Man pages for regclass
Tools for an Introductory Class in Regression and Modeling
| ACCOUNT | Predicting whether a customer will open a new kind of account |
| all_correlations | Pairwise correlations between quantitative variables |
| APPLIANCE | Appliance shipments |
| associate | Association Analysis |
| ATTRACTF | Attractiveness Score (female) |
| ATTRACTM | Attractiveness Score (male) |
| AUTO | AUTO dataset |
| BODYFAT | BODYFAT data |
| BODYFAT2 | Secondary BODYFAT dataset |
| build_model | Variable selection for descriptive or predictive linear and... |
| build_tree | Exploratory building of partition models |
| BULLDOZER | BULLDOZER data |
| BULLDOZER2 | Modified BULLDOZER data |
| CALLS | CALLS dataset |
| CENSUS | CENSUS data |
| CENSUSMLR | Subset of CENSUS data |
| CHARITY | CHARITY dataset |
| check_regression | Linear and Logistic Regression diagnostics |
| 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 |
| cor_demo | Correlation demo |
| cor_matrix | Correlation Matrix |
| CUSTCHURN | CUSTCHURN dataset |
| CUSTLOYALTY | CUSTLOYALTY dataset |
| CUSTREACQUIRE | CUSTREACQUIRE dataset |
| CUSTVALUE | CUSTVALUE dataset |
| DIET | DIET data |
| DONOR | DONOR dataset |
| 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 |
| find_transformations | Transformations for simple linear regression |
| FRIEND | Friendship Potential vs. Attractiveness Ratings |
| FUMBLES | Wins vs. Fumbles of an NFL team |
| generalization_error | Calculating the generalization error of a model on a set of... |
| getcp | Complexity Parameter table for partition models |
| influence_plot | Influence plot for regression diganostics |
| JUNK | Junk-mail dataset |
| LARGEFLYER | Interest in frequent flier program (large version) |
| LAUNCH | New product launch data |
| mode_factor | Find the mode of a categorical variable |
| mosaic | Mosaic plot |
| MOVIE | Movie grosses |
| NFL | NFL database |
| OFFENSE | Some offensive statistics from 'NFL' dataset |
| outlier_demo | Interactive demonstration of the effect of an outlier on a... |
| overfit_demo | Demonstration of overfitting |
| PIMA | Pima Diabetes dataset |
| POISON | Cockroach poisoning data |
| possible_regressions | Illustrating how a simple linear/logistic regression could... |
| PRODUCT | Sales of a product one quarter after release |
| PURCHASE | PURCHASE data |
| QQ plot | |
| SALARY | Harris Bank Salary data |
| see_interactions | Examining pairwise interactions between quantitative... |
| see_models | Examining model AICs from the "all possible" regressions... |
| 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 |
| SURVEY09 | Student survey 2009 |
| SURVEY10 | Student survey 2010 |
| SURVEY11 | Student survey 2011 |
| TIPS | TIPS dataset |
| VIF | Variance Inflation Factor |
| visualize_model | Visualizations of one or two variable linear or logistic... |
| visualize_relationship | Visualizing the relationship between y and x in a partition... |
| WINE | WINE data |
