Tools for an Introductory Class in Regression and Modeling

Documentation for package ‘regclass’ version 1.6

Help Pages

-- A --

ACCOUNT	Predicting whether a customer will open a new kind of account
all.correlations	Pairwise correlations between quantitative variables
all_correlations	Pairwise correlations between quantitative variables
APPLIANCE	Appliance shipments
associate	Association Analysis
ATTRACTF	Attractiveness Score (female)
ATTRACTM	Attractiveness Score (male)
AUTO	AUTO dataset

-- B --

BODYFAT	BODYFAT data
BODYFAT2	Secondary BODYFAT dataset
build.model	Variable selection for descriptive or predictive linear and logistic regression models
build.tree	Exploratory building of partition models
build_model	Variable selection for descriptive or predictive linear and logistic regression models
build_tree	Exploratory building of partition models
BULLDOZER	BULLDOZER data
BULLDOZER2	Modified BULLDOZER data

-- C --

CALLS	CALLS dataset
CENSUS	CENSUS data
CENSUSMLR	Subset of CENSUS data
CHARITY	CHARITY dataset
check.regression	Linear and Logistic Regression diagnostics
check_regression	Linear and Logistic Regression diagnostics
choose.order	Choosing order of a polynomial model
choose_order	Choosing order of a polynomial model
CHURN	CHURN dataset
combine_rare_levels	Combines rare levels of a categorical variable
confusion.matrix	Confusion matrix for logistic regression models
confusion_matrix	Confusion matrix for logistic regression models
cor.demo	Correlation demo
cor.matrix	Correlation Matrix
cor_demo	Correlation demo
cor_matrix	Correlation Matrix
CUSTCHURN	CUSTCHURN dataset
CUSTLOYALTY	CUSTLOYALTY dataset
CUSTREACQUIRE	CUSTREACQUIRE dataset
CUSTVALUE	CUSTVALUE dataset

-- D --

DIET	DIET data
DONOR	DONOR dataset

-- E --

EDUCATION	EDUCATION data
EX2.CENSUS	CENSUS data for Exercise 5 in Chapter 2
EX2.TIPS	TIPS data for Exercise 6 in Chapter 2
EX3.ABALONE	ABALONE dataset for Exercise D in Chapter 3
EX3.BODYFAT	Bodyfat data for Exercise F in Chapter 3
EX3.HOUSING	Housing data for Exercise E in Chapter 3
EX3.NFL	NFL data for Exercise A in Chapter 3
EX4.BIKE	Bike data for Exercise 1 in Chapter 4
EX4.STOCKPREDICT	Stock data for Exercise 2 in Chapter 4 (prediction set)
EX4.STOCKS	Stock data for Exercise 2 in Chapter 4
EX5.BIKE	BIKE dataset for Exercise 4 Chapter 5
EX5.DONOR	DONOR dataset for Exercise 4 in Chapter 5
EX6.CLICK	CLICK data for Exercise 2 in Chapter 6
EX6.DONOR	DONOR dataset for Exercise 1 in Chapter 6
EX6.WINE	WINE data for Exercise 3 Chapter 6
EX7.BIKE	BIKE dataset for Exercise 1 Chapters 7 and 8
EX7.CATALOG	CATALOG data for Exercise 2 in Chapters 7 and 8
EX9.BIRTHWEIGHT	Birthweight dataset for Exercise 1 in Chapter 9
EX9.NFL	NFL data for Exercise 2 Chapter 9
EX9.STORE	Data for Exercise 3 Chapter 9
extrapolation.check	A crude check for extrapolation
extrapolation_check	A crude check for extrapolation

-- F --

find.transformations	Transformations for simple linear regression
find_transformations	Transformations for simple linear regression
FRIEND	Friendship Potential vs. Attractiveness Ratings
FUMBLES	Wins vs. Fumbles of an NFL team

-- G --

generalization.error	Calculating the generalization error of a model on a set of data
generalization_error	Calculating the generalization error of a model on a set of data
getcp	Complexity Parameter table for partition models

-- I --

influence.plot	Influence plot for regression diganostics
influence_plot	Influence plot for regression diganostics

-- J --

JUNK	Junk-mail dataset

-- L --

LARGEFLYER	Interest in frequent flier program (large version)
LAUNCH	New product launch data

-- M --

mode_factor	Find the mode of a categorical variable
mosaic	Mosaic plot
MOVIE	Movie grosses

-- N --

NFL	NFL database

-- O --

OFFENSE	Some offensive statistics from 'NFL' dataset
outlier_demo	Interactive demonstration of the effect of an outlier on a regression
overfit.demo	Demonstration of overfitting
overfit_demo	Demonstration of overfitting

-- P --

PIMA	Pima Diabetes dataset
POISON	Cockroach poisoning data
possible.regressions	Illustrating how a simple linear/logistic regression could have turned out via permutations
possible_regressions	Illustrating how a simple linear/logistic regression could have turned out via permutations
PRODUCT	Sales of a product one quarter after release
PURCHASE	PURCHASE data

-- Q --

QQ plot

-- S --

SALARY	Harris Bank Salary data
see.interactions	Examining pairwise interactions between quantitative variables for a fitted regression model
see.models	Examining model AICs from the "all possible" regressions procedure using regsubsets
see_interactions	Examining pairwise interactions between quantitative variables for a fitted regression model
see_models	Examining model AICs from the "all possible" regressions procedure using regsubsets
segmented.barchart	Segmented barchart
segmented_barchart	Segmented barchart
SMALLFLYER	Interest in a frequent flier program (small version)
SOLD26	Predicting future sales
SPEED	Speed vs. Fuel Efficiency
STUDENT	STUDENT data
suggest_levels	Combining levels of a categorical variable
summarize.tree	Useful summaries of partition models from rpart
summarize_tree	Useful summaries of partition models from rpart
SURVEY09	Student survey 2009
SURVEY10	Student survey 2010
SURVEY11	Student survey 2011

-- T --

TIPS	TIPS dataset

-- V --

VIF	Variance Inflation Factor
visualize.model	Visualizations of one or two variable linear or logistic regressions or of partitions models
visualize.relationship	Visualizing the relationship between y and x in a partition model
visualize_model	Visualizations of one or two variable linear or logistic regressions or of partitions models
visualize_relationship	Visualizing the relationship between y and x in a partition model

-- W --

WINE data