Study of Diets in Alligators

Description

Data.frame

Usage

data(alligatorDiet)

Format

The data frame has 16 rows and 8 variables:

lake

Lake in Florida of the capture of the aligator.

gender

Female (F) or Male (M).

size

small (<=2.3m) or big (> 2.3m).

fish

Number of alligators with a primary stomach contents of fish.

invertabrate

Number of alligators with a primary stomach contents of invertebrate.

reptile

Number of alligators with a primary stomach contents of reptile.

bird

Number of alligators with a primary stomach contents of bird.

other

Number of alligators with a primary stomach contents of other.

Details

A study done at four lakes in Florida captured 219 alligators. The primary food type found in the alligator's stomach is recorded. Along with the gender, lake of capture, and size of the alligator.

References

Agresti, A. (2013) Categorical Data Analysis. 3rd Edition, John Wiley & Sons, Hoboken, New Jersey.

Examples

data("alligatorDiet", package='syllogi')
str(alligatorDiet)

Study of Diets in Alligators at Lake George, Florida

Description

Data.frame

Usage

data(alligatorLength)

Format

The data frame has 63 rows and 3 variables:

sex

Female (F) or Male (M).

length

Length of alligator in meters. Subadult alligators have length < 1.83 and adults if > 1.83 meters.

foodChoice

Primary stomach contents of the alligator.

Details

A study in Lake George, Florida caught 63 alligators. Each alligator's stomach contents were classified as fish, invertebrate, or other. The sex and the length of the alligator were also recorded.

References

Agresti, A. (2013) Categorical Data Analysis. 3rd Edition, John Wiley & Sons, Hoboken, New Jersey.

Examples

data("alligatorLength", package='syllogi')
str(alligatorLength)

Fictitious Data Set of Annual Sales

Description

Data.frame

Usage

data(annualSales)

Format

The data frame has 12 rows and 3 variables:

sales

Annual gross sales in $1000 of dollars.

advert

Annual cost of advertising in $1000 of dollars.

quality

Quality of their store\'s typical product: 0=very poor quality to 25 = exceptional quality.

Details

You are hired as a statistical consultant. Twelve stores in the Fort Collins, CO area have asked you to develop a prediction model for their annual gross sales (sales; measured in $1000 of dollars). They would like to know if it is possible to predict the amount of their sales by knowing how much they spend annually on advertising (advert; measured in $1000 of dollars) and the quality of their store’s typical product (quality; measure on a scale from 0 = very poor quality to 25 = exceptional quality).

References

fictitious data set

Examples

data("annualSales", package='syllogi')
str(annualSales)

Beer

Description

Data.frame

Usage

data(beer)

Format

The data frame has 86 rows and 5 variables:

brand

Brand name of the beverage

brewery

Brewery of the beverage

percentAlcohol

Percent alcohol by volume

calories

Total calories

carbohydrates

Total carbohdrates

Details

Does a beer with more carbohydrates tend to have more alcohol? To answer this question the carbohydrates and percent alcohol from several different beer brands were measured.

Examples

data("beer", package='syllogi')
str(beer)

Bighorn Sheep

Description

Bighorn Sheep data

Usage

data(bighornSheep)

Format

The data frame has 8000 rows (a geographic sample unit) and 15 variables:

sampleUnit

Sample unit ID, 150m circles randomly overlayed across the study area

count

Count of use by bighorn sheep.

slope

Average slope (degrees) within the sampling unit

elev

Average elevation (m) within the sampling unit

distBurn

Sampling unit center to nearest (m) burned habitat edge calculated after fire event

distRoad

Sampling unit center to nearest (m) road

distEscp

Sampling unit center to nearest (m) escape terrain (slope > 27 degrees)

distWater

Sampling unit center to nearest (m) perennial water source

aspect

Dominant cardinal direction within each sampling unit

fire

1 = after fire, 0 = before fire

season

Season, summer or winter

Details

Twelve female bighorn sheep are radio collared and tracked. Location of use of points is recorded before and after a forest fire.

References

Clapp, J.G., Beck, J.L. Short-Term Impacts of Fire-Mediated Habitat Alterations on an Isolated Bighorn Sheep Population. fire ecol 12, 80–98 (2016). https://doi.org/10.4996/fireecology.1203080

Examples

data('bighornSheep', package='syllogi')
str(bighornSheep)

Study of Recurrence of Bladder Cancer

Description

Data.frame

Usage

data(bladderCancer)

Format

The data frame has 31 rows and 3 variables:

Size

0 = small primary tumor (< 3 cm) and 1 = large primary tumor (> 3cm).

Tumors

Number of tumors.

Time

Follow up time in months.

Details

Study on recurrence of bladder cancer tumor patients. Each patient had perviously received surgery to remove a primary tumor. The size of the primary removed tumor was recorded. After different follow up times the number of recurring tumors were recorded.

References

Bilder, C.R., & Loughin, T.M. (2014). Analysis of Categorical Data with R (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/b17211

Examples

data("bladderCancer", package='syllogi')
str(bladderCancer)

Fictitious Data Set of Butterfly Counts

Description

Data.frame

Usage

data(butterflyPlot)

Format

The data frame has 40 rows and 2 variables:

area

Plot area size in hectares.

numSpecies

Count of number of unique species.

Details

Plots ranging in size from 1ha to 1000ha, were left uncut in a larger landscape of logged tropical rainforest. In each plot the number of unique butterfly species was recorded. What is the relationship between plot size and unique species count?

References

fictitious data set

Examples

data("butterflyPlot", package='syllogi')
str(butterflyPlot)

Self Reported Depression

Description

Self reported level of depression and other associated metrics.

Usage

data(depression)

Format

An object of class data.frame with 50 rows and 13 columns.

Details

This is a fictious dataset useful for teaching how to use and interpret linear statistical models. The variables are:

educate

Level of Education: (1) professional degree (non-college), (2) 2 years of college, (3) 2+ years of college, but not a BS degree, (4) BS degree, (5) MS degree

income

Annual Income: 1 = $10,0001 to $19,999; 2 = $20,000 to $29,999; ... 9 = $90,000 to $99,999; 10 = $100,000 or more

trauma

Experience of Trauma; Percent of Life Events Viewed as Traumatic: 0 = 0%, 1 = 10%, 2= 20%, ..., 9 = 90%, 10 = 100%

satisfac

Satisfied with your Life: 0 = No, 1 = Yes

control

Feeling of Control; How much do you feel in control: 0 = Not at all, 1 = A Little, 2 = Some, 3 = A Lot, 4 = Completely

history

Family History of Depression: 0 = No, 1 = Yes

exercise

Weekly Amount of Exercise: 0 = None, 1 = 1 Hour, 2 = 2 Hours, 3 = 3 Hours, 4 = 4 Hours, 5 = 5 or more Hours

mhpg

3-methoxy-4-hydroxyphenylethyleneglycol, Depression Related Chemical Secreted in Urine; milligrams secreted per 24 hour period, labeled as mg/24h: 0 = 0 mg/24h, 1 = 100 mg/24h,..., 9 = 900 mg/24h, 10 = 1000+ mg/24h

sleep

Amount of Sleep Problems: 0 = None, 1 = 10% of the time, ... , 9 = 90% of the time, 10 = 100% of the time

depress

Perceived Level of Depression: 0 = None, 1 = 10% of the time, ... , 9 = 90% of the time, 10 = 100% of the time

depressYes

Do I consider myself depressed: 0 = No, 1 = Yes

welbeing

Feeling of Well Being; how often do you feel good about yourself: 0 = None, 1 = 10% of the time, ... , 9 = 90% of the time, 10 = 100% of the time

gender

Your Sex: 0 = Male, 1 = Female

References

fictitious data set

Fictitious Data Set Comparing Dog Food Brands

Description

Data.frame

Usage

data(dogFood)

Format

The data frame has 25 rows and 2 variables:

type

The type of dog food: our dog food or one of the four top sellers.

gain

The percent weight gain.

Details

You are hired as a statistical consultant for a dog food manufacturing company. The engineers who designed the company's dog food would like to know how it compares to the current top selling dog food brands on the market? To answer this question, 25 puppies of the same breed and age (within a week of each other) were chosen for this study. Five puppies were assigned to each dog food type. After 4 weeks the percent of weight gained for each puppy was determined.

References

fictitious data set

Examples

data("dogFood", package='syllogi')
str(dogFood)

Federalist Papers

Description

List of the Federalist Papers

Usage

data(federalistPapers)

Format

The list has 86 elements, each element is a list with 2 elements. The paper element is the text of the paper. The meta element is a data frame:

number

Paper number.

author

Author of the paper.

title

Title of the paper.

journal

Newpaper that published the paper.

date

Date of publication.

Details

The Project Gutenberg version of the Federalist Papers attributes paper No. 58 to Madison, but Mosteller and Wallace consider this paper to have disputed authorship. Thus, this version considers No. 58 authorship to be disputed.

The Project Gutenberg has two slightly different versions of No. 70, both included.

References

https://www.gutenberg.org/ebooks/18

Mosteller, F. and D. L. Wallace. Inference and Disputed Authorship: The Federalist. Reading, MA., 1964

Examples

data("federalistPapers", package='syllogi')
str(federalistPapers)

Generic Data Set

Description

Generic data set with four ratio predictors (X1,X2,X3,X4), two categorical predictors (A,B) and one ratio response variable (Y).

Usage

data(genericData)

Format

An object of class data.frame with 60 rows and 7 columns.

Details

This is a fictious dataset useful for teaching how to use and interpret linear statistical models.

References

fictitious data set

Examples

data("genericData", package='syllogi')
str(genericData)

Golfing

Description

Data.frame

Usage

data(golf)

Format

The data frame has 18 rows and 3 variables:

clubs

clubs used for that round of golf

course

course for the round of golf

score

score or strokes for 18 holes

Details

I purchased new golf clubs last summer, which I believe will significantly improve my game. I recorded my score after three rounds of golf with my new clubs and my old clubs. I also played at three different courses.

References

fictitious data set

Examples

data("golf", package='syllogi')
str(golf)

Nutrition Cancer Study

Description

Data.frame

Usage

data(nutritionCancer)

Format

The data frame has 50 rows and 6 variables:

id

ID number of each patient.

age

The age of the patient in years.

length

The duration or time in months the patient has had breast cancer.

serving

The number of servings the patient eats of fruits and vegetables in a typical day.

familyHistory

Does or did any blood relatives (i.e. mother, grandmother, aunt, etc.) have or had breast cancer?

stage

The stage of the cancer: 0-non-invasive to IV-very invasive or "advanced" cancer.

Details

Fictitious data set for teaching purposes. The fictitious scenario:

The purpose of a medical study is to examine the relationship between eating fruits and vegetables and breast cancer. To study the relationship, 1500 caucasian women with breast cancer were randomly selected from the list of cancer patients in the U.S. The first 50 patients have been measured.

References

Fictitious data set

Examples

data("nutritionCancer", package='syllogi')
str(nutritionCancer)

Study of Nonmetastatic Osteosarcoma

Description

Data.frame

Usage

data(osteosarcoma)

Format

The data frame has 8 rows and 5 variables:

lymphocyticInfiltration

Patient has high or low lymphocytic inflitration.

gender

Female (F) or Male (M).

osteoblasticPathology

Patient has osteoblastic pathology yes or no.

diseaseFreeYes

Number of patients that are disease free after three years.

diseaseFreeNo

Number of patients that are not disease free after three years.

Details

A study of nonmetastatic osteosarcoma was done. They recorded if the patient was disease free after three years. They recorded the gender, level of lymphocytic infiltration, and if there is osteoblastic pathology or not. Can the probability of being desease free after 3 years be predicted?

References

A M Goorin, A Perez-Atayde, M Gebhardt, J W Andersen, R H Wilkinson, M J Delorey, H Watts, M Link, N Jaffe, and E Frei 3rd Journal of Clinical Oncology 1987 5:8, 1178-1184

Agresti, A. (2002) Categorical Data Analysis. 2nd Edition, John Wiley & Sons, Inc., New York, 320-332. http://dx.doi.org/10.1002/0471249688

Examples

data("osteosarcoma", package='syllogi')
str(osteosarcoma)

Patient Satisfaction

Description

Data.frame

Usage

data(patientSatisfaction)

Format

The data frame has 46 rows and 4 variables:

satisfaction

Patient's level of satisfaction, higher value means more satisfied.

age

Patient's age in years.

severityIllness

Patient's severity of illness, higher value means more sever.

anxietyLevel

Patient's anxiety level, higher value means more sever.

Details

A hospital administrator wants to predict patient's satisfaction using their age, severity of illness, and anxiety level. Forty six patients were selected for the study.

References

Kutner, M. H., Nachtsheim, C., Neter, J., & Li, W. (2005). Applied linear statistical models (5th ed.). McGraw-Hill Irwin.

Examples

data("patientSatisfaction", package='syllogi')
str(patientSatisfaction)

Political Ideology

Description

Data.frame

Usage

data(politicalIdeology)

Format

The data frame has 20 rows and 4 variables:

gender

Female (F) or Male (M).

party

Democrat (D) or Republican (R)

ideol

Very liberal (VL), Slightly Liberal (SL), Moderate (M), Slightly conservative (SC), or Very conservative (VC).

count

Count of people.

Details

A 1991 U.S. General Social survey that cross classifies people according to gender, political party, and political ideology.

References

Bilder, C.R., & Loughin, T.M. (2014). Analysis of Categorical Data with R (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/b17211

Examples

data("politicalIdeology", package='syllogi')
str(politicalIdeology)

High School and Beyond Survey

Description

A survey conducted on high school seniors by the National Center of Education Statistics.

Usage

data(schoolProgram)

Format

The data frame has 200 rows (a student) and 11 variables:

id

Student ID.

gender

Student's gender.

race

Student's race.

ses

Socio economic status of the student's family, with levels low, middle, and high.

schtype

Type of school: public or private.

prog

Type of program the student wants to attend after high school.

read

Student's standardized reading score.

write

Student's standardized writing score.

math

Student's standardized math score.

science

Student's standardized science score

scost

Student's standardized social studies score

Details

Two hundred students were randomly selected from the whole cohort in the survey.

References

https://www.openintro.org/data/index.php?data=hsb2

UCLA Institute for Digital Research & Education - Statistical Consulting.

Examples

data("schoolProgram", package='syllogi')
str(schoolProgram)

Wave Damage of Ships

Description

Data.frame

Usage

data(shipDamage)

Format

The data frame has 20 rows and 5 variables:

shipType

Type of ship

constYear

Year of construction

operation

Period of operation

months

Aggregate months of service

incidents

Number of damage incidents

Details

Cargo carrying vessel's damage to the forward section due to waves. These data are only for the period of operation 1975 to 1979.

References

McCullagh, P. and Nelder, J.A. (1989) Generalized Linear Models. 2nd Edition, Chapman and Hall, London.

Examples

data("shipDamage", package='syllogi')
str(shipDamage)

Ships and Gold

Description

Data.frame

Usage

data(shipGold)

Format

The data frame has 20 rows (a ship) and 2 variables:

shipSize

Size of the ship measured in inches on the horizon.

gold

Amount of gold pieces on the ship.

Details

Fictitious data set for teaching purposes. The fictitious scenario:

Captain Buck Tooth has taken you prisoner aboard his pirate ship, the Lucky Lemon. He sees from your college transcripts you have taken a couple of statistics courses. Captain Buck Tooth wants you to predict the amount of gold a ship is carrying based on the size of the ship. Specifically, he thinks bigger ships carry more gold. For the last several ships he has looted he measured the height in inches when the ship was still way off on the horizon. The captain also has a good memory and remembers how much gold was taken from each ship in number of pieces.

References

Fictitious data set

Examples

data("shipGold", package='syllogi')
str(shipGold)

Ski Resort

Description

Data.frame

Usage

data(ski)

Format

The data frame has 9 rows and 4 variables:

miles

miles of skiable terrain

capacity

number of vistors that could be taken per hour from the base of the mountain to the top via the resorts various lifts

vistors

number of tickets sold per week

resort

sort ID number

Details

Information from local ski resorts in the region. The research question is can weekly visitors be predictors from miles of skiable terrian and/or capacity of lifts?

References

fictitious data set

Examples

data("ski", package='syllogi')
str(ski)

Weight Loss Study

Description

Data.frame

Usage

data(weightLoss)

Format

The data frame has 60 rows and 2 variables:

drug

Which weight loss drug the participant took for 6 weeks.

loss

Percent of weight loss after the 6 weeks.

Details

Fictitious data set for teaching purposes. The fictitious scenario:

You are a statistical consultant. A client comes to you asking for help with their analysis. The client is from a drug company. Their new drug is supposed to help people lose weight. They conducted an experiment with their drug (drug A) and the two best selling weight loss drugs (B and C). Male participants from age 50-60 were used in the study. Each participant took one of the drugs for 6 week and the percent of weight loss was recorded.

References

Fictitious data set

Examples

data("weightLoss", package='syllogi')
str(weightLoss)

Wheat Kernels

Description

Data.frame

Usage

data(wheat)

Format

The data frame has 275 rows and 7 variables:

class

hrw = hard red winter wheat and srw = soft red winter wheat.

density

Density of a kernel.

hardness

Hardness of a kernel.

size

Size of a kernel.

weight

Weight of a kernel.

moisture

Moisture content of a kernel.

type

Kernel's condition: Healthy, Sprout (sprouted prematurely), or Scab (infected with a fungus).

Details

A study on kernels of wheat was done. There are two classes of wheat: hard and soft red winter wheat. Each kernel measured for density, hardness, size, weight, and moisture content. Each kernel was classified by visual inspection if healthy, sprouted, or scab. A row in the data frame represents a kernel of wheat.

References

Bilder, C.R., & Loughin, T.M. (2014). Analysis of Categorical Data with R (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/b17211

Martin, C., Herrman, T.J., Loughin, T. and Oentong, S. (1998), Micropycnometer Measurement of Single-Kernel Density of Healthy, Sprouted, and Scab\-Damaged Wheats†. Cereal Chemistry, 75: 177-180. https://doi-org.libproxy.uwyo.edu/10.1094/CCHEM.1998.75.2.177

Examples

data("wheat", package='syllogi')
str(wheat)