance, 32(1), 27–35.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

Sums of Squares and Cross-Products Matrices for a MANOVA Table

Description

Summarizes the MANOVA results based on the sum of squares and cross-products decomposition for the regression (SSCPR), the error (SSCPE), and the overall total (SSCPTO).

Usage

SSCP.fn(fits)

Arguments

Value

SSCP.fn returns a list of length 3 with the SSCPR, SSCPE, and SSCPTO.

References

Johnson, R. A. and Wichern, D. W. (2007), Applied Multivariate Statistical Analysis, Sixth Edition, Pearson.

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

See Also

manova, reg.manova

Examples

 
## Applied to the amit dataset.

data(amit)

fits <- manova(cbind(TOT, AMI) ~ ., data = amit)
SSCP.fn(fits = fits)

Amitriptyline Dataset

Description

This dataset is from a study on the side effects of amitriptyline, which is a drug some physicians prescribe as an antidepressant.

Usage

data(amit)

Format

This data frame consists of 7 variables on 17 subjects:

• TOT The subject's total TCAD plasma level.

• AMI The amount of amitriptyline present in the TCAD plasma level.

• GEN The subject's gender, where 0 is for a male subject and 1 is for a female subject.

• AMT Amount of the drug taken at the time of overdose.

• PR The subject's PR wave measurement.

• DIAP The subject's diastolic blood pressure.

• QRS The subject's QRS wave measurement.

Source

Rudorfer, M. V. (1982), Cardiovascular Changes and Plasma Drug Levels After Amitriptyline Overdose, Journal of Toxicology - Clinical Toxicology, 19, 67–78.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

Auditory Discrimination Dataset

Description

This dataset is from a study to assess auditory differences between environmental sounds given several other factors.

Usage

data(auditory)

Format

This data frame consists of 3 variables on 20 subjects:

• pre.test The subject's pre-test score.

• gain The gain in auditory score between the pre- and post-test administration of the treatment.

• Culture A cultural status indicator of individuals, where 1 means the subject is from a "culturally-nondeprived" group and 0 means the subject is from a "culturally-deprived" group.

Source

Hendrix, L. J., Carter, M. W., and Scott, D. T. (1982), Covariance Analysis with Heterogeneity of Slopes in Fixed Models, Biometrics, 38, 226–252.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

Cheese-Tasting Experiment Dataset

Description

This dataset is from an experiment concerning the effect on taste of various cheese additives.

Usage

data(cheese)

Format

This data frame (36 rows by 3 columns) is a tabulation of the responses by 208 subjects to 4 different cheeses:

• Cheese The cheese additive used (four levels labeled A, B, C, and D).

• Response The response based on the 9-point hedonic scale.

• N The number of subjects who responded according to the value in Response for the cheese additive in Cheese.

Source

McCullagh, P. and Nelder, J. A. (1989), Generalized Linear Models, CRC Press.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

Odor Dataset

Description

This dataset is from an experiment that was designed to determine the effects of three factors in reducing the unpleasant odor in a chemical product being sold for household use.

Usage

data(chem)

Format

This data frame consists of 4 variables (stored in coded form) at 15 design points:

• odor A measure of the chemical's odor.

• temp Temperature at time of measurement - uncoded units are 40, 80, and 120.

• ratio Gas-liquid ratio - uncoded units are 0.3, 0.5, and 0.7.

• height Packing height - uncoded units are 2, 4, and 6.

Source

John, P. W. (1971), Statistical Design and Analysis of Experiments, MacMillan Company.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

Computer-Assisted Learning Dataset

Description

This dataset is from a study of computer-assisted learning by students in an effort to assess the cost of computer time.

Usage

data(compasst)

Format

This data frame consists of 2 variables measured on 12 students:

• num.responses Total number of responses in completing a lesson.

• cost Cost of the computer time (in cents).

Source

Kutner, M. H., Nachtsheim, C. J., Neter, J., and Li, W. (2005) Applied Linear Statistical Models, Fifth Edition, McGraw-Hill/Irwin.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

Cracker Dataset

Description

This dataset is from marketing research on the sales of crackers for a particular company.

Usage

data(cracker)

Format

This data frame consists of 15 stores, each receiving a particular promotional strategy, with the following 4 variables (columns):

• treat An indicator for which of the three marketing strategies (treatment) was employed.

• store The store number within the particular treatment.

• y The number of cases of the crackers sold during the promotional period.

• x The store's cracker sales during the preceding sales period.

Source

Kutner, M. H., Nachtsheim, C. J., Neter, J., and Li, W. (2005) Applied Linear Statistical Models, Fifth Edition, McGraw-Hill/Irwin.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

Credit Loss Dataset

Description

This dataset consists of credit portfolio loss data that were extracted from the Altman-NYU Salomon Center Corporate Bond Default Database for the years 1982 through 2005.

Usage

data(credloss)

Format

This data frame consists of 5 variables over 24 years:

• year The year the statistics were collected.

• PD The probability of default.

• defs The number of defaults.

• LGD.mean The mean loss given default.

• LGD.vol A loss given default volatility measure.

Source

Bruche, M. and Gonzalez-Aguado, C. (2010), Recovery Rates, Default Probabilities, and the Credit Cycle, Journal of Banking and Finance, 34, 754–764.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

Fiber Strength Dataset

Description

This dataset is from a study about the strength of a particular type of fiber based on the amount of pressure applied.

Usage

data(fiber)

Format

This data frame consists of 30 samples with the following 2 variables measured:

• pressure The amount of water pressure applied (measured in bars); the unique levels are 60, 80, 100, 120, 150, and 200.

• tensile The tensile strength of fiber (measured in N/5 cm).

Source

Ndaro, M. S., Jin, X.-Y., Chen, T., and Yu, C.-W. (2007), Splitting of Islands-in-the-Sea Fibers (PA6/COPET) During Hydroentangling of Nonwovens, Journal of Engineered Fibers and Fabrics, 2, 1–9.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

Fruit Fly Dataset

Description

This dataset is from a study on the effects of temperature on development of the common fruit fly.

Usage

data(fly)

Format

This data frame consists of 23 batches with the following 8 variables measured:

• temp The experimental temperature (in degrees Celsius).

• exp.no The experiment number.

• duration The mean duration of the embryonic period (in hours).

• dur.var The standard deviation of the duration of the recorded embryonic period.

• batch The number of eggs in each batch.

• batch.sd The standard deviation of the number of eggs in each batch.

• egg.temp The temperature in which the eggs were laid (in degrees Celsius).

• egg.dur The duration the eggs remained in the given conditions (in hours).

Source

Powsner, L. (1935), The Effects of Temperature on the Durations of the Developmental Stages of Drosophila Melanogaster, Physiological Zoology, 8, 474–520.

References

McCullagh, P. and Nelder, J. A. (1989), Generalized Linear Models, CRC Press.

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

Natural Gas Prices Dataset

Description

This dataset is of the monthly observations of spot prices for natural gas from January 1988 to October 1991 for the states of Louisiana and Oklahoma.

Usage

data(gas)

Format

This data frame consists of a total of 46 (monthly) observations of spot prices for the 2 states stated above:

• OK Oklahoma spot prices for natural gas (dollars per million British thermal units).

• LA Louisiana spot prices for natural gas (dollars per million British thermal units).

Source

Wei, W. W. S. (2005), Time Series Analysis: Univariate and Multivariate Methods, Pearson.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

Hildreth-Lu Procedure

Description

Returns the linear regression fit for a given level of rho using the Hildreth-Lu procedure.

Usage

hildreth.lu(y, x, rho)

Arguments

Value

hildreth.lu returns an object of class lm using the transformed quantities calculated for the Hildreth-Lu procedure.

References

Hildreth, C. and Lu, J. Y. (1960), Demand Relations with Autocorrelated Disturbances, Technical Bulletin 276, Michigan State University Agricultural Experiment Station.

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

See Also

lm

Examples

 
## Example using the natural gas dataset.

data(gas)

out.1 <- hildreth.lu(y = gas$OK, x = gas$LA, rho = 0.1)
out.2 <- hildreth.lu(y = gas$OK, x = gas$LA, rho = 0.5)

out.1
out.2

Light Dataset

Description

This dataset is from an experiment where light was transmitted through a chemical solution and an optical reading was recorded.

Usage

data(light)

Format

This data frame consists of 2 variables measured on 12 different instances:

• reading The optical reading.

• concentration The concentration of the chemical.

Source

Graybill, F. A. and Iyer, H. K. (1994), Regression Analysis: Concepts and Applications, Duxbury Press.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

Diagnostic Measures of Certain Regression Estimates

Description

A function for computing various residual-based and influence-based quantities from a linear regression fit using lm or a generalized linear regression fit using glm.

Usage

logdiag(out)

Arguments

Value

logdiag returns a data frame with the following columns:

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

See Also

glm, lm

Examples

 
## Diagnostic summaries for the logistic regression fit to the
## menarche dataset.

data(menarche, package = "MASS")

glm.out = glm(cbind(Menarche, Total - Menarche) ~ Age, 
              family = binomial, data = menarche)
logdiag(glm.out)

Expands Design Matrix Based on Polynomials

Description

This function takes a list of objects having class polynomial, evaluates each polynomial as a function of x, then returns the results in a matrix.

Usage

poly2form(poly.out, x)

Arguments

Value

poly.out returns a matrix whose columns are the evaluation of each polynomial in poly.out using x.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

See Also

legendre.polynomials

Examples

 
## Evaluating the order 5 Legendre polynomials.

require(orthopolynom)

px <- legendre.polynomials(n = 5, normalized = FALSE)
lx <- poly2form(poly.out = px, x = 1:10)

lx

Power Function for the General Linear F-Test

Description

A function to calculate the power of the general linear F-test.

Usage

power.F(full, reduced, alpha = 0.05)

Arguments

Value

power.F returns a single value (saved as a matrix) with the power for the corresponding general linear F-test.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

See Also

anova, lm

Examples

 
## Applied to the toy dataset.

data(toy)

full <- lm(y~x, data = toy)
reduced <- lm(y~1, data = toy)
power.F(full = full, reduced = reduced, alpha = 0.05)

Power Functions for Tests of Simple Linear Regression Coefficients

Description

A function to calculate the power of the t-tests corresponding to tests on the intercept and slope coefficients in the simple linear regression model.

Usage

power.b(x, y, alpha = 0.05, B0 = 0, B1 = 0)

Arguments

Value

power.b returns a matrix with the noncentrality parameters and power levels for the corresponding t-tests.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

See Also

power.F

Examples

 
## Applied to the toy dataset.

data(toy)

power.b(x = toy$x, y = toy$y)

Ridge Functions for Projection Pursuit Regression

Description

The portion of the plot.ppr code that computes the ridge traces for projection pursuit regression.

Usage

ppr_funs(obj)

Arguments

Details

This is just the segment of code in plot.ppr, which calculates the ridge traces.

Value

ppr_funs returns the evaluated ridge trace values based on output from the ppr function.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

See Also

ppr, plot.ppr

Examples

 
## Projection pursuit regression on the rock dataset.

data(rock)

ppr.out <- ppr(log(perm) ~ area + peri + shape,
               data = rock, nterms = 2, max.terms = 5)
obj <- ppr_funs(ppr.out)

obj

Andrew's Sine Function

Description

Andrew's sine function for use when fitting a linear model by robust regression using an M-estimator.

Usage

psi.andrew(u, k=1.339, deriv=0)

Arguments

Value

psi.andrew returns a vector of points evaluated using Andrew's sine function.

References

Andrew, D. F. (1974), A Robust Method for Multiple Linear Regression, Technometrics, 16, 523–531.

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

See Also

rlm

Examples

 
## Robust fit of the stackloss dataset.

require(MASS)

data(stackloss, package="datasets")

out <- rlm(stack.loss ~ ., data = stackloss, 
           psi = psi.andrew)

out

Expanded ANOVA Table

Description

Calculate the ANOVA table for an object of class lm. The results are identical to those obtained from anova, but an extra line is included that prints the total degrees of freedom and the total sum of squares.

Usage

reg.anova(lm.out)

Arguments

Value

reg.anova returns exactly the same output as the anova function applied to an object of class lm, but includes an extra line that summarizes the total source of variability.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

See Also

anova, lm

Examples

 
## Applied to the toy dataset.

data(toy)

lm.out <- lm(y ~ x, data = toy)
anova(lm.out)
reg.anova(lm.out)

Expanded MANOVA Table

Description

Expands the MANOVA results from an object of class summary.aov. The results are identical to those obtained from summary.aov, but an extra line is included that prints the total degrees of freedom and the total sum of squares for each dimension of the response vector.

Usage

reg.manova(AOV.out)

Arguments

Value

AOV.out returns exactly the same output as the summary.aov function, but includes an extra line that summarizes the total source of variability for each dimension of the response vector.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

See Also

anova, reg.anova, summary.aov

Examples

 
## Applied to the amit dataset.

data(amit)

fits <- manova(cbind(TOT, AMI) ~ ., data = amit)
out <- summary.aov(fits)
mvreg.out <- lapply(out, reg.manova)
mvreg.out

Regressogram

Description

Computes and plots the regressogram for a single predictor and single response relationship. The regressogram is plotted using ggplot2.

Usage

regressogram(x, y, nbins = 10, show.bins = TRUE,
			       show.means = TRUE, show.lines = TRUE,
             x.lab = "X", y.lab = "Y", main = "TITLE")

Arguments

Value

regressogram returns a plotted regressogram using the ggplot2 package.

References

Wasserman, L. (2006), All of Nonparametric Statistics, Springer.

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

See Also

hist

Examples

 
## Regressogram for the natural gas dataset.

data(gas)

regressogram(x = gas$LA, y = gas$OK, nbins = 6, x.lab = "LA",
             y.lab = "OK", main = "Regressogram")

Computer Repair Dataset

Description

This dataset is from a random sample of service call records for a computer repair company.

Usage

data(repair)

Format

This data frame consists of a sample of 14 companies with the following 2 variables measured:

• minutes The length of service call (in minutes).

• units The number of components repaired or replaced during the service call.

Source

Chatterjee, S. and Hadi, A. S. (2012), Regression Analysis by Example, John Wiley and Sons, Inc.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

Google Stock Dataset

Description

This dataset consists of the closing stock price of a share of Google stock during the trading days between February 7-th and July 7-th of 2005.

Usage

data(stock)

Format

This is an extensible time series (xts) object for the 105 trading days of interest:

• GOOG.close The closing stock price of a share of Google stock.

Source

Yahoo! Finance; accessed 01-26-2017.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

Examples

 
## Not run: 
## How the data were accessed (1/26/17).

require(quantmod)

getSymbols("GOOG", src = "yahoo", 
           from = "2005-02-07", to = "2005-07-07")
stock <- GOOG[,4]

## End(Not run)

Tortoise Eggs Dataset

Description

This dataset is from a study on the number of eggs in female gopher tortoises in southern Florida.

Usage

data(tortoise)

Format

This data frame consists of 2 variables measured on 18 tortoises:

• length The carapace length (in millimeters).

• clutch The number of eggs (clutch size).

Source

Ashton, K. G., Burke, R. L., and Layne, J. N. (2007), Geographic Variation in Body and Clutch Size of Gopher Tortoises, Copeia, 2007, 355–363.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

Toy Dataset

Description

A made-up (toy) dataset.

Usage

data(toy)

Format

This data frame consists of 2 made-up variables for a sample of size 5:

• x The made-up x values.

• y The made-up y values.

Source

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

Pulp Property Dataset

Description

This dataset is from a study about the pulp properties of wood density of the Australian blackwood tree.

Usage

data(wood)

Format

This data frame consists of 2 variables measured on 7 samples:

• pulp The percentage of pulp yield.

• Kappa The Kappa number, which is a measurement of standard potassium permanganate solution that the pulp will consume.

Source

Santos, A., Anjos, O., Amaral, M. E., Gil, N., Pereira, H., and Simoes, R. (2012), Influence on Pulping Yield and Pulp Properties of Wood Density of Acacia melanoxylon, Journal of Wood Science, 58, 479–486.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.

Yarn Fiber Dataset

Description

This dataset is from a mixture experiment regarding a fiber blend that is spun into yarn to make draperies.

Usage

data(yarn)

Format

This data frame consists of 4 variables measured at 15 design points for a {3,2} simplex lattice design:

• x1 The proportion of the polyethylene component.

• x2 The proportion of the polystyrene component.

• x3 The proportion of the polypropylene component.

• y The measurement of yarn elongation.

Source

Cornell, J. A. (2002), Experiments with Mixtures: Designs, Models, and the Analysis of Mixture Data, Third Edition, John Wiley and Sons, Inc.

References

Young, D. S. (2017), Handbook of Regression Methods, CRC Press.