R/data.R
In bcBusinessStats/data1135: Data for Business Statistics OPER1135
#' @title GoogleAnalyticsData
#' @description Sample of Google Analytics data for a website over several months. Possibly made up.
#' @format A data frame with 5732 rows and 7 variables:
#' \describe{
#' \item{\code{date}}{double date in Y-m-d format}
#' \item{\code{channelGrouping}}{character how site viewer found site}
#' \item{\code{deviceCategory}}{character type of device used to view site}
#' \item{\code{sessions}}{integer total sessions: "a group of user interactions with your website that take place within a given time frame"}
#' \item{\code{pageviews}}{integer total pages viewed by those viewers}
#' \item{\code{entrances}}{integer number of entrances, see https://support.google.com/analytics/answer/2956047?hl=en}
#' \item{\code{bounces}}{integer viewers who leave site after viewing exactly one page}
#'}
#' @source \url{http://www.dartistics.com/data/gadata_example_2.csv}
"GoogleAnalyticsData"
#' @title mortality
#' @description Data on whether a term-life policy was claimed during the term. Made-up for lab on one-sample h-tests for proportion.
#' @format A data frame with 2021 rows and 2 variables:
#' \describe{
#' \item{\code{ID}}{integer policy holder ID}
#' \item{\code{Claimed}}{integer 1 if made claim on term life insturance; 0 otherwise}
#'}
#' @source {Made up by Stephanie Jernigan, June 2018}
"mortality"
#' @title mlb11
#' @description Data from all 30 Major League Baseball teams from the 2011 season. This data set is useful for examining the relationships between wins, runs scored in a season, and a number of other player statistics.
#' @format A data frame with 30 rows and 12 variables:
#' \describe{
#' \item{\code{team}}{factor Team name}
#' \item{\code{runs}}{integer number of runs}
#' \item{\code{at_bats}}{integer number of at bats}
#' \item{\code{hits}}{integer number of hits}
#' \item{\code{homeruns}}{integer number of homeruns}
#' \item{\code{bat_avg}}{double batting average}
#' \item{\code{strikeouts}}{integer number of strikeouts}
#' \item{\code{stolen_bases}}{integer number of stolen bases}
#' \item{\code{wins}}{integer number of strikeouts}
#' \item{\code{new_onbase}}{double On base percentage, a measure of how often a batter reaches base for any reason other than a fielding error, fielder's choice, dropped/uncaught third strike, fielder's obstruction, or catcher's interference}
#' \item{\code{new_slug}}{double Slugging percentage, popular measure of the power of a hitter calculated as the total bases divided by at bats}
#' \item{\code{new_obs}}{double On base plus slugging, calculated as the sum of these two variables}
#'}
#' @source \url{https://www.openintro.org/stat/data/mlb11.php, via MLB.com}
"mlb11"
#' @title snsdata
#' @description Excerpt from Pew Research Study on Teens, Social Media, and Privacy from May 21, 2013. Survey of 12 - 17 year olds; only those teens who used at least one social network site were selected for this excerpt.
#' @format A data frame with 609 rows and 4 variables:
#' \describe{
#' \item{\code{gender}}{character gender (male or female)}
#' \item{\code{age}}{integer age}
#' \item{\code{primary_sns}}{character primary social network used by tenn}
#' \item{\code{how_often_visit}}{character how often do they visit their primary social network}
#'}
#' @source \url{http://www.pewinternet.org/dataset/september-2012-teens-and-online-privacy/}
"SNSdata"
#' @title evals
#' @description The data are gathered from end of semester student evaluations for a large sample of professors from the University of Texas at Austin. In addition, six students rate the professors' physical appearance. The result is a data frame where each row contains a different course and each column has information on either the course or the professor. For more information, please see Çetinkaya-Rundel M, Morgan KL, Stangl D. 2013 (coming soon). Looking Good on Course Evaluations. CHANCE 26(2).
#' @format A data frame with 463 rows and 21 variables:
#' \describe{
#' \item{\code{score}}{double Average professor evaluation score}
#' \item{\code{rank}}{integer Rank of professor: teaching, tenure track, tenured.}
#' \item{\code{ethnicity}}{integer Ethnicity of professor: not minority, minority}
#' \item{\code{gender}}{integer Gender of professor: female, male.}
#' \item{\code{language}}{integer Language of school where professor received education: English or non-English.}
#' \item{\code{age}}{integer Language of school where professor received education: English or non-English.}
#' \item{\code{cls_perc_eval}}{double Percent of students in class who completed evaluation.}
#' \item{\code{cls_did_eval}}{integer Number of students in class who completed evaluation.}
#' \item{\code{cls_students}}{integer Total number of students in class.}
#' \item{\code{cls_level}}{integer Class level: lower, upper.}
#' \item{\code{cls_profs}}{integer Number of professors teaching sections in course in sample: single, multiple.}
#' \item{\code{cls_credits}}{integer Number of credits of class: one credit (lab, PE, etc.), multi credit.}
#' \item{\code{bty_f1lower}}{integer Beauty rating of professor from lower level female: (1) lowest - (10) highest.}
#' \item{\code{bty_f1upper}}{integer Beauty rating of professor from upper level female: (1) lowest - (10) highest.}
#' \item{\code{bty_f2upper}}{integer Beauty rating of professor from second level female: (1) lowest - (10) highest.}
#' \item{\code{bty_m1lower}}{integer Beauty rating of professor from lower level male: (1) lowest - (10) highest.}
#' \item{\code{bty_m1upper}}{integer Beauty rating of professor from upper level male: (1) lowest - (10) highest.}
#' \item{\code{bty_m2upper}}{integer Beauty rating of professor from second upper level male: (1) lowest - (10) highest.}
#' \item{\code{pic_outfit}}{integer Outfit of professor in picture: not formal, formal.}
#' \item{\code{pic_color}}{integer Color of professor’s picture: color, black & white.}
#'}
#' @source \url{https://www.openintro.org/stat/data/evals.php}
"evals"
#' @title moviedata
#' @description made-up data for HW about movie attendance
#' @format A data frame with 487 rows and 2 variables:
#' \describe{
#' \item{\code{AgeGroup}}{character: age group of viewer}
#' \item{\code{SeenBefore}}{character: had viewer seen before?}
#'}
"moviedata"
#' @title creditcards
#' @description Credit card customer data
#' @format A data frame with 400 rows and 11 variables:
#' \describe{
#' \item{\code{ID}}{integer: customer ID}
#' \item{\code{Income}}{double: Income, in $1000s}
#' \item{\code{Limit}}{integer: Credit limit, in $}
#' \item{\code{Rating}}{integer: Credit rating score from 0 to 1000 possible.}
#' \item{\code{Cards}}{integer: total number of credit cards customer has}
#' \item{\code{Age}}{integer: Customer age in years}
#' \item{\code{Education}}{integer: years of education (12 = high school)}
#' \item{\code{Gender}}{character: Customer gender}
#' \item{\code{Student}}{character: 1 if student; 0 otherwise}
#' \item{\code{Married}}{character: 1 if married; 0 otherwise}
#' \item{\code{Balance}}{integer: Average monthly balance, in $}
#'}
#' @source {adjusted from a sample of 'credit' dataset in 'ISLR' package.}
"creditcards"
#' @title distfromhome
#' @description Simulated data set with distance from home (miles) of students from 4 business programs
#' @format A data frame with 375 rows and 2 variables:
#' \describe{
#' \item{\code{School}}{character: School identifier}
#' \item{\code{Distance}}{double: distance from home in miles}
#'}
#' @source {made up for an in-class exercise. BC data comes from a class survey.}
"distfromhome"
#' @title distfromhome_bc
#' @description dataset from previous class about distance students travel to attend BC.
#' @format A data frame with 97 rows and 2 variables:
#' \describe{
#' \item{\code{School}}{character: school identifier (only BC)}
#' \item{\code{Distance}}{double: distance from home in miles}
#'}
#' @source {from previous class survey}
"distfromhome_bc"
#' @title foodads
#' @description simulated data about amount of Goldfish crackers eaten by two different groups of children: those who watched a TV program with food ads and those who watched the TV program with non-food ads.
#' @format A data frame with 118 rows and 2 variables:
#' \describe{
#' \item{\code{AdType}}{character: type of ad child watched, either food or nonfood}
#' \item{\code{GramsEaten}}{double: grams of Goldfish crackers child ate while watching}
#'}
#' @source \url{https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2743554/}
"foodads"
#' @title gettysburg
#' @description The words in the Gettysburg address
#' @format A data frame with 268 rows and 3 variables:
#' \describe{
#' \item{\code{words}}{character: the words in the Gettysburg Address}
#' \item{\code{length}}{integer: number of letters in the word}
#' \item{\code{has_an_e}}{integer: 1 if contains an e; 0 otherwise}
#'}
#' @source {parsed Gettysburg Address}
"gettysburg"
#' @title marylou
#' @description age group and purchase amount for 50 customers of a coffee chain
#' @format A data frame with 50 rows and 2 variables:
#' \describe{
#' \item{\code{CustomerType}}{character: adult or teenager}
#' \item{\code{Spend}}{double amount of purchase when surveyed, in $}
#'}
#' @source \url{adapted from exercise in Essentials of Business Statistics, 1st Edition, Sanjiv Jaggia and Alison Kelly , Copyright: 2014}
"marylou"
#' @title shopgender
#' @description simulated data to match USA Today poll about gender and shopping
#' @format A data frame with 1085 rows and 2 variables:
#' \describe{
#' \item{\code{Gender}}{character Male or Female}
#' \item{\code{LikesToShop}}{character yes or no}
#'}
#' @source \url{adapted from exercise in Business Statistics - A First Course, 6th Edition, Levine, Krehbiel, and Berenson, 2012}
"shopgender"
#' @title vendor
#' @description appraisals for electronic items from two different appraisal companies (vendors)
#' @format A data frame with 453 rows and 3 variables:
#' \describe{
#' \item{\code{Item}}{character ID of electronic item being appraised}
#' \item{\code{VendorA}}{double appraisal value from Vendor A}
#' \item{\code{VendorB}}{double appraisal value from Vendor B}
#'}
#' @source {donated by former student of Linda Boardman Liu}
"vendor"
#' @title accts_recv
#' @description simulated data from IBM Watson about "Understand "the factors of successful collection efforts. You can Predict which customers will pay fastest and recover more money and improve collections efficiency."
#' @format A data frame with 2466 rows and 12 variables:
#' \describe{
#' \item{\code{countryCode}}{integer: country identifier}
#' \item{\code{customerID}}{character: customer identifier}
#' \item{\code{PaperlessDate}}{character: date the company began paperless billing}
#' \item{\code{invoiceNumber}}{character: invoice identifier}
#' \item{\code{InvoiceDate}}{character: date of invoice}
#' \item{\code{DueDate}}{character: day payment due}
#' \item{\code{InvoiceAmount}}{double: amount of invoice, in $}
#' \item{\code{Disputed}}{character: Yes if invoice was disputed; No otherwise.}
#' \item{\code{SettledDate}}{character: Date that invoice was paid or settled.}
#' \item{\code{PaperlessBill}}{character: Paper if a hard copy of the invoice was sent; Electronic otherwise}
#' \item{\code{DaysToSettle}}{integer: number of days required to settle the invoice}
#' \item{\code{DaysLate}}{integer: number of days late the invoice was}
#'}
#' @source \url{https://www.ibm.com/communities/analytics/watson-analytics-blog/guide-to-sample-datasets/}
"accts_recv"
#' @title bikeshare
#' @description hourly information about a public bike share program in Washington, D.C.
#' @format A data frame with 17379 rows and 12 variables:
#' \describe{
#' \item{\code{Instant}}{integer: time slice identifier}
#' \item{\code{Riders}}{integer: number of riders during at this time}
#' \item{\code{Season}}{integer: 1 = winter, 2 = spring, 3 = summer, 4 = fall}
#' \item{\code{Month}}{integer: month}
#' \item{\code{Hour}}{integer: hour}
#' \item{\code{Holiday}}{integer: 1 if holiday; 0 otherwise}
#' \item{\code{Weekday}}{integer: 0 through 6 representing the day of the week, Monday to Sunday}
#' \item{\code{Workday}}{integer: 1 if workday; 0 otherwise}
#' \item{\code{Weather}}{integer: general category of weather. Exact definition unknown.}
#' \item{\code{Temperature}}{integer: temperature in degrees F}
#' \item{\code{Humidity}}{double: relative humidity measure}
#' \item{\code{Wind}}{double: an unknown measure of wind speed}
#'}
#' @source \url{https://www.ibm.com/communities/analytics/watson-analytics-blog/guide-to-sample-datasets/}
"bikeshare"
#' @title atus_summary
#' @description Summary of results of US gov't American Time Use Survey
#' @format A data frame with 130150 rows and 23 variables:
#' \describe{
#' \item{\code{Education Level}}{character COLUMN_DESCRIPTION}
#' \item{\code{Age}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Age Range}}{character COLUMN_DESCRIPTION}
#' \item{\code{Employment Status}}{character COLUMN_DESCRIPTION}
#' \item{\code{Gender}}{character COLUMN_DESCRIPTION}
#' \item{\code{Children}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Weekly Earnings}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Year}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Weekly Hours Worked}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Sleeping}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Grooming}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Housework}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Food & Drink Prep}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Caring for Children}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Playing with Children}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Job Searching}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Shopping}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Eating and Drinking}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Socializing & Relaxing}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Television}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Golfing}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Running}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Volunteering}}{integer COLUMN_DESCRIPTION}
#'}
#' @source \url{https://www.ibm.com/communities/analytics/watson-analytics-blog/guide-to-sample-datasets/}
"atus_summary"
#' @title popcorn_data
#' @description Popcorn sales by Cub Scouts in 2017 and 2018
#' @format A data frame with 134 rows and 5 variables:
#' \describe{
#' \item{\code{ID}}{character: identifier}
#' \item{\code{rank}}{character: scout rank}
#' \item{\code{grade}}{integer: scout's grade in school}
#' \item{\code{sale}}{double: amount of popcorn sold by scout}
#' \item{\code{year}}{integer: year of sale}
#'}
#' @source {data collected from Cub Scout Pack 658 by Linda Boardman Liu}
"popcorn_data"
#' @title AttendGrad
#' @description data from past class survey about whether or not students plan to attend grad school
#' @format A data frame with 97 rows and 1 variables:
#' \describe{
#' \item{\code{attend_grad}}{character Yes, No, or Not sure}
#'}
#' @source {past survey in Business Statistics}
"AttendGrad"
#' @title FilmAges
#' @description made-up data set for HW Ch. 4
#' @format A data frame with 468 rows and 1 variables:
#' \describe{
#' \item{\code{ages}}{character: age groups who attended movie}
#'}
"FilmAges"
#' @title EdLevel
#' @description made-up data set for HW Ch. 4
#' @format A data frame with 510 rows and 1 variables:
#' \describe{
#' \item{\code{edlevel}}{character: highest education level attained}
#'}
"EdLevel"
#' @title ConcChoice
#' @description Business Statistics students' choices of concentrations if they had to decide today.
#' @format A data frame with 49 rows and 1 variables:
#' \describe{
#' \item{\code{conc_choice}}{character: The name of the concentration they chose.}
#'}
#' @source {past survey of Business Statistics students}
"ConcChoice"
#' @title bluebook
#' @description sample of used car attributes and sales prices
#' @format A data frame with 804 rows and 3 variables:
#' \describe{
#' \item{\code{Price}}{double: sales price}
#' \item{\code{Mileage}}{double: miles on odometer at time of sale}
#' \item{\code{Make}}{character: car manufacturer}
#'}
#' @source \url{Jaggia and Kelly textbook}
"bluebook"
#' @title burgerking
#' @description nutrition information for Burger King menu items
#' @format A data frame with 32 rows and 10 variables:
#' \describe{
#' \item{\code{item}}{character: menu item name}
#' \item{\code{Serving}}{double: serving size in grams}
#' \item{\code{Calories}}{double: number of calories per serving}
#' \item{\code{Carbs}}{double: grams of carbohydrates per serving}
#' \item{\code{Meat}}{double: 1 if contains meat; 0 otherwise}
#' \item{\code{Total Fat}}{double: grams of fat per serving}
#' \item{\code{Cholesterol}}{double: milligrams of cholesterol per serving}
#' \item{\code{Sodium}}{double: milligrams of sodium per serving}
#' \item{\code{Sugars}}{double: grams of sugar per serving}
#' \item{\code{Protein}}{double: grams of protein per serving}
#'}
#' @source \url{Sharpe, DeVeaux, Velleman electronic chapters}
"burgerking"
#' @title mpgdata
#' @description auto fuel economy and other data
#' @format A data frame with 84 rows and 9 variables:
#' \describe{
#' \item{\code{Model}}{character: model of car}
#' \item{\code{Eng Size}}{double: liters}
#' \item{\code{Cylinders}}{double: number of engine cylinders}
#' \item{\code{MSRP}}{double: suggested retail price, in USD}
#' \item{\code{City Mpg}}{double: city miles per gallon}
#' \item{\code{Highway Mpg}}{double: highway miles per gallon}
#' \item{\code{Weight}}{double: weight in pounds}
#' \item{\code{Type}}{character: car class}
#' \item{\code{Country}}{character: country of manufacturer}
#'}
#' @source \url{Sharpe, DeVeaux, Velleman electronic chapters}
"mpgdata"
#' @title decindep
#' @description words in the U.S. Declaration of Independence
#' @format A data frame with 1323 rows and 3 variables:
#' \describe{
#' \item{\code{Word}}{character: word}
#' \item{\code{Length}}{double: number of letters in word}
#' \item{\code{Has_an_e}}{logical: TRUE if word contains an e}
#'}
#' @source \url{http://somewhere.important.com/}
"decindep"
#' @title PopcornData
#' @description Popcorn sales by Cub Scouts in 2017 and 2018
#' @format A data frame with 134 rows and 5 variables:
#' \describe{
#' \item{\code{id}}{character: identifier}
#' \item{\code{rank}}{character: scout rank}
#' \item{\code{grade}}{integer: scout's grade in school}
#' \item{\code{sale}}{double: dollar amount of popcorn sold by scout}
#' \item{\code{year}}{integer: year of sale}
#'}
#' @source {data collected from Cub Scout Pack 658 by Linda Boardman Liu}
"PopcornData"
#' @title ZagatData
#' @description Zagat survey ratings with average menu prices and locations
#' @format A data frame with 100 rows and 6 variables:
#' \describe{
#' \item{\code{location}}{character: restaurant location, suburban or city}
#' \item{\code{food}}{integer: food rating on 30 point scale}
#' \item{\code{decor}}{integer: decor rating on 30 point scale}
#' \item{\code{service}}{integer: service rating on 30 point scale}
#' \item{\code{totalrating}}{integer: sum of food, decor, and service ratings}
#' \item{\code{avgprice}}{integer: average menu price per person}
#'}
#' @source {from Zagat}
"ZagatData"
#' @title AirlineData
#' @description On-time performance of airline arrivals
#' @format A data frame with 50 rows and 4 variables:
#' \describe{
#' \item{\code{airline}}{character: airline}
#' \item{\code{status}}{character: delay status}
#' \item{\code{minutes}}{integer: actual number of minutes deviation from schedule, negative indicates minutes early, positive indicates minutes late)}
#'}
#' @source {}
"AirlineData"
#' @title CoffeeTypeData
#' @description Customer type of coffee purchases
#' @format A data frame with 50 rows and 1 variables:
#' \describe{
#' \item{\code{type}}{character: purchaser type}
#'}
#' @source {}
"CoffeeTypeData"
#' @title CollegeRetData
#' @description Retention data for college students
#' @format A data frame with 603 rows and 4 variables:
#' \describe{
#' \item{\code{currentyear}}{character: student's year in college}
#' \item{\code{status}}{character: student's returning status for next college year}
#'}
#' @source {}
"CollegeRetData"
#' @title CreditCardData
#' @description Data set of credit card customers
#' @format A data frame with 401 rows and 11 variables:
#' \describe{
#' \item{\code{id}}{integer: identifier}
#' \item{\code{income}}{double: in thousands of US dollars}
#' \item{\code{limit}}{integer: credit limit in US dollars}
#' \item{\code{rating}}{integer: internal bank credit rating, a number between 300 and 850}
#' \item{\code{cards}}{integer: number of credit cards}
#' \item{\code{age}}{integer: in years}
#' \item{\code{education}}{integer: years of education}
#' \item{\code{gender}}{character: male or female}
#' \item{\code{student}}{character: customer student indicator, yes or no}
#' \item{\code{married}}{character: customer marital status indicator, yes or no}
#' \item{\code{balance}}{integer: average credit card debt in US dollars}
#'}
#' @source {}
"CreditCardData"
#' @title EmailAccessData
#' @description Download speeds of two email systems
#' @format A data frame with 15 rows and 2 variables:
#' \describe{
#' \item{\code{emailv1}}{double: Version 1 download speed in seconds}
#' \item{\code{emailv2}}{double: Version 2 download speed in seconds}
#'}
#' @source {}
"EmailAccessData"
#' @title RectanglesData
#' @description Actual sizes of rectangles exercise
#' @format A data frame with 100 rows and 3 variables:
#' \describe{
#' \item{\code{Number}}{integer: identifier}
#' \item{\code{Actual Sizes}}{integer: number of rectangles}
#' \item{\code{OddEven}}{character: categorical indicating odd or even number of rectangles}
#'}
#' @source {}
"RectanglesData"
#' @title UltraGreenData
#' @description Ultra green car mileage per gallon
#' @format A data frame with 25 rows and 2 variables:
#' \describe{
#' \item{\code{carID}}{integer: identifier}
#' \item{\code{mpg}}{integer: average miles per gallon}
#'}
#' @source {}
"UltraGreenData"
#' @title VendorSelectionData
#' @description insurance replacement values for a collection of consumer electronics
#' @format A data frame with 454 rows and 3 variables:
#' \describe{
#' \item{\code{item}}{character: identifier}
#' \item{\code{vendorA}}{double: Vendor A replacement value quote}
#' \item{\code{vendorB}}{double: Vendor B replacement value quote}
#'}
#' @source {collected from insurance company}
"VendorSelectionData"
#' @title CoffeeSaleData
#' @description Sample of individual customer coffee sales
#' @format A data frame with 50 rows and 1 variables:
#' \describe{
#' \item{\code{sales}}{double: customer level sales in US dollars}
#'}
#' @source {}
"CoffeeSaleData"
#' @title adultdata
#' @description Data extracted from the 1994 census bureau database
#' @format A data frame with 32561 rows and 15 variables:
#' \describe{
#' \item{\code{age}}{double: age in years}
#' \item{\code{workclass}}{character categories: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked}
#' \item{\code{fnlwgt}}{double final weight index determined by: the estimated population 16+ for each state, origin, race, age and sex. People with similar demographic characteristics should have similar weights. see https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.names for a more detailed description}
#' \item{\code{education}}{character categories: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool}
#' \item{\code{education_num}}{double number of education years}
#' \item{\code{marital_status}}{character categories: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse}
#' \item{\code{occupation}}{character categories: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces}
#' \item{\code{relationship}}{character categories: relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried}
#' \item{\code{race}}{character categories: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black}
#' \item{\code{sex}}{character categories: Female, Male}
#' \item{\code{capital_gain}}{double capital gain from the year}
#' \item{\code{capital_loss}}{double capital loss from the year}
#' \item{\code{hours_per_week}}{double number of hours worked per week}
#' \item{\code{native_country}}{character categories: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands}
#' \item{\code{morethan50k}}{character categories: >50K (annual income greater than $50K), <=50K (annual income less than $50K)}
#'}
#' @source \url{http://www.census.gov/ftp/pub/DES/www/welcome.html}
"adultdata"
#' @title restloc
#' @description demographic
#' @format A data frame with 33 rows and 5 variables:
#' \describe{
#' \item{\code{AnnSales}}{double: annual sales, in $1000s}
#' \item{\code{RestNear}}{double: number of similar restaurants within a half-mile radius}
#' \item{\code{PopulationK}}{double: COLUMN_DESCRIPTION}
#' \item{\code{MedIncome}}{double: population in 1000s}
#' \item{\code{InMall}}{double: 1 if restaurant is in a mall; 0 otherwise}
#'}
#' @source \url{}
"restloc"
#' @title cardata
#' @description data about used cars from the Kelley Blue book
#' @format A data frame with 804 rows and 3 variables:
#' \describe{
#' \item{\code{Price}}{double: price of used car in US$}
#' \item{\code{Mileage}}{double: miles on odometer at time of sale}
#' \item{\code{Make}}{character: brand of car}
#'}
#' @source {}
"cardata"
#' @title bk
#' @description nutrition items for foods at Burger King
#' @format A data frame with 32 rows and 9 variables:
#' \describe{
#' \item{\code{serving}}{double: serving size, in grams}
#' \item{\code{calories}}{double: calories per serving}
#' \item{\code{carbs}}{double: grams of carbohydrates}
#' \item{\code{meat}}{double: 1 if item contains meat; 0 otherwise}
#' \item{\code{total_fat}}{double: grams of fat per serving}
#' \item{\code{cholesterol}}{double: milligrams of cholesterol per serving}
#' \item{\code{sodium}}{double milligrams of sodium per serving}
#' \item{\code{sugars}}{double: grams of sugar per serving}
#' \item{\code{protein}}{double: grams of protein per serving}
#'}
#' @source {Sharpe, De Veaux, Velleman: Business Statistics, A First Course, 2e, online chapters}
"bk"
#' @title mileage
#' @description fuel efficiency information for many types of cars
#' @format A data frame with 84 rows and 9 variables:
#' \describe{
#' \item{\code{model}}{character: car model}
#' \item{\code{eng_size}}{double: engine size, in liters}
#' \item{\code{cylinders}}{double: number of cylinders in engine}
#' \item{\code{msrp}}{double: suggested retail price of car, USD}
#' \item{\code{city_mpg}}{double: average miles per gallon, city driving}
#' \item{\code{highway_mpg}}{double: average miles per gallon, highway driving}
#' \item{\code{weight}}{double: weight, pounds}
#' \item{\code{type}}{character: type of car}
#' \item{\code{country}}{character: country where manufacturer is based}
#'}
#' @source {Sharpe, De Veaux, Velleman: Business Statistics, A First Course, 2e, online chapters}
"mileage"
#' @title complaints
#' @description complaints recorded about customer service agents before training and after training
#' @format A data frame with 31 rows and 2 variables:
#' \describe{
#' \item{\code{complaints_before}}{double: average complaints per week before training}
#' \item{\code{complaints_after}}{double: average complaints per week after training}
#'}
#' @source {}
"complaints"
#' @title PearsonTitanic
#' @description Titanic survival data set from Pearson
#' @format A data frame with 2201 rows and 4 variables:
#' \describe{
#' \item{\code{class}}{character: fare class}
#' \item{\code{adult_child}}{character: age category recorded as Adult or Child}
#' \item{\code{male_female}}{character: gender recorded as Male or Female}
#' \item{\code{survival}}{character: survival recorded as Alive or Dead}
#'}
#' @source {Pearson Publishing}
"PearsonTitanic"
#' @title PearsonDealerships
#' @description information about car dealerships from Pearson
#' @format A data frame with 1000 rows and 7 variables:
#' \describe{
#' \item{\code{dealership_number}}{double: dealership ID}
#' \item{\code{state}}{character: state dealership is located in}
#' \item{\code{median_salary}}{double: median salary of employees in $}
#' \item{\code{annual_sales}}{double: annual sales of dealership, in $}
#' \item{\code{number_of_vehicles_sold}}{double: number of vehicles sold in a period}
#' \item{\code{square_feet}}{double: square feet of dealership}
#' \item{\code{quality_award_winner}}{character: did they win a quality award, y or n}
#'}
#' @source {Pearson Publishing}
"PearsonDealerships"
#' @title Pearson Fuel Efficiency
#' @description Fuel efficiency data for a number of cars
#' @format A data frame with 84 rows and 9 variables:
#' \describe{
#' \item{\code{model}}{character: car model}
#' \item{\code{eng_size}}{double: engine size, in liters}
#' \item{\code{cylinders}}{double: number of cylinders in engine}
#' \item{\code{msrp}}{double: suggested retail price of car, USD}
#' \item{\code{city_mpg}}{double: average miles per gallon, city driving}
#' \item{\code{highway_mpg}}{double: average miles per gallon, highway driving}
#' \item{\code{weight}}{double: weight, pounds}
#' \item{\code{type}}{character: type of car}
#' \item{\code{country}}{character: country where manufacturer is based}
#'}
#' @source {Pearson Publishing}
"PearsonFuelEff"
#' @title PearsonIQ
#' @description IQ and demographic data for 500 respondents
#' @format A data frame with 500 rows and 7 variables:
#' \describe{
#' \item{\code{volunteer_number}}{double: ID of respondent}
#' \item{\code{gender}}{character: gender}
#' \item{\code{iq}}{double: IQ score}
#' \item{\code{annual_income}}{double: annual income, in dollars}
#' \item{\code{pre_test_score}}{double: pre-test score}
#' \item{\code{lifetime_savings}}{double: lifetime savings, in $}
#' \item{\code{gifted}}{character: identified as gifted recorded as y or n}
#'}
#' @source \url{Pearson Publishing}
"PearsonIQ"
#' @title PearsonFuelEconomy
#' @description DATASET_DESCRIPTION
#' @format A data frame with 750 rows and 7 variables:
#' \describe{
#' \item{\code{vehicle_number}}{double: observation ID}
#' \item{\code{type}}{character: car class}
#' \item{\code{vehicle_weight}}{double: weight of vehicle in pounds}
#' \item{\code{average_mpg}}{double: average miles per gallon}
#' \item{\code{fuel_tank_size_gallons}}{double: size of fuel tank in gallons}
#' \item{\code{engine_size_l}}{double: size of engine in liters}
#' \item{\code{meet_or_not_meet_current_standards}}{character: does car meet current fuel standards, recorded as y or n.}
#'}
#' @source {Pearson Publishing}
"PearsonFuelEconomy"
#' @title PearsonClassSize
#' @description demogaraphic information about class sizes and performance
#' @format A data frame with 300 rows and 6 variables:
#' \describe{
#' \item{\code{campus}}{character: which campus}
#' \item{\code{class_size}}{double: number of students in class}
#' \item{\code{average_final_grade}}{double: average final numeric grade}
#' \item{\code{number_of_f_s}}{double: how many Fs given in the class}
#' \item{\code{average_g_p_a}}{double: average GPA of students in class}
#' \item{\code{successful_or_unsuccessful}}{character:}
#'}
#' @source {Pearson Publishing}
"PearsonClassSize"
#' @title PearsonCrackerBarrel
#' @description information about various Cracker Barrel restaurants
#' @format A data frame with 150 rows and 7 variables:
#' \describe{
#' \item{\code{restaurant_number}}{double: restaurant ID}
#' \item{\code{geographic_region}}{character: geographic region}
#' \item{\code{annual_revenue}}{double: annual revenue in $}
#' \item{\code{average_cost_of_gasoline}}{double: average cost of gasoline near restaurant}
#' \item{\code{miles_from_interstate}}{double: distance from nearest interstate, in miles}
#' \item{\code{square_feet}}{double: size of restaurant in square feet}
#' \item{\code{annual_increase_in_revenue}}{character: did revenue increase this year over the previous year, coded as y or n.}
#'}
#' @source {Pearson Publishing}
"PearsonCrackerBarrel"
#' @title HouseSalesData
#' @description Collection of variables that describe house sales in one community.
#' @format A data frame with 1460 rows and 80 variables:
#' \describe{
#' \item{\code{Id}}{double Identifier}
#' \item{\code{MsZoning}}{character zoning type}
#' \item{\code{LotFrontage}}{double linear feet of property frontage to street}
#' \item{\code{LotArea}}{double area in square feet of plot}
#' \item{\code{Street}}{character type of street}
#' \item{\code{Alley}}{character indicator for alley}
#' \item{\code{LotShape}}{character descriptor for shape of land plot}
#' \item{\code{LandContour}}{character contour of land}
#' \item{\code{Utilities}}{character utility type to property}
#' \item{\code{LotConfig}}{character configuration of plot}
#' \item{\code{LandSlope}}{character how sloped is the plot}
#' \item{\code{Neighborhood}}{character neighborhood}
#' \item{\code{Condition1}}{character }
#' \item{\code{Condition2}}{character }
#' \item{\code{BldgType}}{character }
#' \item{\code{HouseStyle}}{character }
#' \item{\code{OverallQual}}{double quality rating, 1 – 10 scale}
#' \item{\code{OverallCond}}{double condition rating, 1 – 10 scale}
#' \item{\code{YearBuilt}}{double }
#' \item{\code{YearRemodAdd}}{double }
#' \item{\code{RoofStyle}}{character type of roof on property}
#' \item{\code{RoofMatl}}{character material used in roof}
#' \item{\code{Exterior1St}}{character }
#' \item{\code{Exterior2Nd}}{character }
#' \item{\code{MasVnrType}}{character }
#' \item{\code{MasVnrArea}}{double COLUMN }
#' \item{\code{ExterQual}}{character }
#' \item{\code{ExterCond}}{character }
#' \item{\code{Foundation}}{character }
#' \item{\code{BsmtQual}}{character }
#' \item{\code{BsmtCond}}{character }
#' \item{\code{BsmtExposure}}{character }
#' \item{\code{BsmtFinType1}}{character }
#' \item{\code{BsmtFinSf1}}{double }
#' \item{\code{BsmtFinType2}}{character }
#' \item{\code{BsmtFinSf2}}{double }
#' \item{\code{BsmtUnfSf}}{double }
#' \item{\code{TotalBsmtSf}}{double }
#' \item{\code{Heating}}{character }
#' \item{\code{HeatingQc}}{character }
#' \item{\code{CentralAir}}{character }
#' \item{\code{Electrical}}{character }
#' \item{\code{X1StFlrSf}}{double }
#' \item{\code{X2NdFlrSf}}{double }
#' \item{\code{LowQualFinSf}}{double }
#' \item{\code{GrLivArea}}{double }
#' \item{\code{BsmtFullBath}}{double }
#' \item{\code{BsmtHalfBath}}{double }
#' \item{\code{FullBath}}{double }
#' \item{\code{HalfBath}}{double }
#' \item{\code{BedroomAbvGr}}{double }
#' \item{\code{KitchenAbvGr}}{double }
#' \item{\code{KitchenQual}}{character }
#' \item{\code{TotRmsAbvGrd}}{double }
#' \item{\code{Functional}}{character }
#' \item{\code{Fireplaces}}{double }
#' \item{\code{FireplaceQu}}{character }
#' \item{\code{GarageType}}{character }
#' \item{\code{GarageYrBlt}}{double }
#' \item{\code{GarageFinish}}{character }
#' \item{\code{GarageCars}}{double }
#' \item{\code{GarageArea}}{double }
#' \item{\code{GarageQual}}{character }
#' \item{\code{GarageCond}}{character }
#' \item{\code{PavedDrive}}{character }
#' \item{\code{WoodDeckSf}}{double }
#' \item{\code{OpenPorchSf}}{double }
#' \item{\code{EnclosedPorch}}{double }
#' \item{\code{X3SsnPorch}}{double }
#' \item{\code{ScreenPorch}}{double }
#' \item{\code{PoolArea}}{double }
#' \item{\code{PoolQc}}{character }
#' \item{\code{Fence}}{character }
#' \item{\code{MiscFeature}}{character }
#' \item{\code{MiscVal}}{double }
#' \item{\code{MoSold}}{double }
#' \item{\code{YrSold}}{double }
#' \item{\code{SaleType}}{character }
#' \item{\code{SaleCondition}}{character }
#' \item{\code{SalePrice}}{double }
#'}
#'
"HouseSalesData"
#' @title CarInsData
#' @description dataset that includes various attributes about drivers, cars, and car crashes
#' @format A data frame with 500 rows and 31 variables:
#' \describe{
#' \item{\code{Obs}}{double }
#' \item{\code{ID}}{double }
#' \item{\code{Crash}}{double 0/1 indicator for crash where 1 indicates a crash}
#' \item{\code{CrashCost}}{double cost to insurance company of crash}
#' \item{\code{DriverAge}}{double age of driver}
#' \item{\code{LevelEducation}}{character level education of driver}
#' \item{\code{YearsJob}}{double years on current job}
#' \item{\code{Income}}{double income in US dollars}
#' \item{\code{SexofDriver}}{character sex, male or female}
#' \item{\code{TypeofJob}}{character job type}
#' \item{\code{TimeCommute}}{double in minutes, length of regular work commute}
#' \item{\code{PointsonRecord}}{double number points on driving record}
#' \item{\code{LicenseRevoked}}{character 0/1 indicator, 1 indicates has had license revoked}
#' \item{\code{ValueofHome}}{double in US dollars, value of home}
#' \item{\code{MaritalStatus}}{character indicator marital status}
#' \item{\code{SingleParent}}{character indicator single parent status}
#' \item{\code{NumberKidsHome}}{double }
#' \item{\code{NumberKidsDrive}}{double }
#' \item{\code{UrbanorRural}}{character where driver typically lives & drives}
#' \item{\code{AgeofCar}}{double }
#' \item{\code{TypeofCar}}{character }
#' \item{\code{TypeCarUse}}{character how vehicle is typically used}
#' \item{\code{BlueBookValue}}{double replacement value from Blue Book}
#' \item{\code{RedCar}}{character indicator for red cars}
#' \item{\code{TimeasCustomer}}{double number years driver has been a customer}
#' \item{\code{NumberClaims5yr}}{double number of claims in last 5 years}
#' \item{\code{Total5YrPayout}}{double US dollar value payouts of claims in last 5 years}
#' \item{\code{NotWorking}}{character indicator working or not}
#' \item{\code{NewCar}}{character indicator, new car or not}
#' \item{\code{GoodDriver}}{character indicator, good driver or not, based on points}
#' \item{\code{OwnHome}}{character indicator, own or rent, based on home value}
#'}
#'
"CarInsData"
#' @title HeartData
#' @description variables about heart health events
#' @format A data frame with 303 rows and 14 variables:
#' \describe{
#' \item{\code{age}}{double }
#' \item{\code{sex}}{double }
#' \item{\code{cp}}{double }
#' \item{\code{trestbps}}{double }
#' \item{\code{chol}}{double }
#' \item{\code{fbs}}{double }
#' \item{\code{restecg}}{double }
#' \item{\code{thalach}}{double }
#' \item{\code{exang}}{double }
#' \item{\code{oldpeak}}{double }
#' \item{\code{slope}}{double }
#' \item{\code{ca}}{double }
#' \item{\code{thal}}{double }
#' \item{\code{target}}{double }
#'}
#'
"HeartData"
#' @title SpeedData
#' @description email download speeds
#' @format A data frame with 30 rows and 2 variables:
#' \describe{
#' \item{\code{TYPE}}{character email interface application}
#' \item{\code{SPEED}}{double email opening speed in seconds}
#'}
#'
"SpeedData"
#' @title AdsManager
#' @description Fields from Google AdsManager
#' @format A data frame with 2667 rows and 6 variables:
#' \describe{
#' \item{\code{platform}}{character did viewer see ad on Facebook or Instagram}
#' \item{\code{age_group}}{character age group of viewer}
#' \item{\code{device_type}}{character device type viewer used to see ad}
#' \item{\code{clicked}}{character yes if viewer clicked on the ad to navigate to website}
#' \item{\code{pageviews}}{double number of pages viewed after clicking}
#' \item{\code{amt_spent}}{double amount spent on website after clicking}
#'}
#' @source "made-up by Stephanie Jernigan"
"AdsManager"
#' @title EmployeeAtt
#' @description simulated data on employee attrition created by IBM
#' @format A data frame with 1470 rows and 35 variables:
#' \describe{
#' \item{\code{Age}}{double, employee age}
#' \item{\code{Attrition}}{character, Yes if left company, No otherwise}
#' \item{\code{BusinessTravel}}{character, frequency of business travel}
#' \item{\code{DailyRate}}{double, unsure what this field is}
#' \item{\code{Department}}{character, employee's work department}
#' \item{\code{DistanceFromHome}}{double, commute distance in miles}
#' \item{\code{Education}}{double, level of education: lower numbers are less}
#' \item{\code{EducationField}}{character, field of study during education}
#' \item{\code{EmployeeCount}}{double, this field is always 1}
#' \item{\code{EmployeeNumber}}{double, employee ID}
#' \item{\code{EnvironmentSatisfaction}}{double, lower numbers indicate lower satisfaction}
#' \item{\code{Gender}}{character, gender}
#' \item{\code{HourlyRate}}{double, unsure what this field is}
#' \item{\code{JobInvolvement}}{double, lower numbers indicate less involvement}
#' \item{\code{JobLevel}}{double, lower numbers indicate lower level}
#' \item{\code{JobRole}}{character, job role}
#' \item{\code{JobSatisfaction}}{double, lower numbers indicate lower satisfaction}
#' \item{\code{MaritalStatus}}{character, marital status}
#' \item{\code{MonthlyIncome}}{double, monthy income}
#' \item{\code{MonthlyRate}}{double, unsure what this field is}
#' \item{\code{NumCompaniesWorked}}{double, number of companies have worked for previously}
#' \item{\code{Over18}}{character, Y if over 18}
#' \item{\code{OverTime}}{character, not sure what ths field is}
#' \item{\code{PercentSalaryHike}}{double}
#' \item{\code{PerformanceRating}}{double, higher numbers indicate higher rating}
#' \item{\code{RelationshipSatisfaction}}{double, higher numbers indicate higher satisfaction}
#' \item{\code{StandardHours}}{double COLUMN_DESCRIPTION}
#' \item{\code{StockOptionLevel}}{double COLUMN_DESCRIPTION}
#' \item{\code{TotalWorkingYears}}{double COLUMN_DESCRIPTION}
#' \item{\code{TrainingTimesLastYear}}{double COLUMN_DESCRIPTION}
#' \item{\code{WorkLifeBalance}}{double COLUMN_DESCRIPTION}
#' \item{\code{YearsAtCompany}}{double COLUMN_DESCRIPTION}
#' \item{\code{YearsInCurrentRole}}{double COLUMN_DESCRIPTION}
#' \item{\code{YearsSinceLastPromotion}}{double COLUMN_DESCRIPTION}
#' \item{\code{YearsWithCurrManager}}{double COLUMN_DESCRIPTION}
#'}
#' @source \url{https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset/metadata}
"EmployeeAtt"
#' @title CustValue
#' @description Simulated data about auto insurance company and value of customer, by IBM
#' @format A data frame with 9134 rows and 24 variables:
#' \describe{
#' \item{\code{customer}}{character Customer ID}
#' \item{\code{state}}{character U.S. state of residence}
#' \item{\code{customer_lifetime_value}}{double value of customer to company in USD}
#' \item{\code{response}}{character unsure what this is: if responded to marketing?}
#' \item{\code{coverage}}{character type of auto insurance policy}
#' \item{\code{education}}{character level of education}
#' \item{\code{effective_to_date}}{character }
#' \item{\code{employment_status}}{character employment status}
#' \item{\code{gender}}{character gender}
#' \item{\code{income}}{double income, USD}
#' \item{\code{location_code}}{character type of location of policyholder residence}
#' \item{\code{marital_status}}{character marital status}
#' \item{\code{monthly_premium_auto}}{double monthly cost of auto premium}
#' \item{\code{months_since_last_claim}}{double number of months since last claim}
#' \item{\code{months_since_policy_inception}}{double months since became a policyholder}
#' \item{\code{number_of_open_complaints}}{double number of open complaints (unsure made by whom about who)}
#' \item{\code{number_of_policies}}{double number of policies (other insurance policies by same company)}
#' \item{\code{policy_type}}{character type of auto policy}
#' \item{\code{policy}}{character further breakdown of type of auto policy}
#' \item{\code{renew_offer_type}}{character what type of renewal offer were they given?}
#' \item{\code{sales_channel}}{character sales channel}
#' \item{\code{total_claim_amount}}{double total amount of claims made by policyholder?? seems low}
#' \item{\code{vehicle_class}}{character type of vehicle insured}
#' \item{\code{vehicle_size}}{character size of insured vehicle}
#'}
#' @source \url{https://www.kaggle.com/pankajjsh06/ibm-watson-marketing-customer-value-data}
"CustValue"
#' @title HealthIns
#' @description Simulated data about health insurance expenses from Eric Suess
#' @format A data frame with 1338 rows and 7 variables:
#' \describe{
#' \item{\code{age}}{double age in years}
#' \item{\code{sex}}{character gender}
#' \item{\code{bmi}}{double body mass index: higher is generally worse}
#' \item{\code{children}}{double number of children}
#' \item{\code{smoker}}{character yes if smoker; no otherwise}
#' \item{\code{region}}{character one of three regions in the U.S.}
#' \item{\code{expenses}}{double medical expenses in one year in USD}
#'}
#' @source \url{https://www.kaggle.com/noordeen/insurance-premium-prediction?select=insurance.csv}
"HealthIns"
bcBusinessStats/data1135 documentation built on July 26, 2020, 11:37 a.m.
R Package Documentation
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#' @title GoogleAnalyticsData
#' @description Sample of Google Analytics data for a website over several months. Possibly made up.
#' @format A data frame with 5732 rows and 7 variables:
#' \describe{
#' \item{\code{date}}{double date in Y-m-d format}
#' \item{\code{channelGrouping}}{character how site viewer found site}
#' \item{\code{deviceCategory}}{character type of device used to view site}
#' \item{\code{sessions}}{integer total sessions: "a group of user interactions with your website that take place within a given time frame"}
#' \item{\code{pageviews}}{integer total pages viewed by those viewers}
#' \item{\code{entrances}}{integer number of entrances, see https://support.google.com/analytics/answer/2956047?hl=en}
#' \item{\code{bounces}}{integer viewers who leave site after viewing exactly one page}
#'}
#' @source \url{http://www.dartistics.com/data/gadata_example_2.csv}
"GoogleAnalyticsData"
#' @title mortality
#' @description Data on whether a term-life policy was claimed during the term. Made-up for lab on one-sample h-tests for proportion.
#' @format A data frame with 2021 rows and 2 variables:
#' \describe{
#' \item{\code{ID}}{integer policy holder ID}
#' \item{\code{Claimed}}{integer 1 if made claim on term life insturance; 0 otherwise}
#'}
#' @source {Made up by Stephanie Jernigan, June 2018}
"mortality"
#' @title mlb11
#' @description Data from all 30 Major League Baseball teams from the 2011 season. This data set is useful for examining the relationships between wins, runs scored in a season, and a number of other player statistics.
#' @format A data frame with 30 rows and 12 variables:
#' \describe{
#' \item{\code{team}}{factor Team name}
#' \item{\code{runs}}{integer number of runs}
#' \item{\code{at_bats}}{integer number of at bats}
#' \item{\code{hits}}{integer number of hits}
#' \item{\code{homeruns}}{integer number of homeruns}
#' \item{\code{bat_avg}}{double batting average}
#' \item{\code{strikeouts}}{integer number of strikeouts}
#' \item{\code{stolen_bases}}{integer number of stolen bases}
#' \item{\code{wins}}{integer number of strikeouts}
#' \item{\code{new_onbase}}{double On base percentage, a measure of how often a batter reaches base for any reason other than a fielding error, fielder's choice, dropped/uncaught third strike, fielder's obstruction, or catcher's interference}
#' \item{\code{new_slug}}{double Slugging percentage, popular measure of the power of a hitter calculated as the total bases divided by at bats}
#' \item{\code{new_obs}}{double On base plus slugging, calculated as the sum of these two variables}
#'}
#' @source \url{https://www.openintro.org/stat/data/mlb11.php, via MLB.com}
"mlb11"
#' @title snsdata
#' @description Excerpt from Pew Research Study on Teens, Social Media, and Privacy from May 21, 2013. Survey of 12 - 17 year olds; only those teens who used at least one social network site were selected for this excerpt.
#' @format A data frame with 609 rows and 4 variables:
#' \describe{
#' \item{\code{gender}}{character gender (male or female)}
#' \item{\code{age}}{integer age}
#' \item{\code{primary_sns}}{character primary social network used by tenn}
#' \item{\code{how_often_visit}}{character how often do they visit their primary social network}
#'}
#' @source \url{http://www.pewinternet.org/dataset/september-2012-teens-and-online-privacy/}
"SNSdata"
#' @title evals
#' @description The data are gathered from end of semester student evaluations for a large sample of professors from the University of Texas at Austin. In addition, six students rate the professors' physical appearance. The result is a data frame where each row contains a different course and each column has information on either the course or the professor. For more information, please see Çetinkaya-Rundel M, Morgan KL, Stangl D. 2013 (coming soon). Looking Good on Course Evaluations. CHANCE 26(2).
#' @format A data frame with 463 rows and 21 variables:
#' \describe{
#' \item{\code{score}}{double Average professor evaluation score}
#' \item{\code{rank}}{integer Rank of professor: teaching, tenure track, tenured.}
#' \item{\code{ethnicity}}{integer Ethnicity of professor: not minority, minority}
#' \item{\code{gender}}{integer Gender of professor: female, male.}
#' \item{\code{language}}{integer Language of school where professor received education: English or non-English.}
#' \item{\code{age}}{integer Language of school where professor received education: English or non-English.}
#' \item{\code{cls_perc_eval}}{double Percent of students in class who completed evaluation.}
#' \item{\code{cls_did_eval}}{integer Number of students in class who completed evaluation.}
#' \item{\code{cls_students}}{integer Total number of students in class.}
#' \item{\code{cls_level}}{integer Class level: lower, upper.}
#' \item{\code{cls_profs}}{integer Number of professors teaching sections in course in sample: single, multiple.}
#' \item{\code{cls_credits}}{integer Number of credits of class: one credit (lab, PE, etc.), multi credit.}
#' \item{\code{bty_f1lower}}{integer Beauty rating of professor from lower level female: (1) lowest - (10) highest.}
#' \item{\code{bty_f1upper}}{integer Beauty rating of professor from upper level female: (1) lowest - (10) highest.}
#' \item{\code{bty_f2upper}}{integer Beauty rating of professor from second level female: (1) lowest - (10) highest.}
#' \item{\code{bty_m1lower}}{integer Beauty rating of professor from lower level male: (1) lowest - (10) highest.}
#' \item{\code{bty_m1upper}}{integer Beauty rating of professor from upper level male: (1) lowest - (10) highest.}
#' \item{\code{bty_m2upper}}{integer Beauty rating of professor from second upper level male: (1) lowest - (10) highest.}
#' \item{\code{pic_outfit}}{integer Outfit of professor in picture: not formal, formal.}
#' \item{\code{pic_color}}{integer Color of professor’s picture: color, black & white.}
#'}
#' @source \url{https://www.openintro.org/stat/data/evals.php}
"evals"
#' @title moviedata
#' @description made-up data for HW about movie attendance
#' @format A data frame with 487 rows and 2 variables:
#' \describe{
#' \item{\code{AgeGroup}}{character: age group of viewer}
#' \item{\code{SeenBefore}}{character: had viewer seen before?}
#'}
"moviedata"
#' @title creditcards
#' @description Credit card customer data
#' @format A data frame with 400 rows and 11 variables:
#' \describe{
#' \item{\code{ID}}{integer: customer ID}
#' \item{\code{Income}}{double: Income, in $1000s}
#' \item{\code{Limit}}{integer: Credit limit, in $}
#' \item{\code{Rating}}{integer: Credit rating score from 0 to 1000 possible.}
#' \item{\code{Cards}}{integer: total number of credit cards customer has}
#' \item{\code{Age}}{integer: Customer age in years}
#' \item{\code{Education}}{integer: years of education (12 = high school)}
#' \item{\code{Gender}}{character: Customer gender}
#' \item{\code{Student}}{character: 1 if student; 0 otherwise}
#' \item{\code{Married}}{character: 1 if married; 0 otherwise}
#' \item{\code{Balance}}{integer: Average monthly balance, in $}
#'}
#' @source {adjusted from a sample of 'credit' dataset in 'ISLR' package.}
"creditcards"
#' @title distfromhome
#' @description Simulated data set with distance from home (miles) of students from 4 business programs
#' @format A data frame with 375 rows and 2 variables:
#' \describe{
#' \item{\code{School}}{character: School identifier}
#' \item{\code{Distance}}{double: distance from home in miles}
#'}
#' @source {made up for an in-class exercise. BC data comes from a class survey.}
"distfromhome"
#' @title distfromhome_bc
#' @description dataset from previous class about distance students travel to attend BC.
#' @format A data frame with 97 rows and 2 variables:
#' \describe{
#' \item{\code{School}}{character: school identifier (only BC)}
#' \item{\code{Distance}}{double: distance from home in miles}
#'}
#' @source {from previous class survey}
"distfromhome_bc"
#' @title foodads
#' @description simulated data about amount of Goldfish crackers eaten by two different groups of children: those who watched a TV program with food ads and those who watched the TV program with non-food ads.
#' @format A data frame with 118 rows and 2 variables:
#' \describe{
#' \item{\code{AdType}}{character: type of ad child watched, either food or nonfood}
#' \item{\code{GramsEaten}}{double: grams of Goldfish crackers child ate while watching}
#'}
#' @source \url{https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2743554/}
"foodads"
#' @title gettysburg
#' @description The words in the Gettysburg address
#' @format A data frame with 268 rows and 3 variables:
#' \describe{
#' \item{\code{words}}{character: the words in the Gettysburg Address}
#' \item{\code{length}}{integer: number of letters in the word}
#' \item{\code{has_an_e}}{integer: 1 if contains an e; 0 otherwise}
#'}
#' @source {parsed Gettysburg Address}
"gettysburg"
#' @title marylou
#' @description age group and purchase amount for 50 customers of a coffee chain
#' @format A data frame with 50 rows and 2 variables:
#' \describe{
#' \item{\code{CustomerType}}{character: adult or teenager}
#' \item{\code{Spend}}{double amount of purchase when surveyed, in $}
#'}
#' @source \url{adapted from exercise in Essentials of Business Statistics, 1st Edition, Sanjiv Jaggia and Alison Kelly , Copyright: 2014}
"marylou"
#' @title shopgender
#' @description simulated data to match USA Today poll about gender and shopping
#' @format A data frame with 1085 rows and 2 variables:
#' \describe{
#' \item{\code{Gender}}{character Male or Female}
#' \item{\code{LikesToShop}}{character yes or no}
#'}
#' @source \url{adapted from exercise in Business Statistics - A First Course, 6th Edition, Levine, Krehbiel, and Berenson, 2012}
"shopgender"
#' @title vendor
#' @description appraisals for electronic items from two different appraisal companies (vendors)
#' @format A data frame with 453 rows and 3 variables:
#' \describe{
#' \item{\code{Item}}{character ID of electronic item being appraised}
#' \item{\code{VendorA}}{double appraisal value from Vendor A}
#' \item{\code{VendorB}}{double appraisal value from Vendor B}
#'}
#' @source {donated by former student of Linda Boardman Liu}
"vendor"
#' @title accts_recv
#' @description simulated data from IBM Watson about "Understand "the factors of successful collection efforts. You can Predict which customers will pay fastest and recover more money and improve collections efficiency."
#' @format A data frame with 2466 rows and 12 variables:
#' \describe{
#' \item{\code{countryCode}}{integer: country identifier}
#' \item{\code{customerID}}{character: customer identifier}
#' \item{\code{PaperlessDate}}{character: date the company began paperless billing}
#' \item{\code{invoiceNumber}}{character: invoice identifier}
#' \item{\code{InvoiceDate}}{character: date of invoice}
#' \item{\code{DueDate}}{character: day payment due}
#' \item{\code{InvoiceAmount}}{double: amount of invoice, in $}
#' \item{\code{Disputed}}{character: Yes if invoice was disputed; No otherwise.}
#' \item{\code{SettledDate}}{character: Date that invoice was paid or settled.}
#' \item{\code{PaperlessBill}}{character: Paper if a hard copy of the invoice was sent; Electronic otherwise}
#' \item{\code{DaysToSettle}}{integer: number of days required to settle the invoice}
#' \item{\code{DaysLate}}{integer: number of days late the invoice was}
#'}
#' @source \url{https://www.ibm.com/communities/analytics/watson-analytics-blog/guide-to-sample-datasets/}
"accts_recv"
#' @title bikeshare
#' @description hourly information about a public bike share program in Washington, D.C.
#' @format A data frame with 17379 rows and 12 variables:
#' \describe{
#' \item{\code{Instant}}{integer: time slice identifier}
#' \item{\code{Riders}}{integer: number of riders during at this time}
#' \item{\code{Season}}{integer: 1 = winter, 2 = spring, 3 = summer, 4 = fall}
#' \item{\code{Month}}{integer: month}
#' \item{\code{Hour}}{integer: hour}
#' \item{\code{Holiday}}{integer: 1 if holiday; 0 otherwise}
#' \item{\code{Weekday}}{integer: 0 through 6 representing the day of the week, Monday to Sunday}
#' \item{\code{Workday}}{integer: 1 if workday; 0 otherwise}
#' \item{\code{Weather}}{integer: general category of weather. Exact definition unknown.}
#' \item{\code{Temperature}}{integer: temperature in degrees F}
#' \item{\code{Humidity}}{double: relative humidity measure}
#' \item{\code{Wind}}{double: an unknown measure of wind speed}
#'}
#' @source \url{https://www.ibm.com/communities/analytics/watson-analytics-blog/guide-to-sample-datasets/}
"bikeshare"
#' @title atus_summary
#' @description Summary of results of US gov't American Time Use Survey
#' @format A data frame with 130150 rows and 23 variables:
#' \describe{
#' \item{\code{Education Level}}{character COLUMN_DESCRIPTION}
#' \item{\code{Age}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Age Range}}{character COLUMN_DESCRIPTION}
#' \item{\code{Employment Status}}{character COLUMN_DESCRIPTION}
#' \item{\code{Gender}}{character COLUMN_DESCRIPTION}
#' \item{\code{Children}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Weekly Earnings}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Year}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Weekly Hours Worked}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Sleeping}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Grooming}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Housework}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Food & Drink Prep}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Caring for Children}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Playing with Children}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Job Searching}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Shopping}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Eating and Drinking}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Socializing & Relaxing}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Television}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Golfing}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Running}}{integer COLUMN_DESCRIPTION}
#' \item{\code{Volunteering}}{integer COLUMN_DESCRIPTION}
#'}
#' @source \url{https://www.ibm.com/communities/analytics/watson-analytics-blog/guide-to-sample-datasets/}
"atus_summary"
#' @title popcorn_data
#' @description Popcorn sales by Cub Scouts in 2017 and 2018
#' @format A data frame with 134 rows and 5 variables:
#' \describe{
#' \item{\code{ID}}{character: identifier}
#' \item{\code{rank}}{character: scout rank}
#' \item{\code{grade}}{integer: scout's grade in school}
#' \item{\code{sale}}{double: amount of popcorn sold by scout}
#' \item{\code{year}}{integer: year of sale}
#'}
#' @source {data collected from Cub Scout Pack 658 by Linda Boardman Liu}
"popcorn_data"
#' @title AttendGrad
#' @description data from past class survey about whether or not students plan to attend grad school
#' @format A data frame with 97 rows and 1 variables:
#' \describe{
#' \item{\code{attend_grad}}{character Yes, No, or Not sure}
#'}
#' @source {past survey in Business Statistics}
"AttendGrad"
#' @title FilmAges
#' @description made-up data set for HW Ch. 4
#' @format A data frame with 468 rows and 1 variables:
#' \describe{
#' \item{\code{ages}}{character: age groups who attended movie}
#'}
"FilmAges"
#' @title EdLevel
#' @description made-up data set for HW Ch. 4
#' @format A data frame with 510 rows and 1 variables:
#' \describe{
#' \item{\code{edlevel}}{character: highest education level attained}
#'}
"EdLevel"
#' @title ConcChoice
#' @description Business Statistics students' choices of concentrations if they had to decide today.
#' @format A data frame with 49 rows and 1 variables:
#' \describe{
#' \item{\code{conc_choice}}{character: The name of the concentration they chose.}
#'}
#' @source {past survey of Business Statistics students}
"ConcChoice"
#' @title bluebook
#' @description sample of used car attributes and sales prices
#' @format A data frame with 804 rows and 3 variables:
#' \describe{
#' \item{\code{Price}}{double: sales price}
#' \item{\code{Mileage}}{double: miles on odometer at time of sale}
#' \item{\code{Make}}{character: car manufacturer}
#'}
#' @source \url{Jaggia and Kelly textbook}
"bluebook"
#' @title burgerking
#' @description nutrition information for Burger King menu items
#' @format A data frame with 32 rows and 10 variables:
#' \describe{
#' \item{\code{item}}{character: menu item name}
#' \item{\code{Serving}}{double: serving size in grams}
#' \item{\code{Calories}}{double: number of calories per serving}
#' \item{\code{Carbs}}{double: grams of carbohydrates per serving}
#' \item{\code{Meat}}{double: 1 if contains meat; 0 otherwise}
#' \item{\code{Total Fat}}{double: grams of fat per serving}
#' \item{\code{Cholesterol}}{double: milligrams of cholesterol per serving}
#' \item{\code{Sodium}}{double: milligrams of sodium per serving}
#' \item{\code{Sugars}}{double: grams of sugar per serving}
#' \item{\code{Protein}}{double: grams of protein per serving}
#'}
#' @source \url{Sharpe, DeVeaux, Velleman electronic chapters}
"burgerking"
#' @title mpgdata
#' @description auto fuel economy and other data
#' @format A data frame with 84 rows and 9 variables:
#' \describe{
#' \item{\code{Model}}{character: model of car}
#' \item{\code{Eng Size}}{double: liters}
#' \item{\code{Cylinders}}{double: number of engine cylinders}
#' \item{\code{MSRP}}{double: suggested retail price, in USD}
#' \item{\code{City Mpg}}{double: city miles per gallon}
#' \item{\code{Highway Mpg}}{double: highway miles per gallon}
#' \item{\code{Weight}}{double: weight in pounds}
#' \item{\code{Type}}{character: car class}
#' \item{\code{Country}}{character: country of manufacturer}
#'}
#' @source \url{Sharpe, DeVeaux, Velleman electronic chapters}
"mpgdata"
#' @title decindep
#' @description words in the U.S. Declaration of Independence
#' @format A data frame with 1323 rows and 3 variables:
#' \describe{
#' \item{\code{Word}}{character: word}
#' \item{\code{Length}}{double: number of letters in word}
#' \item{\code{Has_an_e}}{logical: TRUE if word contains an e}
#'}
#' @source \url{http://somewhere.important.com/}
"decindep"
#' @title PopcornData
#' @description Popcorn sales by Cub Scouts in 2017 and 2018
#' @format A data frame with 134 rows and 5 variables:
#' \describe{
#' \item{\code{id}}{character: identifier}
#' \item{\code{rank}}{character: scout rank}
#' \item{\code{grade}}{integer: scout's grade in school}
#' \item{\code{sale}}{double: dollar amount of popcorn sold by scout}
#' \item{\code{year}}{integer: year of sale}
#'}
#' @source {data collected from Cub Scout Pack 658 by Linda Boardman Liu}
"PopcornData"
#' @title ZagatData
#' @description Zagat survey ratings with average menu prices and locations
#' @format A data frame with 100 rows and 6 variables:
#' \describe{
#' \item{\code{location}}{character: restaurant location, suburban or city}
#' \item{\code{food}}{integer: food rating on 30 point scale}
#' \item{\code{decor}}{integer: decor rating on 30 point scale}
#' \item{\code{service}}{integer: service rating on 30 point scale}
#' \item{\code{totalrating}}{integer: sum of food, decor, and service ratings}
#' \item{\code{avgprice}}{integer: average menu price per person}
#'}
#' @source {from Zagat}
"ZagatData"
#' @title AirlineData
#' @description On-time performance of airline arrivals
#' @format A data frame with 50 rows and 4 variables:
#' \describe{
#' \item{\code{airline}}{character: airline}
#' \item{\code{status}}{character: delay status}
#' \item{\code{minutes}}{integer: actual number of minutes deviation from schedule, negative indicates minutes early, positive indicates minutes late)}
#'}
#' @source {}
"AirlineData"
#' @title CoffeeTypeData
#' @description Customer type of coffee purchases
#' @format A data frame with 50 rows and 1 variables:
#' \describe{
#' \item{\code{type}}{character: purchaser type}
#'}
#' @source {}
"CoffeeTypeData"
#' @title CollegeRetData
#' @description Retention data for college students
#' @format A data frame with 603 rows and 4 variables:
#' \describe{
#' \item{\code{currentyear}}{character: student's year in college}
#' \item{\code{status}}{character: student's returning status for next college year}
#'}
#' @source {}
"CollegeRetData"
#' @title CreditCardData
#' @description Data set of credit card customers
#' @format A data frame with 401 rows and 11 variables:
#' \describe{
#' \item{\code{id}}{integer: identifier}
#' \item{\code{income}}{double: in thousands of US dollars}
#' \item{\code{limit}}{integer: credit limit in US dollars}
#' \item{\code{rating}}{integer: internal bank credit rating, a number between 300 and 850}
#' \item{\code{cards}}{integer: number of credit cards}
#' \item{\code{age}}{integer: in years}
#' \item{\code{education}}{integer: years of education}
#' \item{\code{gender}}{character: male or female}
#' \item{\code{student}}{character: customer student indicator, yes or no}
#' \item{\code{married}}{character: customer marital status indicator, yes or no}
#' \item{\code{balance}}{integer: average credit card debt in US dollars}
#'}
#' @source {}
"CreditCardData"
#' @title EmailAccessData
#' @description Download speeds of two email systems
#' @format A data frame with 15 rows and 2 variables:
#' \describe{
#' \item{\code{emailv1}}{double: Version 1 download speed in seconds}
#' \item{\code{emailv2}}{double: Version 2 download speed in seconds}
#'}
#' @source {}
"EmailAccessData"
#' @title RectanglesData
#' @description Actual sizes of rectangles exercise
#' @format A data frame with 100 rows and 3 variables:
#' \describe{
#' \item{\code{Number}}{integer: identifier}
#' \item{\code{Actual Sizes}}{integer: number of rectangles}
#' \item{\code{OddEven}}{character: categorical indicating odd or even number of rectangles}
#'}
#' @source {}
"RectanglesData"
#' @title UltraGreenData
#' @description Ultra green car mileage per gallon
#' @format A data frame with 25 rows and 2 variables:
#' \describe{
#' \item{\code{carID}}{integer: identifier}
#' \item{\code{mpg}}{integer: average miles per gallon}
#'}
#' @source {}
"UltraGreenData"
#' @title VendorSelectionData
#' @description insurance replacement values for a collection of consumer electronics
#' @format A data frame with 454 rows and 3 variables:
#' \describe{
#' \item{\code{item}}{character: identifier}
#' \item{\code{vendorA}}{double: Vendor A replacement value quote}
#' \item{\code{vendorB}}{double: Vendor B replacement value quote}
#'}
#' @source {collected from insurance company}
"VendorSelectionData"
#' @title CoffeeSaleData
#' @description Sample of individual customer coffee sales
#' @format A data frame with 50 rows and 1 variables:
#' \describe{
#' \item{\code{sales}}{double: customer level sales in US dollars}
#'}
#' @source {}
"CoffeeSaleData"
#' @title adultdata
#' @description Data extracted from the 1994 census bureau database
#' @format A data frame with 32561 rows and 15 variables:
#' \describe{
#' \item{\code{age}}{double: age in years}
#' \item{\code{workclass}}{character categories: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked}
#' \item{\code{fnlwgt}}{double final weight index determined by: the estimated population 16+ for each state, origin, race, age and sex. People with similar demographic characteristics should have similar weights. see https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.names for a more detailed description}
#' \item{\code{education}}{character categories: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool}
#' \item{\code{education_num}}{double number of education years}
#' \item{\code{marital_status}}{character categories: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse}
#' \item{\code{occupation}}{character categories: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces}
#' \item{\code{relationship}}{character categories: relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried}
#' \item{\code{race}}{character categories: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black}
#' \item{\code{sex}}{character categories: Female, Male}
#' \item{\code{capital_gain}}{double capital gain from the year}
#' \item{\code{capital_loss}}{double capital loss from the year}
#' \item{\code{hours_per_week}}{double number of hours worked per week}
#' \item{\code{native_country}}{character categories: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands}
#' \item{\code{morethan50k}}{character categories: >50K (annual income greater than $50K), <=50K (annual income less than $50K)}
#'}
#' @source \url{http://www.census.gov/ftp/pub/DES/www/welcome.html}
"adultdata"
#' @title restloc
#' @description demographic
#' @format A data frame with 33 rows and 5 variables:
#' \describe{
#' \item{\code{AnnSales}}{double: annual sales, in $1000s}
#' \item{\code{RestNear}}{double: number of similar restaurants within a half-mile radius}
#' \item{\code{PopulationK}}{double: COLUMN_DESCRIPTION}
#' \item{\code{MedIncome}}{double: population in 1000s}
#' \item{\code{InMall}}{double: 1 if restaurant is in a mall; 0 otherwise}
#'}
#' @source \url{}
"restloc"
#' @title cardata
#' @description data about used cars from the Kelley Blue book
#' @format A data frame with 804 rows and 3 variables:
#' \describe{
#' \item{\code{Price}}{double: price of used car in US$}
#' \item{\code{Mileage}}{double: miles on odometer at time of sale}
#' \item{\code{Make}}{character: brand of car}
#'}
#' @source {}
"cardata"
#' @title bk
#' @description nutrition items for foods at Burger King
#' @format A data frame with 32 rows and 9 variables:
#' \describe{
#' \item{\code{serving}}{double: serving size, in grams}
#' \item{\code{calories}}{double: calories per serving}
#' \item{\code{carbs}}{double: grams of carbohydrates}
#' \item{\code{meat}}{double: 1 if item contains meat; 0 otherwise}
#' \item{\code{total_fat}}{double: grams of fat per serving}
#' \item{\code{cholesterol}}{double: milligrams of cholesterol per serving}
#' \item{\code{sodium}}{double milligrams of sodium per serving}
#' \item{\code{sugars}}{double: grams of sugar per serving}
#' \item{\code{protein}}{double: grams of protein per serving}
#'}
#' @source {Sharpe, De Veaux, Velleman: Business Statistics, A First Course, 2e, online chapters}
"bk"
#' @title mileage
#' @description fuel efficiency information for many types of cars
#' @format A data frame with 84 rows and 9 variables:
#' \describe{
#' \item{\code{model}}{character: car model}
#' \item{\code{eng_size}}{double: engine size, in liters}
#' \item{\code{cylinders}}{double: number of cylinders in engine}
#' \item{\code{msrp}}{double: suggested retail price of car, USD}
#' \item{\code{city_mpg}}{double: average miles per gallon, city driving}
#' \item{\code{highway_mpg}}{double: average miles per gallon, highway driving}
#' \item{\code{weight}}{double: weight, pounds}
#' \item{\code{type}}{character: type of car}
#' \item{\code{country}}{character: country where manufacturer is based}
#'}
#' @source {Sharpe, De Veaux, Velleman: Business Statistics, A First Course, 2e, online chapters}
"mileage"
#' @title complaints
#' @description complaints recorded about customer service agents before training and after training
#' @format A data frame with 31 rows and 2 variables:
#' \describe{
#' \item{\code{complaints_before}}{double: average complaints per week before training}
#' \item{\code{complaints_after}}{double: average complaints per week after training}
#'}
#' @source {}
"complaints"
#' @title PearsonTitanic
#' @description Titanic survival data set from Pearson
#' @format A data frame with 2201 rows and 4 variables:
#' \describe{
#' \item{\code{class}}{character: fare class}
#' \item{\code{adult_child}}{character: age category recorded as Adult or Child}
#' \item{\code{male_female}}{character: gender recorded as Male or Female}
#' \item{\code{survival}}{character: survival recorded as Alive or Dead}
#'}
#' @source {Pearson Publishing}
"PearsonTitanic"
#' @title PearsonDealerships
#' @description information about car dealerships from Pearson
#' @format A data frame with 1000 rows and 7 variables:
#' \describe{
#' \item{\code{dealership_number}}{double: dealership ID}
#' \item{\code{state}}{character: state dealership is located in}
#' \item{\code{median_salary}}{double: median salary of employees in $}
#' \item{\code{annual_sales}}{double: annual sales of dealership, in $}
#' \item{\code{number_of_vehicles_sold}}{double: number of vehicles sold in a period}
#' \item{\code{square_feet}}{double: square feet of dealership}
#' \item{\code{quality_award_winner}}{character: did they win a quality award, y or n}
#'}
#' @source {Pearson Publishing}
"PearsonDealerships"
#' @title Pearson Fuel Efficiency
#' @description Fuel efficiency data for a number of cars
#' @format A data frame with 84 rows and 9 variables:
#' \describe{
#' \item{\code{model}}{character: car model}
#' \item{\code{eng_size}}{double: engine size, in liters}
#' \item{\code{cylinders}}{double: number of cylinders in engine}
#' \item{\code{msrp}}{double: suggested retail price of car, USD}
#' \item{\code{city_mpg}}{double: average miles per gallon, city driving}
#' \item{\code{highway_mpg}}{double: average miles per gallon, highway driving}
#' \item{\code{weight}}{double: weight, pounds}
#' \item{\code{type}}{character: type of car}
#' \item{\code{country}}{character: country where manufacturer is based}
#'}
#' @source {Pearson Publishing}
"PearsonFuelEff"
#' @title PearsonIQ
#' @description IQ and demographic data for 500 respondents
#' @format A data frame with 500 rows and 7 variables:
#' \describe{
#' \item{\code{volunteer_number}}{double: ID of respondent}
#' \item{\code{gender}}{character: gender}
#' \item{\code{iq}}{double: IQ score}
#' \item{\code{annual_income}}{double: annual income, in dollars}
#' \item{\code{pre_test_score}}{double: pre-test score}
#' \item{\code{lifetime_savings}}{double: lifetime savings, in $}
#' \item{\code{gifted}}{character: identified as gifted recorded as y or n}
#'}
#' @source \url{Pearson Publishing}
"PearsonIQ"
#' @title PearsonFuelEconomy
#' @description DATASET_DESCRIPTION
#' @format A data frame with 750 rows and 7 variables:
#' \describe{
#' \item{\code{vehicle_number}}{double: observation ID}
#' \item{\code{type}}{character: car class}
#' \item{\code{vehicle_weight}}{double: weight of vehicle in pounds}
#' \item{\code{average_mpg}}{double: average miles per gallon}
#' \item{\code{fuel_tank_size_gallons}}{double: size of fuel tank in gallons}
#' \item{\code{engine_size_l}}{double: size of engine in liters}
#' \item{\code{meet_or_not_meet_current_standards}}{character: does car meet current fuel standards, recorded as y or n.}
#'}
#' @source {Pearson Publishing}
"PearsonFuelEconomy"
#' @title PearsonClassSize
#' @description demogaraphic information about class sizes and performance
#' @format A data frame with 300 rows and 6 variables:
#' \describe{
#' \item{\code{campus}}{character: which campus}
#' \item{\code{class_size}}{double: number of students in class}
#' \item{\code{average_final_grade}}{double: average final numeric grade}
#' \item{\code{number_of_f_s}}{double: how many Fs given in the class}
#' \item{\code{average_g_p_a}}{double: average GPA of students in class}
#' \item{\code{successful_or_unsuccessful}}{character:}
#'}
#' @source {Pearson Publishing}
"PearsonClassSize"
#' @title PearsonCrackerBarrel
#' @description information about various Cracker Barrel restaurants
#' @format A data frame with 150 rows and 7 variables:
#' \describe{
#' \item{\code{restaurant_number}}{double: restaurant ID}
#' \item{\code{geographic_region}}{character: geographic region}
#' \item{\code{annual_revenue}}{double: annual revenue in $}
#' \item{\code{average_cost_of_gasoline}}{double: average cost of gasoline near restaurant}
#' \item{\code{miles_from_interstate}}{double: distance from nearest interstate, in miles}
#' \item{\code{square_feet}}{double: size of restaurant in square feet}
#' \item{\code{annual_increase_in_revenue}}{character: did revenue increase this year over the previous year, coded as y or n.}
#'}
#' @source {Pearson Publishing}
"PearsonCrackerBarrel"
#' @title HouseSalesData
#' @description Collection of variables that describe house sales in one community.
#' @format A data frame with 1460 rows and 80 variables:
#' \describe{
#' \item{\code{Id}}{double Identifier}
#' \item{\code{MsZoning}}{character zoning type}
#' \item{\code{LotFrontage}}{double linear feet of property frontage to street}
#' \item{\code{LotArea}}{double area in square feet of plot}
#' \item{\code{Street}}{character type of street}
#' \item{\code{Alley}}{character indicator for alley}
#' \item{\code{LotShape}}{character descriptor for shape of land plot}
#' \item{\code{LandContour}}{character contour of land}
#' \item{\code{Utilities}}{character utility type to property}
#' \item{\code{LotConfig}}{character configuration of plot}
#' \item{\code{LandSlope}}{character how sloped is the plot}
#' \item{\code{Neighborhood}}{character neighborhood}
#' \item{\code{Condition1}}{character }
#' \item{\code{Condition2}}{character }
#' \item{\code{BldgType}}{character }
#' \item{\code{HouseStyle}}{character }
#' \item{\code{OverallQual}}{double quality rating, 1 – 10 scale}
#' \item{\code{OverallCond}}{double condition rating, 1 – 10 scale}
#' \item{\code{YearBuilt}}{double }
#' \item{\code{YearRemodAdd}}{double }
#' \item{\code{RoofStyle}}{character type of roof on property}
#' \item{\code{RoofMatl}}{character material used in roof}
#' \item{\code{Exterior1St}}{character }
#' \item{\code{Exterior2Nd}}{character }
#' \item{\code{MasVnrType}}{character }
#' \item{\code{MasVnrArea}}{double COLUMN }
#' \item{\code{ExterQual}}{character }
#' \item{\code{ExterCond}}{character }
#' \item{\code{Foundation}}{character }
#' \item{\code{BsmtQual}}{character }
#' \item{\code{BsmtCond}}{character }
#' \item{\code{BsmtExposure}}{character }
#' \item{\code{BsmtFinType1}}{character }
#' \item{\code{BsmtFinSf1}}{double }
#' \item{\code{BsmtFinType2}}{character }
#' \item{\code{BsmtFinSf2}}{double }
#' \item{\code{BsmtUnfSf}}{double }
#' \item{\code{TotalBsmtSf}}{double }
#' \item{\code{Heating}}{character }
#' \item{\code{HeatingQc}}{character }
#' \item{\code{CentralAir}}{character }
#' \item{\code{Electrical}}{character }
#' \item{\code{X1StFlrSf}}{double }
#' \item{\code{X2NdFlrSf}}{double }
#' \item{\code{LowQualFinSf}}{double }
#' \item{\code{GrLivArea}}{double }
#' \item{\code{BsmtFullBath}}{double }
#' \item{\code{BsmtHalfBath}}{double }
#' \item{\code{FullBath}}{double }
#' \item{\code{HalfBath}}{double }
#' \item{\code{BedroomAbvGr}}{double }
#' \item{\code{KitchenAbvGr}}{double }
#' \item{\code{KitchenQual}}{character }
#' \item{\code{TotRmsAbvGrd}}{double }
#' \item{\code{Functional}}{character }
#' \item{\code{Fireplaces}}{double }
#' \item{\code{FireplaceQu}}{character }
#' \item{\code{GarageType}}{character }
#' \item{\code{GarageYrBlt}}{double }
#' \item{\code{GarageFinish}}{character }
#' \item{\code{GarageCars}}{double }
#' \item{\code{GarageArea}}{double }
#' \item{\code{GarageQual}}{character }
#' \item{\code{GarageCond}}{character }
#' \item{\code{PavedDrive}}{character }
#' \item{\code{WoodDeckSf}}{double }
#' \item{\code{OpenPorchSf}}{double }
#' \item{\code{EnclosedPorch}}{double }
#' \item{\code{X3SsnPorch}}{double }
#' \item{\code{ScreenPorch}}{double }
#' \item{\code{PoolArea}}{double }
#' \item{\code{PoolQc}}{character }
#' \item{\code{Fence}}{character }
#' \item{\code{MiscFeature}}{character }
#' \item{\code{MiscVal}}{double }
#' \item{\code{MoSold}}{double }
#' \item{\code{YrSold}}{double }
#' \item{\code{SaleType}}{character }
#' \item{\code{SaleCondition}}{character }
#' \item{\code{SalePrice}}{double }
#'}
#'
"HouseSalesData"
#' @title CarInsData
#' @description dataset that includes various attributes about drivers, cars, and car crashes
#' @format A data frame with 500 rows and 31 variables:
#' \describe{
#' \item{\code{Obs}}{double }
#' \item{\code{ID}}{double }
#' \item{\code{Crash}}{double 0/1 indicator for crash where 1 indicates a crash}
#' \item{\code{CrashCost}}{double cost to insurance company of crash}
#' \item{\code{DriverAge}}{double age of driver}
#' \item{\code{LevelEducation}}{character level education of driver}
#' \item{\code{YearsJob}}{double years on current job}
#' \item{\code{Income}}{double income in US dollars}
#' \item{\code{SexofDriver}}{character sex, male or female}
#' \item{\code{TypeofJob}}{character job type}
#' \item{\code{TimeCommute}}{double in minutes, length of regular work commute}
#' \item{\code{PointsonRecord}}{double number points on driving record}
#' \item{\code{LicenseRevoked}}{character 0/1 indicator, 1 indicates has had license revoked}
#' \item{\code{ValueofHome}}{double in US dollars, value of home}
#' \item{\code{MaritalStatus}}{character indicator marital status}
#' \item{\code{SingleParent}}{character indicator single parent status}
#' \item{\code{NumberKidsHome}}{double }
#' \item{\code{NumberKidsDrive}}{double }
#' \item{\code{UrbanorRural}}{character where driver typically lives & drives}
#' \item{\code{AgeofCar}}{double }
#' \item{\code{TypeofCar}}{character }
#' \item{\code{TypeCarUse}}{character how vehicle is typically used}
#' \item{\code{BlueBookValue}}{double replacement value from Blue Book}
#' \item{\code{RedCar}}{character indicator for red cars}
#' \item{\code{TimeasCustomer}}{double number years driver has been a customer}
#' \item{\code{NumberClaims5yr}}{double number of claims in last 5 years}
#' \item{\code{Total5YrPayout}}{double US dollar value payouts of claims in last 5 years}
#' \item{\code{NotWorking}}{character indicator working or not}
#' \item{\code{NewCar}}{character indicator, new car or not}
#' \item{\code{GoodDriver}}{character indicator, good driver or not, based on points}
#' \item{\code{OwnHome}}{character indicator, own or rent, based on home value}
#'}
#'
"CarInsData"
#' @title HeartData
#' @description variables about heart health events
#' @format A data frame with 303 rows and 14 variables:
#' \describe{
#' \item{\code{age}}{double }
#' \item{\code{sex}}{double }
#' \item{\code{cp}}{double }
#' \item{\code{trestbps}}{double }
#' \item{\code{chol}}{double }
#' \item{\code{fbs}}{double }
#' \item{\code{restecg}}{double }
#' \item{\code{thalach}}{double }
#' \item{\code{exang}}{double }
#' \item{\code{oldpeak}}{double }
#' \item{\code{slope}}{double }
#' \item{\code{ca}}{double }
#' \item{\code{thal}}{double }
#' \item{\code{target}}{double }
#'}
#'
"HeartData"
#' @title SpeedData
#' @description email download speeds
#' @format A data frame with 30 rows and 2 variables:
#' \describe{
#' \item{\code{TYPE}}{character email interface application}
#' \item{\code{SPEED}}{double email opening speed in seconds}
#'}
#'
"SpeedData"
#' @title AdsManager
#' @description Fields from Google AdsManager
#' @format A data frame with 2667 rows and 6 variables:
#' \describe{
#' \item{\code{platform}}{character did viewer see ad on Facebook or Instagram}
#' \item{\code{age_group}}{character age group of viewer}
#' \item{\code{device_type}}{character device type viewer used to see ad}
#' \item{\code{clicked}}{character yes if viewer clicked on the ad to navigate to website}
#' \item{\code{pageviews}}{double number of pages viewed after clicking}
#' \item{\code{amt_spent}}{double amount spent on website after clicking}
#'}
#' @source "made-up by Stephanie Jernigan"
"AdsManager"
#' @title EmployeeAtt
#' @description simulated data on employee attrition created by IBM
#' @format A data frame with 1470 rows and 35 variables:
#' \describe{
#' \item{\code{Age}}{double, employee age}
#' \item{\code{Attrition}}{character, Yes if left company, No otherwise}
#' \item{\code{BusinessTravel}}{character, frequency of business travel}
#' \item{\code{DailyRate}}{double, unsure what this field is}
#' \item{\code{Department}}{character, employee's work department}
#' \item{\code{DistanceFromHome}}{double, commute distance in miles}
#' \item{\code{Education}}{double, level of education: lower numbers are less}
#' \item{\code{EducationField}}{character, field of study during education}
#' \item{\code{EmployeeCount}}{double, this field is always 1}
#' \item{\code{EmployeeNumber}}{double, employee ID}
#' \item{\code{EnvironmentSatisfaction}}{double, lower numbers indicate lower satisfaction}
#' \item{\code{Gender}}{character, gender}
#' \item{\code{HourlyRate}}{double, unsure what this field is}
#' \item{\code{JobInvolvement}}{double, lower numbers indicate less involvement}
#' \item{\code{JobLevel}}{double, lower numbers indicate lower level}
#' \item{\code{JobRole}}{character, job role}
#' \item{\code{JobSatisfaction}}{double, lower numbers indicate lower satisfaction}
#' \item{\code{MaritalStatus}}{character, marital status}
#' \item{\code{MonthlyIncome}}{double, monthy income}
#' \item{\code{MonthlyRate}}{double, unsure what this field is}
#' \item{\code{NumCompaniesWorked}}{double, number of companies have worked for previously}
#' \item{\code{Over18}}{character, Y if over 18}
#' \item{\code{OverTime}}{character, not sure what ths field is}
#' \item{\code{PercentSalaryHike}}{double}
#' \item{\code{PerformanceRating}}{double, higher numbers indicate higher rating}
#' \item{\code{RelationshipSatisfaction}}{double, higher numbers indicate higher satisfaction}
#' \item{\code{StandardHours}}{double COLUMN_DESCRIPTION}
#' \item{\code{StockOptionLevel}}{double COLUMN_DESCRIPTION}
#' \item{\code{TotalWorkingYears}}{double COLUMN_DESCRIPTION}
#' \item{\code{TrainingTimesLastYear}}{double COLUMN_DESCRIPTION}
#' \item{\code{WorkLifeBalance}}{double COLUMN_DESCRIPTION}
#' \item{\code{YearsAtCompany}}{double COLUMN_DESCRIPTION}
#' \item{\code{YearsInCurrentRole}}{double COLUMN_DESCRIPTION}
#' \item{\code{YearsSinceLastPromotion}}{double COLUMN_DESCRIPTION}
#' \item{\code{YearsWithCurrManager}}{double COLUMN_DESCRIPTION}
#'}
#' @source \url{https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset/metadata}
"EmployeeAtt"
#' @title CustValue
#' @description Simulated data about auto insurance company and value of customer, by IBM
#' @format A data frame with 9134 rows and 24 variables:
#' \describe{
#' \item{\code{customer}}{character Customer ID}
#' \item{\code{state}}{character U.S. state of residence}
#' \item{\code{customer_lifetime_value}}{double value of customer to company in USD}
#' \item{\code{response}}{character unsure what this is: if responded to marketing?}
#' \item{\code{coverage}}{character type of auto insurance policy}
#' \item{\code{education}}{character level of education}
#' \item{\code{effective_to_date}}{character }
#' \item{\code{employment_status}}{character employment status}
#' \item{\code{gender}}{character gender}
#' \item{\code{income}}{double income, USD}
#' \item{\code{location_code}}{character type of location of policyholder residence}
#' \item{\code{marital_status}}{character marital status}
#' \item{\code{monthly_premium_auto}}{double monthly cost of auto premium}
#' \item{\code{months_since_last_claim}}{double number of months since last claim}
#' \item{\code{months_since_policy_inception}}{double months since became a policyholder}
#' \item{\code{number_of_open_complaints}}{double number of open complaints (unsure made by whom about who)}
#' \item{\code{number_of_policies}}{double number of policies (other insurance policies by same company)}
#' \item{\code{policy_type}}{character type of auto policy}
#' \item{\code{policy}}{character further breakdown of type of auto policy}
#' \item{\code{renew_offer_type}}{character what type of renewal offer were they given?}
#' \item{\code{sales_channel}}{character sales channel}
#' \item{\code{total_claim_amount}}{double total amount of claims made by policyholder?? seems low}
#' \item{\code{vehicle_class}}{character type of vehicle insured}
#' \item{\code{vehicle_size}}{character size of insured vehicle}
#'}
#' @source \url{https://www.kaggle.com/pankajjsh06/ibm-watson-marketing-customer-value-data}
"CustValue"
#' @title HealthIns
#' @description Simulated data about health insurance expenses from Eric Suess
#' @format A data frame with 1338 rows and 7 variables:
#' \describe{
#' \item{\code{age}}{double age in years}
#' \item{\code{sex}}{character gender}
#' \item{\code{bmi}}{double body mass index: higher is generally worse}
#' \item{\code{children}}{double number of children}
#' \item{\code{smoker}}{character yes if smoker; no otherwise}
#' \item{\code{region}}{character one of three regions in the U.S.}
#' \item{\code{expenses}}{double medical expenses in one year in USD}
#'}
#' @source \url{https://www.kaggle.com/noordeen/insurance-premium-prediction?select=insurance.csv}
"HealthIns"
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