R : Copyright 2005, The R Foundation for Statistical Computing Version 2.1.1 (2005-06-20), ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for a HTML browser interface to help. Type 'q()' to quit R. > ### *
> ### > attach(NULL, name = "CheckExEnv") > assign(".CheckExEnv", as.environment(2), pos = length(search())) # base > ## add some hooks to label plot pages for base and grid graphics > setHook("plot.new", ".newplot.hook") > setHook("persp", ".newplot.hook") > setHook("grid.newpage", ".gridplot.hook") > > assign("cleanEx", + function(env = .GlobalEnv) { + rm(list = ls(envir = env, all.names = TRUE), envir = env) + RNGkind("default", "default") + set.seed(1) + options(warn = 1) + delayedAssign("T", stop("T used instead of TRUE"), + assign.env = .CheckExEnv) + delayedAssign("F", stop("F used instead of FALSE"), + assign.env = .CheckExEnv) + sch <- search() + newitems <- sch[! sch %in% .oldSearch] + for(item in rev(newitems)) + eval(substitute(detach(item), list(item=item))) + missitems <- .oldSearch[! .oldSearch %in% sch] + if(length(missitems)) + warning("items ", paste(missitems, collapse=", "), + " have been removed from the search path") + }, + env = .CheckExEnv) > assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now > assign("ptime", proc.time(), env = .CheckExEnv) > grDevices::postscript("SenSrivastava-Examples.ps") > assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv) > options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly")) > options(warn = 1) > library('SenSrivastava') > > assign(".oldSearch", search(), env = .CheckExEnv) > assign(".oldNS", loadedNamespaces(), env = .CheckExEnv) > cleanEx(); ..nameEx <- "E1.1" > > ### * E1.1 > > flush(stderr()); flush(stdout()) > > ### Name: E1.1 > ### Title: Data on density of vehicles and average speed > ### Aliases: E1.1 > ### Keywords: datasets > > ### ** Examples > > data(E1.1) > attach(E1.1) > plot(DENSITY, sqrt(SPEED)) > E1.1.m1 <- lm(sqrt(SPEED) ~ DENSITY + I(DENSITY^2), data=E1.1) > summary(E1.1.m1) Call: lm(formula = sqrt(SPEED) ~ DENSITY + I(DENSITY^2), data = E1.1) Residuals: Min 1Q Median 3Q Max -0.249086 -0.057458 -0.001957 0.048387 0.198584 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 7.003e+00 8.791e-02 79.656 < 2e-16 *** DENSITY -5.069e-02 2.721e-03 -18.631 1.54e-14 *** I(DENSITY^2) 1.486e-04 1.732e-05 8.578 2.64e-08 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.102 on 21 degrees of freedom Multiple R-Squared: 0.9931, Adjusted R-squared: 0.9924 F-statistic: 1500 on 2 and 21 DF, p-value: < 2.2e-16 > > > > cleanEx(); ..nameEx <- "E1.11" > > ### * E1.11 > > flush(stderr()); flush(stdout()) > > ### Name: E1.11 > ### Title: Data on violent and property crimes in 22 metropolitan areas of > ### the U.S. > ### Aliases: E1.11 > ### Keywords: datasets > > ### ** Examples > > data(E1.11) > attach(E1.11) > plot(Population, Violent.Crimes) > detach() > > > > cleanEx(); ..nameEx <- "E1.15" > > ### * E1.15 > > flush(stderr()); flush(stdout()) > > ### Name: E1.15 > ### Title: Stevens Experiment to compare notes against a standard (80 Db) > ### Aliases: E1.15 > ### Keywords: datasets > > ### ** Examples > > data(E1.15) > attach(E1.15) > plot(x, logy) > abline(lm( logy ~ x, data=E1.15)) > detach() > > > > cleanEx(); ..nameEx <- "E1.16" > > ### * E1.16 > > flush(stderr()); flush(stdout()) > > ### Name: E1.16 > ### Title: Earnings and Prices of Selected Paper Company Stocks > ### Aliases: E1.16 > ### Keywords: datasets > > ### ** Examples > > with(E1.16, plot(Price.Share, Earn.Share)) > > > > cleanEx(); ..nameEx <- "E1.17" > > ### * E1.17 > > flush(stderr()); flush(stdout()) > > ### Name: E1.17 > ### Title: Data on Population Density and Vehicle Thefts > ### Aliases: E1.17 > ### Keywords: datasets > > ### ** Examples > > data(E1.17) > attach(E1.17) > plot(pd, vtt) > cat("Use the mouse to identify the outlier in the plot (click on the outlier)\n") Use the mouse to identify the outlier in the plot (click on the outlier) > ## Not run: identify(pd, vtt) > > > > cleanEx(); ..nameEx <- "E1.18" > > ### * E1.18 > > flush(stderr()); flush(stdout()) > > ### Name: E1.18 > ### Title: Data on Simsbury Marriages > ### Aliases: E1.18 > ### Keywords: datasets > > ### ** Examples > > data(E1.18) > summary(E1.18) d pop ma Min. : 6.0 Min. : 2583 Min. : 4.00 1st Qu.: 9.5 1st Qu.: 4535 1st Qu.: 7.00 Median :13.0 Median : 27309 Median :12.00 Mean :13.0 Mean : 49784 Mean :16.88 3rd Qu.:16.5 3rd Qu.: 70352 3rd Qu.:20.00 Max. :20.0 Max. :175458 Max. :49.00 > > > > cleanEx(); ..nameEx <- "E1.19" > > ### * E1.19 > > flush(stderr()); flush(stdout()) > > ### Name: E1.19 > ### Title: Data on Book Prices, Pages and Type of Binding > ### Aliases: E1.19 > ### Keywords: datasets > > ### ** Examples > > data(E1.19) > summary(E1.19) Price P B Min. :10.25 Min. :112.0 c:10 1st Qu.:16.44 1st Qu.:198.5 p:10 Median :18.75 Median :251.5 Mean :20.95 Mean :256.2 3rd Qu.:27.88 3rd Qu.:299.5 Max. :30.50 Max. :425.0 > > > > cleanEx(); ..nameEx <- "E1.20" > > ### * E1.20 > > flush(stderr()); flush(stdout()) > > ### Name: E1.20 > ### Title: Data on Physical Quality of Life Index (PQLI) Scores and Infant > ### Mortality Rates (IMR) for Selected Indian States > ### Aliases: E1.20 > ### Keywords: datasets > > ### ** Examples > > data(E1.20) > ## Some data reorganization before analysis: > ## Maybe reshape could have been used here? > e1.20 <- data.frame(rbind(as.matrix(E1.20[,c(2,4)]), + as.matrix(E1.20[,c(2,5)]), + as.matrix(E1.20[,c(2,6)]), + as.matrix(E1.20[,c(2,7)])),row.names=1:52) > attr(e1.20,"names")[[2]] <- "IMR" > e1.20$Female <- c(rep(0,13), rep(1,13),rep(0,13),rep(1,13)) > e1.20$Urban <- c(rep(0,26),rep(1,26)) > ## Now the analysis can start. > summary(e1.20) PQLI IMR Female Urban Min. :17.00 Min. : 22.0 Min. :0.0 Min. :0.0 1st Qu.:29.00 1st Qu.: 61.5 1st Qu.:0.0 1st Qu.:0.0 Median :36.00 Median : 87.5 Median :0.5 Median :0.5 Mean :43.54 Mean : 92.8 Mean :0.5 Mean :0.5 3rd Qu.:55.00 3rd Qu.:121.2 3rd Qu.:1.0 3rd Qu.:1.0 Max. :92.00 Max. :187.0 Max. :1.0 Max. :1.0 > > > > cleanEx(); ..nameEx <- "E1.21" > > ### * E1.21 > > flush(stderr()); flush(stdout()) > > ### Name: E1.21 > ### Title: Data on Loads and Deformation of a Bar > ### Aliases: E1.21 > ### Keywords: datasets > > ### ** Examples > > data(E1.21) > attach(E1.21) > plot(L, D) > detach() > > > > cleanEx(); ..nameEx <- "E1.7" > > ### * E1.7 > > flush(stderr()); flush(stdout()) > > ### Name: E1.7 > ### Title: Data on Population and Number of Telephones > ### Aliases: E1.7 > ### Keywords: datasets > > ### ** Examples > > data(E1.7) > attach(E1.7) > plot(RES, MAINS) > plot(sqrt(RES), sqrt(MAINS)) > > > > cleanEx(); ..nameEx <- "E10.1" > > ### * E10.1 > > flush(stderr()); flush(stdout()) > > ### Name: E10.1 > ### Title: Multicollinear Data > ### Aliases: E10.1 > ### Keywords: datasets > > ### ** Examples > > data(E10.1) > attach(E10.1) > plot(x.1, x.2) > names(E10.1) [1] "x.1" "x.2" "y.1" "y.2" "y.3" > hascar <- require(car) Loading required package: car > if (hascar) { + mod <- lm(y.1 ~ x.1+x.2, data=E10.1) + vif(mod) + } x.1 x.2 5868.654 5868.654 > > > > cleanEx(); ..nameEx <- "E10.11" > > ### * E10.11 > > flush(stderr()); flush(stdout()) > > ### Name: E10.11 > ### Title: Longley's Data > ### Aliases: E10.11 > ### Keywords: datasets > > ### ** Examples > > data(E10.11) > summary(E10.11) Def GNP Unemp AF Min. : 83.00 Min. :234289 Min. :1870 Min. :1456 1st Qu.: 94.53 1st Qu.:317881 1st Qu.:2348 1st Qu.:2298 Median :100.60 Median :381427 Median :3144 Median :2718 Mean :101.68 Mean :387698 Mean :3193 Mean :2607 3rd Qu.:111.25 3rd Qu.:454086 3rd Qu.:3842 3rd Qu.:3061 Max. :116.90 Max. :554894 Max. :4806 Max. :3594 Pop. Year Total Min. :107608 Min. :1947 Min. :60171 1st Qu.:111788 1st Qu.:1951 1st Qu.:62712 Median :116804 Median :1954 Median :65504 Mean :117424 Mean :1954 Mean :65317 3rd Qu.:122304 3rd Qu.:1958 3rd Qu.:68290 Max. :130081 Max. :1962 Max. :70551 > plot(E10.11) > > > > cleanEx(); ..nameEx <- "E10.3" > > ### * E10.3 > > flush(stderr()); flush(stdout()) > > ### Name: E10.3 > ### Title: Supervisor Rating Data > ### Aliases: E10.3 > ### Keywords: datasets > > ### ** Examples > > data(E10.3) > summary(E10.3) x.1 x.2 x.3 x.4 Min. : 3.800 Min. :4.600 Min. : 5.300 Min. : 5.70 1st Qu.: 6.050 1st Qu.:5.825 1st Qu.: 8.475 1st Qu.: 9.05 Median : 7.500 Median :6.950 Median :10.500 Median :11.25 Mean : 7.377 Mean :7.157 Mean :10.293 Mean :10.94 3rd Qu.: 8.700 3rd Qu.:8.400 3rd Qu.:12.075 3rd Qu.:13.00 Max. :10.000 Max. :9.900 Max. :14.000 Max. :14.00 x.5 y Min. :10.20 Min. :12.60 1st Qu.:12.70 1st Qu.:15.93 Median :14.90 Median :16.35 Mean :15.13 Mean :16.27 3rd Qu.:17.73 3rd Qu.:16.95 Max. :20.00 Max. :17.90 > plot(E10.3) > > > > cleanEx(); ..nameEx <- "E11.1" > > ### * E11.1 > > flush(stderr()); flush(stdout()) > > ### Name: E11.1 > ### Title: Artificially Created Data for an Example on Variable Search > ### Aliases: E11.1 > ### Keywords: datasets > > ### ** Examples > > data(E11.1) > exleaps <- require("leaps", quietly=TRUE) > if (exleaps) { + E11.1.m1 <- regsubsets(y ~x.1+x.2+x.3+x.4, data=E11.1) + summary(E11.1.m1) + plot(E11.1.m1) + } > > > > cleanEx(); ..nameEx <- "E2.1" > > ### * E2.1 > > flush(stderr()); flush(stdout()) > > ### Name: E2.1 > ### Title: Data on Grade Point Average and SAT Scores > ### Aliases: E2.1 > ### Keywords: datasets > > ### ** Examples > > data(E2.1) > summary(E2.1) GPA SATV SATM Min. :3.080 Min. :57.00 Min. :67.00 1st Qu.:3.470 1st Qu.:66.00 1st Qu.:71.00 Median :3.570 Median :71.00 Median :74.00 Mean :3.563 Mean :70.33 Mean :73.78 3rd Qu.:3.680 3rd Qu.:76.00 3rd Qu.:76.00 Max. :3.950 Max. :76.00 Max. :79.00 > > > > cleanEx(); ..nameEx <- "E2.11" > > ### * E2.11 > > flush(stderr()); flush(stdout()) > > ### Name: E2.11 > ### Title: Demographic Data for the 50 States of the U.S. > ### Aliases: E2.11 > ### Keywords: datasets > > ### ** Examples > > data(E2.11) > summary(E2.11) State POP UR MV Length:50 Min. : 400 Min. :338.0 Min. :363.0 Class :character 1st Qu.: 1168 1st Qu.:552.2 1st Qu.:437.5 Mode :character Median : 3066 Median :670.5 Median :461.5 Mean : 4511 Mean :669.3 Mean :480.9 3rd Qu.: 5319 3rd Qu.:801.2 3rd Qu.:513.8 Max. :23669 Max. :913.0 Max. :731.0 BL SP AI IN Min. : 1.00 Min. : 3.00 Min. : 10.00 Min. : 1.00 1st Qu.: 30.25 1st Qu.: 25.25 1st Qu.: 64.25 1st Qu.: 11.00 Median : 194.00 Median : 58.50 Median : 113.00 Median : 31.00 Mean : 520.78 Mean : 291.76 Mean : 283.46 Mean : 42.42 3rd Qu.: 986.50 3rd Qu.: 123.00 3rd Qu.: 336.25 3rd Qu.: 49.50 Max. :2402.00 Max. :4544.00 Max. :2013.00 Max. :218.00 PR MH B HT Min. : 3.00 Min. : 0.00 Min. :122.0 Min. : 76.0 1st Qu.: 9.75 1st Qu.: 10.00 1st Qu.:152.2 1st Qu.:286.8 Median : 41.50 Median : 24.50 Median :164.5 Median :320.5 Mean : 65.26 Mean : 40.88 Mean :168.0 Mean :314.3 3rd Qu.: 86.50 3rd Qu.: 51.75 3rd Qu.:178.2 3rd Qu.:357.8 Max. :499.00 Max. :223.00 Max. :301.0 Max. :431.0 S DI MA D Min. : 72.0 Min. : 27.0 Min. : 75.00 Min. : 30.00 1st Qu.:110.0 1st Qu.:128.2 1st Qu.: 91.25 1st Qu.: 42.00 Median :124.5 Median :153.0 Median : 107.00 Median : 55.00 Mean :130.1 Mean :151.8 Mean : 135.50 Mean : 57.78 3rd Qu.:145.0 3rd Qu.:170.0 3rd Qu.: 122.75 3rd Qu.: 69.00 Max. :248.0 Max. :252.0 Max. :1474.00 Max. :168.00 DR DN HS CR Min. :102.0 Min. :32.00 Min. :523.0 Min. :2552 1st Qu.:133.2 1st Qu.:43.00 1st Qu.:642.8 1st Qu.:4697 Median :153.0 Median :49.50 Median :682.0 Median :5405 Mean :161.9 Mean :51.74 Mean :672.9 Mean :5490 3rd Qu.:183.8 3rd Qu.:58.75 3rd Qu.:724.8 3rd Qu.:6353 Max. :261.0 Max. :74.00 Max. :802.0 Max. :8854 M PI RP VT Min. : 7.00 Min. : 28.00 Min. :372.0 Min. :407.0 1st Qu.: 44.00 1st Qu.: 78.25 1st Qu.:481.2 1st Qu.:497.0 Median : 83.50 Median :106.00 Median :504.5 Median :552.5 Mean : 81.78 Mean :119.46 Mean :521.1 Mean :551.4 3rd Qu.:107.50 3rd Qu.:152.50 3rd Qu.:558.8 3rd Qu.:592.2 Max. :200.00 Max. :244.00 Max. :728.0 Max. :704.0 PH INC PL Min. :36.00 Min. :3677 Min. : 67.0 1st Qu.:53.00 1st Qu.:4448 1st Qu.: 83.5 Median :56.00 Median :5216 Median :100.0 Mean :55.52 Mean :5130 Mean :115.7 3rd Qu.:59.00 3rd Qu.:5634 3rd Qu.:142.5 Max. :66.00 Max. :7141 Max. :261.0 > > > > cleanEx(); ..nameEx <- "E2.2" > > ### * E2.2 > > flush(stderr()); flush(stdout()) > > ### Name: E2.2 > ### Title: Data on House Prices > ### Aliases: E2.2 > ### Keywords: datasets > > ### ** Examples > > data(E2.2) > summary(E2.2) Price BDR FLR FP RMS Min. :35.00 Min. :2.000 Min. : 596 Min. :0.0000 Min. : 4.0 1st Qu.:46.25 1st Qu.:2.000 1st Qu.: 806 1st Qu.:0.0000 1st Qu.: 5.0 Median :55.50 Median :3.000 Median : 987 Median :0.0000 Median : 6.0 Mean :56.15 Mean :3.231 Mean :1100 Mean :0.1538 Mean : 6.5 3rd Qu.:64.00 3rd Qu.:4.000 3rd Qu.:1204 3rd Qu.:0.0000 3rd Qu.: 7.0 Max. :85.00 Max. :8.000 Max. :2261 Max. :1.0000 Max. :12.0 ST LOT TAX BTH CON Min. :0.0000 Min. :24.00 Min. : 440 Min. :1.000 Min. :0.0 1st Qu.:0.0000 1st Qu.:25.50 1st Qu.: 658 1st Qu.:1.000 1st Qu.:0.0 Median :0.0000 Median :30.00 Median : 817 Median :1.500 Median :0.5 Mean :0.2692 Mean :32.96 Mean : 898 Mean :1.481 Mean :0.5 3rd Qu.:0.7500 3rd Qu.:36.50 3rd Qu.: 991 3rd Qu.:1.875 3rd Qu.:1.0 Max. :1.0000 Max. :50.00 Max. :2700 Max. :3.000 Max. :1.0 GAR CDN L1 L2 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 Median :1.0000 Median :0.0000 Median :0.0000 Median :0.0000 Mean :0.8462 Mean :0.2308 Mean :0.4615 Mean :0.3077 3rd Qu.:1.5000 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 Max. :2.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000 > > > > cleanEx(); ..nameEx <- "E2.4" > > ### * E2.4 > > flush(stderr()); flush(stdout()) > > ### Name: E2.4 > ### Title: International Car Ownership Data > ### Aliases: E2.4 > ### Keywords: datasets > > ### ** Examples > > data(E2.4) > summary(E2.4) Country AO POP DEN Length:24 Min. :0.0140 Min. : 0.200 Min. : 2.00 Class :character 1st Qu.:0.1950 1st Qu.: 5.025 1st Qu.: 21.75 Mode :character Median :0.2900 Median : 9.800 Median : 87.50 Mean :0.2727 Mean : 32.129 Mean :117.83 3rd Qu.:0.3400 3rd Qu.: 45.350 3rd Qu.:161.75 Max. :0.5300 Max. :218.200 Max. :412.00 GDP PR CON TR Min. : 1.200 Min. :17.00 Min. :0.600 Min. :0.100 1st Qu.: 4.450 1st Qu.:40.00 1st Qu.:1.000 1st Qu.:0.775 Median : 8.600 Median :49.00 Median :1.200 Median :1.600 Mean : 7.362 Mean :47.33 Mean :1.371 Mean :1.421 3rd Qu.: 9.800 3rd Qu.:56.25 3rd Qu.:1.350 3rd Qu.:1.925 Max. :13.300 Max. :68.00 Max. :3.300 Max. :3.500 > > > > cleanEx(); ..nameEx <- "E2.6" > > ### * E2.6 > > flush(stderr()); flush(stdout()) > > ### Name: E2.6 > ### Title: Voltage Data > ### Aliases: E2.6 > ### Keywords: datasets > > ### ** Examples > > data(E2.6) > E2.6.m1 <- lm(V.c/V.a ~ V.a + I(V.a^2), data=E2.6) > plot(E2.6.m1) > > > > cleanEx(); ..nameEx <- "E2.7" > > ### * E2.7 > > flush(stderr()); flush(stdout()) > > ### Name: E2.7 > ### Title: Korean Auto Ownership Data > ### Aliases: E2.7 > ### Keywords: datasets > > ### ** Examples > > data(E2.7) > summary(E2.7) Year AO GNP CP Min. :1974 Min. :0.00220 Min. : 183.0 Min. :2322 1st Qu.:1976 1st Qu.:0.00290 1st Qu.: 341.2 1st Qu.:2432 Median :1978 Median :0.00570 Median : 608.0 Median :2680 Mean :1978 Mean :0.00529 Mean : 630.0 Mean :2715 3rd Qu.:1981 3rd Qu.:0.00680 3rd Qu.: 904.2 3rd Qu.:2992 Max. :1983 Max. :0.00950 Max. :1171.0 Max. :3200 OP Min. :189.0 1st Qu.:200.8 Median :219.5 Mean :399.3 3rd Qu.:652.5 Max. :740.0 > > > > cleanEx(); ..nameEx <- "E2.8" > > ### * E2.8 > > flush(stderr()); flush(stdout()) > > ### Name: E2.8 > ### Title: Data on per Capita Output of Workers in Shanghai > ### Aliases: E2.8 > ### Keywords: datasets > > ### ** Examples > > data(E2.8) > summary(E2.8) Output SI SP I Min. :11360 Min. : 56.0 Min. : 840 Min. :10.54 1st Qu.:12930 1st Qu.: 408.0 1st Qu.:2480 1st Qu.:12.45 Median :16680 Median : 805.0 Median :2840 Median :14.74 Mean :18348 Mean : 856.9 Mean :2859 Mean :16.94 3rd Qu.:20030 3rd Qu.:1217.0 3rd Qu.:3200 3rd Qu.:19.52 Max. :30750 Max. :1754.0 Max. :4240 Max. :29.19 > > > > cleanEx(); ..nameEx <- "E2.9" > > ### * E2.9 > > flush(stderr()); flush(stdout()) > > ### Name: E2.9 > ### Title: Data on Capital, Labour and Value Added for Three Sectors > ### Aliases: E2.9 > ### Keywords: datasets > > ### ** Examples > > data(E2.9) > summary(E2.9) YEAR Cap.20 Cap.36 Cap.37 Min. :72.0 Min. :243462 Min. :246028 Min. : 988165 1st Qu.:75.5 1st Qu.:270288 1st Qu.:257897 1st Qu.:1100744 Median :79.0 Median :290910 Median :264913 Median :1209188 Mean :79.0 Mean :282201 Mean :271074 Mean :1200915 3rd Qu.:82.5 3rd Qu.:295196 3rd Qu.:283088 3rd Qu.:1303755 Max. :86.0 Max. :307346 Max. :314728 Max. :1355769 Lab.20 Lab.36 Lab.37 Val.20 Min. :571454 Min. :664249 Min. : 947502 Min. :5521 1st Qu.:606902 1st Qu.:697714 1st Qu.:1097716 1st Qu.:6519 Median :667951 Median :779393 Median :1195255 Median :6694 Mean :649609 Mean :782500 Mean :1207414 Mean :6835 3rd Qu.:682138 3rd Qu.:850064 3rd Qu.:1301514 3rd Qu.:7397 Max. :708014 Max. :960917 Max. :1451595 Max. :8506 Val.36 Val.37 Min. :5555 Min. : 8140 1st Qu.:6337 1st Qu.: 9674 Median :6651 Median :10839 Mean :6636 Mean :10903 3rd Qu.:7004 3rd Qu.:12471 Max. :7552 Max. :13402 > > > > cleanEx(); ..nameEx <- "E3.4" > > ### * E3.4 > > flush(stderr()); flush(stdout()) > > ### Name: E3.4 > ### Title: Men's Worlds Record Times for Running and Corresponding > ### Distances > ### Aliases: E3.4 > ### Keywords: datasets > > ### ** Examples > > data(E3.4) > summary(E3.4) Dist. Time Min. : 100 Min. : 9.9 1st Qu.: 800 1st Qu.: 103.7 Median : 2000 Median : 296.2 Mean : 7615 Mean :1321.1 3rd Qu.:10000 3rd Qu.:1650.8 Max. :30000 Max. :5490.4 > > > > cleanEx(); ..nameEx <- "E3.5" > > ### * E3.5 > > flush(stderr()); flush(stdout()) > > ### Name: E3.5 > ### Title: Women's World Record Times for Running and Corresponding > ### Distances > ### Aliases: E3.5 > ### Keywords: datasets > > ### ** Examples > > data(E3.5) > data(E3.4) > summary(E3.5) Dist. Time Min. : 60 Min. : 7.20 1st Qu.: 125 1st Qu.: 13.62 Median : 300 Median : 36.55 Mean : 510 Mean : 74.92 3rd Qu.: 700 3rd Qu.:100.50 Max. :1500 Max. :241.40 > summary(E3.4) Dist. Time Min. : 100 Min. : 9.9 1st Qu.: 800 1st Qu.: 103.7 Median : 2000 Median : 296.2 Mean : 7615 Mean :1321.1 3rd Qu.:10000 3rd Qu.:1650.8 Max. :30000 Max. :5490.4 > records <- rbind(E3.5,E3.4) > sex <- factor(c(rep("F", 6), rep("M", 13))) > records$sex <- sex > summary(records) Dist. Time sex Min. : 60 Min. : 7.20 F: 6 1st Qu.: 300 1st Qu.: 32.95 M:13 Median : 1000 Median : 136.00 Mean : 5372 Mean : 927.57 3rd Qu.: 4000 3rd Qu.: 625.30 Max. :30000 Max. :5490.40 > > > > cleanEx(); ..nameEx <- "E3.6" > > ### * E3.6 > > flush(stderr()); flush(stdout()) > > ### Name: E3.6 > ### Title: Data on Corporations and Corporation Chairmen > ### Aliases: E3.6 > ### Keywords: datasets > > ### ** Examples > > data(E3.6) > summary(E3.6) Y84 Y83 SHARES REV Min. : 430000 Min. : 267510 Min. : 2000 Min. : 220 1st Qu.: 473662 1st Qu.: 392276 1st Qu.: 27873 1st Qu.: 922 Median : 555391 Median : 493752 Median : 72506 Median : 1592 Mean : 650632 Mean : 571694 Mean : 445801 Mean : 4449 3rd Qu.: 746267 3rd Qu.: 656997 3rd Qu.: 171628 3rd Qu.: 4054 Max. :1481250 Max. :1455350 Max. :5713459 Max. :38828 INC AGE Min. : 4.50 Min. :44.00 1st Qu.: 36.62 1st Qu.:52.25 Median : 91.75 Median :57.00 Mean : 192.35 Mean :56.72 3rd Qu.: 157.57 3rd Qu.:62.00 Max. :2183.00 Max. :69.00 > > > > cleanEx(); ..nameEx <- "E3.7" > > ### * E3.7 > > flush(stderr()); flush(stdout()) > > ### Name: E3.7 > ### Title: Data on Oxygen Demand in Dairy Wastes > ### Aliases: E3.7 > ### Keywords: datasets > > ### ** Examples > > data(E3.7) > summary(E3.7) Day x.1 x.2 x.3 Min. : 0.0 Min. : 79.0 Min. :147.0 Min. :2777 1st Qu.: 35.0 1st Qu.: 497.5 1st Qu.:183.0 1st Qu.:3904 Median : 76.0 Median : 576.5 Median :201.0 Median :4918 Mean : 80.4 Mean : 633.5 Mean :219.7 Mean :5075 3rd Qu.:110.8 3rd Qu.: 860.0 3rd Qu.:249.2 3rd Qu.:6014 Max. :220.0 Max. :1150.0 Max. :334.0 Max. :8804 x.4 x.5 y Min. :57.70 Min. :2599 Min. :-0.5229 1st Qu.:72.50 1st Qu.:4125 1st Qu.:-0.2218 Median :78.55 Median :4752 Median :-0.0229 Mean :77.34 Mean :5171 Mean : 0.1192 3rd Qu.:81.42 3rd Qu.:6000 3rd Qu.: 0.3252 Max. :86.50 Max. :8905 Max. : 1.5563 > > > > cleanEx(); ..nameEx <- "E3.8" > > ### * E3.8 > > flush(stderr()); flush(stdout()) > > ### Name: E3.8 > ### Title: Map reading Test scores and Route Finding Scores > ### Aliases: E3.8 > ### Keywords: datasets > > ### ** Examples > > data(E3.8) > summary(E3.8) y sc Use Min. :60.0 Min. :2.0 Non.users:10 1st Qu.:65.0 1st Qu.:3.0 Users :10 Median :70.0 Median :4.0 Mean :73.9 Mean :4.8 3rd Qu.:78.5 3rd Qu.:7.0 Max. :99.0 Max. :9.0 > > > > cleanEx(); ..nameEx <- "E3.9" > > ### * E3.9 > > flush(stderr()); flush(stdout()) > > ### Name: E3.9 > ### Title: Blood Velocity Data > ### Aliases: E3.9 > ### Keywords: datasets > > ### ** Examples > > data(E3.9) > summary(E3.9) x.1 x.2 y Aminophylline Min. :229.0 Min. :28.00 Min. : 8.10 no :9 1st Qu.:277.0 1st Qu.:35.00 1st Qu.:15.07 with:9 Median :385.0 Median :37.00 Median :18.70 Mean :438.7 Mean :38.44 Mean :18.25 3rd Qu.:514.2 3rd Qu.:43.00 3rd Qu.:20.98 Max. :873.0 Max. :52.00 Max. :35.60 > > > > cleanEx(); ..nameEx <- "E4.1" > > ### * E4.1 > > flush(stderr()); flush(stdout()) > > ### Name: E4.1 > ### Title: Traffic Fatality Data for Illinois > ### Aliases: E4.1 > ### Keywords: datasets > > ### ** Examples > > data(E4.1) > summary(E4.1) Year Deaths DFR Min. :1962 Min. :4.20 Min. :-0.50000 1st Qu.:1964 1st Qu.:4.75 1st Qu.:-0.20000 Median :1966 Median :5.00 Median :-0.10000 Mean :1966 Mean :4.87 Mean :-0.07778 3rd Qu.:1969 3rd Qu.:5.10 3rd Qu.: 0.10000 Max. :1971 Max. :5.30 Max. : 0.20000 NA's : 1.00000 > > > > cleanEx(); ..nameEx <- "E4.10" > > ### * E4.10 > > flush(stderr()); flush(stdout()) > > ### Name: E4.10 > ### Title: Votes from Chicago's Twenty-second Ward by Precinct > ### Aliases: E4.10 > ### Keywords: datasets > > ### ** Examples > > data(E4.10) > summary(E4.10) Pr. LATV NONLV TURNOUT GARCIA Min. : 1.0 Min. : 1.0 Min. : 59.0 Min. :146.0 Min. : 52.0 1st Qu.: 7.5 1st Qu.:116.5 1st Qu.: 64.5 1st Qu.:196.5 1st Qu.:106.5 Median :14.0 Median :143.0 Median : 92.0 Median :237.0 Median :123.0 Mean :14.0 Mean :137.1 Mean :107.7 Mean :244.8 Mean :122.0 3rd Qu.:20.5 3rd Qu.:170.5 3rd Qu.:127.5 3rd Qu.:295.5 3rd Qu.:140.5 Max. :27.0 Max. :190.0 Max. :305.0 Max. :410.0 Max. :206.0 MARTINEZ YANEZ Min. : 15.00 Min. : 7.00 1st Qu.: 58.50 1st Qu.:15.00 Median : 70.00 Median :26.00 Mean : 73.63 Mean :27.11 3rd Qu.: 88.00 3rd Qu.:39.50 Max. :158.00 Max. :52.00 > > > > cleanEx(); ..nameEx <- "E4.11" > > ### * E4.11 > > flush(stderr()); flush(stdout()) > > ### Name: E4.11 > ### Title: Data on Cost of Repairing Starters, Ring Gears or Both in Diesel > ### Engines > ### Aliases: E4.11 > ### Keywords: datasets > > ### ** Examples > > data(E4.11) > E4.11.m1 <- lm(Cost ~ Part - 1, data=E4.11) > summary(E4.11.m1) Call: lm(formula = Cost ~ Part - 1, data = E4.11) Residuals: Min 1Q Median 3Q Max -363.58 -124.58 -30.58 95.42 644.10 Coefficients: Estimate Std. Error t value Pr(>|t|) PartBoth 430.58 31.54 13.653 <2e-16 *** PartRing gear 424.90 31.54 13.473 <2e-16 *** PartStarter 191.58 20.84 9.193 8e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 175.6 on 130 degrees of freedom Multiple R-Squared: 0.7768, Adjusted R-squared: 0.7717 F-statistic: 150.8 on 3 and 130 DF, p-value: < 2.2e-16 > > > > cleanEx(); ..nameEx <- "E4.12" > > ### * E4.12 > > flush(stderr()); flush(stdout()) > > ### Name: E4.12 > ### Title: Time taken by Professional Dieticians and Interns for Four > ### Patient Contact Activities > ### Aliases: E4.12 > ### Keywords: datasets > > ### ** Examples > > m1 <- lm(Time ~ SC+DC+MR+TR-1, data=E4.12, subset=Dietician=="Prof") > summary(m1) Call: lm(formula = Time ~ SC + DC + MR + TR - 1, data = E4.12, subset = Dietician == "Prof") Residuals: Min 1Q Median 3Q Max -16.037 -8.709 3.843 6.319 14.105 Coefficients: Estimate Std. Error t value Pr(>|t|) SC 6.3422 1.7300 3.666 0.00801 ** DC 2.8937 2.3986 1.206 0.26684 MR 1.5748 0.4156 3.789 0.00681 ** TR 2.9680 1.0383 2.858 0.02439 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 11.52 on 7 degrees of freedom Multiple R-Squared: 0.9985, Adjusted R-squared: 0.9977 F-statistic: 1185 on 4 and 7 DF, p-value: 5.534e-10 > > > > cleanEx(); ..nameEx <- "E4.13" > > ### * E4.13 > > flush(stderr()); flush(stdout()) > > ### Name: E4.13 > ### Title: Data on Hospital Charges > ### Aliases: E4.13 > ### Keywords: datasets > > ### ** Examples > > data(E4.13) > summary(E4.13) Sex MD Svty Chrg Age F:25 499 :15 1:13 Min. : 1487 Min. :17.00 M:24 730 :19 2:12 1st Qu.: 8759 1st Qu.:60.00 1021:15 3:16 Median :15600 Median :67.00 4: 8 Mean :16256 Mean :66.92 3rd Qu.:22642 3rd Qu.:80.00 Max. :64465 Max. :93.00 > > > > cleanEx(); ..nameEx <- "E4.4" > > ### * E4.4 > > flush(stderr()); flush(stdout()) > > ### Name: E4.4 > ### Title: Measures of Quality for Agencies Delivering Transportation for > ### the Elderly and the Handicapped > ### Aliases: E4.4 > ### Keywords: datasets > > ### ** Examples > > data(E4.4) > summary(E4.4) QUAL X.1 X.2 Min. :45.83 Min. :0.0 Min. :0.00 1st Qu.:63.64 1st Qu.:0.0 1st Qu.:0.00 Median :68.82 Median :0.0 Median :0.00 Mean :67.91 Mean :0.4 Mean :0.15 3rd Qu.:72.19 3rd Qu.:1.0 3rd Qu.:0.00 Max. :82.92 Max. :1.0 Max. :1.00 > > > > cleanEx(); ..nameEx <- "E4.7" > > ### * E4.7 > > flush(stderr()); flush(stdout()) > > ### Name: E4.7 > ### Title: Data on Per-Capita Income and Life Expectancy > ### Aliases: E4.7 > ### Keywords: datasets > > ### ** Examples > > data(E4.7) > attach(E4.7) > plot(INC, LIFE) > plot(log(INC), LIFE) > detach() > > > > cleanEx(); ..nameEx <- "E6.1" > > ### * E6.1 > > flush(stderr()); flush(stdout()) > > ### Name: E6.1 > ### Title: Data on Automobile Speed and Distance Covered to Come to a > ### Standstill after Breaking > ### Aliases: E6.1 > ### Keywords: datasets > > ### ** Examples > > data(E6.1) > attach(E6.1) > plot(sp., d.) > detach() > > > > cleanEx(); ..nameEx <- "E6.10" > > ### * E6.10 > > flush(stderr()); flush(stdout()) > > ### Name: E6.10 > ### Title: Data on Perceived and Computed Travel Times by Bus > ### Aliases: E6.10 > ### Keywords: datasets > > ### ** Examples > > data(E6.10) > ## Manipulations of the data for example 8.1, page 161: > t <- c(0,1,rep(0,20),1,rep(0,5),1,rep(0,3)) > e6.10 <- data.frame(E6.10, t=t) > rm(t) > summary(e6.10) n x y t Min. : 1.000 Min. :20.00 Min. :25.00 Min. :0.00000 1st Qu.: 2.000 1st Qu.:24.75 1st Qu.:30.00 1st Qu.:0.00000 Median : 3.000 Median :30.00 Median :35.00 Median :0.00000 Mean : 3.812 Mean :30.00 Mean :36.20 Mean :0.09375 3rd Qu.: 4.250 3rd Qu.:35.25 3rd Qu.:40.58 3rd Qu.:0.00000 Max. :17.000 Max. :40.00 Max. :57.00 Max. :1.00000 > > > > cleanEx(); ..nameEx <- "E6.11" > > ### * E6.11 > > flush(stderr()); flush(stdout()) > > ### Name: E6.11 > ### Title: Heights of Fathers and Sons > ### Aliases: E6.11 > ### Keywords: datasets > > ### ** Examples > > data(E6.11) > summary(E6.11) Height.Father Aver.Height.Son No.Fathers Min. :62.00 Min. :65.50 Min. : 2.0 1st Qu.:64.75 1st Qu.:66.80 1st Qu.: 7.5 Median :67.50 Median :68.20 Median :17.0 Mean :67.50 Mean :68.42 Mean :16.0 3rd Qu.:70.25 3rd Qu.:69.70 3rd Qu.:26.0 Max. :73.00 Max. :72.00 Max. :27.0 > > > > cleanEx(); ..nameEx <- "E6.8" > > ### * E6.8 > > flush(stderr()); flush(stdout()) > > ### Name: E6.8 > ### Title: Dial-a-ride Data > ### Aliases: E6.8 > ### Keywords: datasets > > ### ** Examples > > data(E6.8) > summary(E6.8) POP AR RDR HR Min. : 3025 Min. : 2.300 Min. : 56.0 Min. : 4.00 1st Qu.: 13241 1st Qu.: 4.375 1st Qu.: 202.8 1st Qu.:12.00 Median : 24108 Median : 6.450 Median : 272.5 Median :12.00 Mean : 28113 Mean : 30.993 Mean : 415.7 Mean :12.96 3rd Qu.: 31712 3rd Qu.: 10.775 3rd Qu.: 392.5 3rd Qu.:14.50 Max. :102711 Max. :568.000 Max. :3400.0 Max. :24.00 VH F IND Min. : 2.000 Min. :0.0100 Min. :0.0000 1st Qu.: 3.250 1st Qu.:0.3500 1st Qu.:0.0000 Median : 4.500 Median :0.5000 Median :0.0000 Mean : 6.074 Mean :0.4404 Mean :0.4444 3rd Qu.: 6.750 3rd Qu.:0.5000 3rd Qu.:1.0000 Max. :22.000 Max. :1.0000 Max. :1.0000 > > > > cleanEx(); ..nameEx <- "E7.1" > > ### * E7.1 > > flush(stderr()); flush(stdout()) > > ### Name: E7.1 > ### Title: Data on Dental Measurements > ### Aliases: E7.1 > ### Keywords: datasets > > ### ** Examples > > data(E7.1) > summary(E7.1) Age S.1 S.2 S.3 S.4 Min. : 8.0 Min. :20.00 Min. :21.00 Min. :20.50 Min. :23.50 1st Qu.: 9.5 1st Qu.:20.75 1st Qu.:21.38 1st Qu.:23.12 1st Qu.:24.25 Median :11.0 Median :21.25 Median :22.75 Median :24.25 Median :24.75 Mean :11.0 Mean :21.38 Mean :23.00 Mean :23.75 Mean :24.88 3rd Qu.:12.5 3rd Qu.:21.88 3rd Qu.:24.38 3rd Qu.:24.88 3rd Qu.:25.38 Max. :14.0 Max. :23.00 Max. :25.50 Max. :26.00 Max. :26.50 S.5 S.6 S.7 S.8 Min. :21.50 Min. :20.00 Min. :21.50 Min. :23.00 1st Qu.:22.25 1st Qu.:20.75 1st Qu.:22.25 1st Qu.:23.00 Median :22.75 Median :21.00 Median :22.75 Median :23.25 Mean :22.62 Mean :21.12 Mean :23.00 Mean :23.38 3rd Qu.:23.12 3rd Qu.:21.38 3rd Qu.:23.50 3rd Qu.:23.62 Max. :23.50 Max. :22.50 Max. :25.00 Max. :24.00 S.9 S.10 S.11 Min. :20.00 Min. :16.50 Min. :24.50 1st Qu.:20.75 1st Qu.:18.38 1st Qu.:24.88 Median :21.25 Median :19.00 Median :26.50 Mean :21.12 Mean :18.50 Mean :26.38 3rd Qu.:21.62 3rd Qu.:19.12 3rd Qu.:28.00 Max. :22.00 Max. :19.50 Max. :28.00 > > > > cleanEx(); ..nameEx <- "E7.2" > > ### * E7.2 > > flush(stderr()); flush(stdout()) > > ### Name: E7.2 > ### Title: Prices of Crude Oil, Natural Gas, Bituminous Coal and Lignite, > ### and Anthracite by Year. > ### Aliases: E7.2 > ### Keywords: datasets > > ### ** Examples > > data(E7.2) > summary(E7.2) year Oil Gas Bit. Min. :1950 Min. : 58.40 Min. :11.60 Min. :22.50 1st Qu.:1958 1st Qu.: 65.90 1st Qu.:16.07 1st Qu.:25.55 Median :1966 Median : 75.85 Median :18.30 Median :29.90 Mean :1966 Mean : 88.40 Mean :26.54 Mean :36.38 3rd Qu.:1973 3rd Qu.: 87.30 3rd Qu.:21.02 3rd Qu.:40.23 Max. :1981 Max. :273.60 Max. :96.00 Max. :65.90 Anth. Min. : 43.90 1st Qu.: 49.55 Median : 55.10 Mean : 66.50 3rd Qu.: 74.17 Max. :112.60 > > > > cleanEx(); ..nameEx <- "E7.3" > > ### * E7.3 > > flush(stderr()); flush(stdout()) > > ### Name: E7.3 > ### Title: Data on Intake/Output Ratio > ### Aliases: E7.3 > ### Keywords: datasets > > ### ** Examples > > data(E7.3) > summary(E7.3) G u.1 u.2 u.3 surfactant:12 Min. :0.000 Min. :0.1700 Min. :0.140 placebo : 7 1st Qu.:0.125 1st Qu.:0.2900 1st Qu.:0.375 Median :0.290 Median :0.4100 Median :0.460 Mean :0.400 Mean :0.4568 Mean :0.560 3rd Qu.:0.565 3rd Qu.:0.5900 3rd Qu.:0.795 Max. :1.810 Max. :1.0000 Max. :1.200 u.4 u.5 Min. :0.2800 Min. :0.3500 1st Qu.:0.5300 1st Qu.:0.7050 Median :0.6700 Median :0.9300 Mean :0.8326 Mean :0.9626 3rd Qu.:1.0050 3rd Qu.:1.1400 Max. :1.9900 Max. :2.7000 > > > > cleanEx(); ..nameEx <- "E7.4" > > ### * E7.4 > > flush(stderr()); flush(stdout()) > > ### Name: E7.4 > ### Title: Data on PCO2 and Cerebral Blood Flow for Five Regions of the > ### Brain of five Chimpanzees > ### Aliases: E7.4 > ### Keywords: datasets > > ### ** Examples > > data(E7.4) > summary(E7.4) Ch. Fron.x Fron.y Pari.x Pari.y Min. :1 Min. :30.30 Min. :43.60 Min. :29.6 Min. :62.30 1st Qu.:2 1st Qu.:31.00 1st Qu.:56.80 1st Qu.:30.3 1st Qu.:73.20 Median :3 Median :35.10 Median :60.50 Median :34.0 Median :92.90 Mean :3 Mean :33.52 Mean :57.52 Mean :33.3 Mean :84.62 3rd Qu.:4 3rd Qu.:35.10 3rd Qu.:62.40 3rd Qu.:35.8 3rd Qu.:95.10 Max. :5 Max. :36.10 Max. :64.30 Max. :36.8 Max. :99.60 Occi.x Occi.y Temp.x Temp.y Cere.x Min. :28.70 Min. :40.10 Min. :28.5 Min. :79.10 Min. :30.20 1st Qu.:30.30 1st Qu.:44.50 1st Qu.:29.3 1st Qu.:83.00 1st Qu.:30.30 Median :34.40 Median :51.60 Median :35.2 Median :86.50 Median :33.40 Mean :32.86 Mean :55.72 Mean :33.3 Mean :88.62 Mean :33.22 3rd Qu.:34.90 3rd Qu.:70.70 3rd Qu.:36.1 3rd Qu.:97.00 3rd Qu.:35.00 Max. :36.00 Max. :71.70 Max. :37.4 Max. :97.50 Max. :37.20 Cere.y Min. :42.20 1st Qu.:47.10 Median :55.70 Mean :56.36 3rd Qu.:61.80 Max. :75.00 > > > > cleanEx(); ..nameEx <- "E7.5" > > ### * E7.5 > > flush(stderr()); flush(stdout()) > > ### Name: E7.5 > ### Title: Data on Static Weights and Weight in Motion of Trucks > ### Aliases: E7.5 > ### Keywords: datasets > > ### ** Examples > > data(E7.5) > summary(E7.5) sw.1 wim.1 sw.23 wim.23 Min. : 8660 Min. : 5616 Min. :11920 Min. :13104 1st Qu.: 8860 1st Qu.: 6240 1st Qu.:18295 1st Qu.:19448 Median : 9740 Median : 6864 Median :26880 Median :29952 Mean : 9936 Mean : 7208 Mean :24425 Mean :27072 3rd Qu.:10710 3rd Qu.: 8112 3rd Qu.:30890 3rd Qu.:33904 Max. :12740 Max. :10192 Max. :33560 Max. :42016 sw.45 wim.45 Min. : 8520 Min. : 9984 1st Qu.:15830 1st Qu.:16640 Median :23110 Median :20384 Mean :22653 Mean :22588 3rd Qu.:31225 3rd Qu.:29952 Max. :34100 Max. :37024 > plot(E7.5) > > > > cleanEx(); ..nameEx <- "E7.6" > > ### * E7.6 > > flush(stderr()); flush(stdout()) > > ### Name: E7.6 > ### Title: Community Area Data for the North Part of the City of Chicago > ### Aliases: E7.6 > ### Keywords: datasets > > ### ** Examples > > data(E7.6) > summary(E7.6) Area.Name PB PS PA Length:34 Min. : 0.000 Min. : 0.820 Min. : 4.960 Class :character 1st Qu.: 0.230 1st Qu.: 1.893 1st Qu.: 9.402 Mode :character Median : 1.905 Median : 5.830 Median :14.325 Mean :21.147 Mean :13.930 Mean :13.969 3rd Qu.:23.775 3rd Qu.:18.957 3rd Qu.:18.580 Max. :99.000 Max. :77.570 Max. :22.680 Income Min. : 7326 1st Qu.:14667 Median :19826 Mean :19391 3rd Qu.:24335 Max. :31651 > > > > cleanEx(); ..nameEx <- "E8.12" > > ### * E8.12 > > flush(stderr()); flush(stdout()) > > ### Name: E8.12 > ### Title: Data on Lung Cancer Deaths and Cigarette Smoking > ### Aliases: E8.12 > ### Keywords: datasets > > ### ** Examples > > data(E8.12) > summary(E8.12) Country y x Length:11 Min. : 58.0 Min. : 220.0 Class :character 1st Qu.:132.5 1st Qu.: 345.0 Mode :character Median :170.0 Median : 460.0 Mean :204.4 Mean : 605.0 3rd Qu.:247.5 3rd Qu.: 822.5 Max. :465.0 Max. :1280.0 > > > > cleanEx(); ..nameEx <- "E8.13" > > ### * E8.13 > > flush(stderr()); flush(stdout()) > > ### Name: E8.13 > ### Title: Florida Cumulus Experiment Data > ### Aliases: E8.13 > ### Keywords: datasets > > ### ** Examples > > data(E8.13) > summary(E8.13) A T S C P NoSeed:10 Min. : 0.00 Min. :1.600 Min. : 2.200 Min. :0.0180 Seed :10 1st Qu.:21.00 1st Qu.:2.725 1st Qu.: 3.700 1st Qu.:0.1330 Median :32.50 Median :3.250 Median : 5.250 Median :0.2060 Mean :34.55 Mean :3.186 Mean : 6.035 Mean :0.2574 3rd Qu.:53.75 3rd Qu.:3.962 3rd Qu.: 7.025 3rd Qu.:0.2560 Max. :82.00 Max. :4.400 Max. :13.400 Max. :0.7960 E y Moving :15 Min. :-0.190 Stationary: 5 1st Qu.: 0.980 Median : 1.500 Mean : 1.335 3rd Qu.: 1.755 Max. : 2.550 > plot(E8.13) > > > > cleanEx(); ..nameEx <- "E9.11" > > ### * E9.11 > > flush(stderr()); flush(stdout()) > > ### Name: E9.11 > ### Title: Data on Transit Privatization > ### Aliases: E9.11 > ### Keywords: datasets > > ### ** Examples > > data(E9.11) > summary(E9.11) V1 V2 V3 V4 Min. : 9.00 Min. :0.0000 Min. : 7.57 Min. : 27.93 1st Qu.:35.00 1st Qu.:0.4500 1st Qu.:12.63 1st Qu.:145.00 Median :45.00 Median :0.8300 Median :14.21 Median :220.00 Mean :43.24 Mean :0.7035 Mean :16.66 Mean :288.59 3rd Qu.:55.00 3rd Qu.:1.0000 3rd Qu.:19.03 3rd Qu.:379.45 Max. :69.00 Max. :1.0000 Max. :33.29 Max. :810.98 V5 V6 V7 V8 Min. : 0.000 Min. : 24.15 Min. : 0.120 Min. :0.0000 1st Qu.: 0.000 1st Qu.: 38.99 1st Qu.: 0.760 1st Qu.:0.0000 Median : 0.000 Median : 1327.02 Median : 3.520 Median :1.0000 Mean : 8.412 Mean : 2480.35 Mean : 4.905 Mean :0.6794 3rd Qu.:15.000 3rd Qu.: 1861.85 3rd Qu.: 4.380 3rd Qu.:1.0000 Max. :25.000 Max. :16120.02 Max. :16.680 Max. :1.0000 V9 PCS Min. : 6.090 Min. : 0.10 1st Qu.: 6.940 1st Qu.:19.80 Median : 7.340 Median :30.00 Mean : 7.798 Mean :28.71 3rd Qu.: 8.340 3rd Qu.:45.90 Max. :11.560 Max. :50.00 > plot(E9.11) > > > > cleanEx(); ..nameEx <- "E9.18" > > ### * E9.18 > > flush(stderr()); flush(stdout()) > > ### Name: E9.18 > ### Title: Data Travel Times and Usage for Automobiles and Public > ### Transportation > ### Aliases: E9.18 > ### Keywords: datasets > > ### ** Examples > > data(E9.18) > summary(E9.18) t.a t.r m.a m.r Min. : 70.0 Min. :120.0 Min. : 5.00 Min. : 10.00 1st Qu.: 290.0 1st Qu.:331.5 1st Qu.: 13.00 1st Qu.: 24.00 Median : 650.0 Median :408.0 Median : 25.00 Median : 41.00 Mean : 598.7 Mean :385.2 Mean : 29.39 Mean : 69.41 3rd Qu.: 900.0 3rd Qu.:454.5 3rd Qu.: 36.50 3rd Qu.: 80.50 Max. :1200.0 Max. :600.0 Max. :147.00 Max. :313.00 > plot(E9.18) > > > > cleanEx(); ..nameEx <- "E9.19" > > ### * E9.19 > > flush(stderr()); flush(stdout()) > > ### Name: E9.19 > ### Title: Acceleration data > ### Aliases: E9.19 > ### Keywords: datasets > > ### ** Examples > > data(E9.19) > summary(E9.19) ACC WHP SP G Min. :0.200 Min. : 20.50 Min. : 7.5 Min. :0.00 1st Qu.:0.700 1st Qu.: 20.50 1st Qu.:22.5 1st Qu.:0.00 Median :1.700 Median : 40.00 Median :30.0 Median :2.00 Mean :2.606 Mean : 77.38 Mean :30.4 Mean :2.12 3rd Qu.:3.925 3rd Qu.: 84.50 3rd Qu.:45.0 3rd Qu.:2.00 Max. :8.000 Max. :257.00 Max. :60.0 Max. :6.00 > plot(E9.19) > > > > cleanEx(); ..nameEx <- "E9.20" > > ### * E9.20 > > flush(stderr()); flush(stdout()) > > ### Name: E9.20 > ### Title: Stadium Cleanup Data > ### Aliases: E9.20 > ### Keywords: datasets > > ### ** Examples > > data(E9.20) > summary(E9.20) C R.HD R.B Min. : 6.700 Min. :113.0 Min. : 97.0 1st Qu.: 8.425 1st Qu.:231.8 1st Qu.:301.5 Median : 9.050 Median :392.0 Median :394.5 Mean : 9.819 Mean :421.7 Mean :424.0 3rd Qu.:10.700 3rd Qu.:664.5 3rd Qu.:514.0 Max. :14.700 Max. :840.0 Max. :917.0 > plot(E9.20) > > > > cleanEx(); ..nameEx <- "E9.21" > > ### * E9.21 > > flush(stderr()); flush(stdout()) > > ### Name: E9.21 > ### Title: Depreciation in Market Value of Large Factories > ### Aliases: E9.21 > ### Keywords: datasets > > ### ** Examples > > data(E9.21) > summary(E9.21) Age Depr Min. : 7.00 Min. :60.70 1st Qu.:19.50 1st Qu.:69.85 Median :22.00 Median :81.30 Mean :24.27 Mean :80.43 3rd Qu.:28.00 3rd Qu.:89.30 Max. :48.00 Max. :96.90 > plot(E9.21) > > > > cleanEx(); ..nameEx <- "E9.3" > > ### * E9.3 > > flush(stderr()); flush(stdout()) > > ### Name: E9.3 > ### Title: "Areas", lengths and widths of rectangles > ### Aliases: E9.3 > ### Keywords: datasets > > ### ** Examples > > data(E9.3) > E9.3.m1 <- lm(y ~ x1 + x2, data=E9.3) > attach(E9.3) > plot(x1, resid(E9.3.m1)) > plot(x2, resid(E9.3.m1)) > detach(E9.3) > > > > cleanEx(); ..nameEx <- "E9.8" > > ### * E9.8 > > flush(stderr()); flush(stdout()) > > ### Name: E9.8 > ### Title: Data on monthly rent, annual income and househould size > ### Aliases: E9.8 > ### Keywords: datasets > > ### ** Examples > > data(E9.8) > attach(E9.8) > E9.8.m1 <- lm(R ~ I + S, data=E9.8) > summary(E9.8.m1) Call: lm(formula = R ~ I + S, data = E9.8) Residuals: Min 1Q Median 3Q Max -144.10 -49.81 10.60 45.49 125.38 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -95.299 35.263 -2.703 0.0124 * I 15.261 1.422 10.734 1.21e-10 *** S 121.565 9.681 12.558 4.85e-12 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 69.17 on 24 degrees of freedom Multiple R-Squared: 0.9341, Adjusted R-squared: 0.9287 F-statistic: 170.2 on 2 and 24 DF, p-value: 6.655e-15 > plot(I, resid(E9.8.m1, type="partial")[,"I"]) > plot(S, resid(E9.8.m1, type="partial")[,"S"]) > detach() > > > > cleanEx(); ..nameEx <- "Ec.8" > > ### * Ec.8 > > flush(stderr()); flush(stdout()) > > ### Name: Ec.8 > ### Title: Data on asylum requests to the U.S. by country of origen of > ### applicant > ### Aliases: Ec.8 > ### Keywords: datasets > > ### ** Examples > > data(Ec.8) > summary(Ec.8) Country APR DEN H Length:112 Min. : 0.00 Min. : 0.0 Min. :0.0000 Class :character 1st Qu.: 0.00 1st Qu.: 4.0 1st Qu.:0.0000 Mode :character Median : 1.00 Median : 13.5 Median :0.0000 Mean : 136.84 Mean : 495.6 Mean :0.2411 3rd Qu.: 9.75 3rd Qu.: 105.5 3rd Qu.:0.0000 Max. :9556.00 Max. :18258.0 Max. :1.0000 E Min. :0.0000 1st Qu.:0.0000 Median :0.0000 Mean :0.1964 3rd Qu.:0.0000 Max. :1.0000 > attach(Ec.8) > Ec.8.m1 <- glm(cbind(APR, DEN) ~ E + H, data=Ec.8, family=binomial) > summary(Ec.8.m1) Call: glm(formula = cbind(APR, DEN) ~ E + H, family = binomial, data = Ec.8) Deviance Residuals: Min 1Q Median 3Q Max -62.8621 -0.9766 -0.3771 1.5050 60.0319 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -3.31876 0.03317 -100.060 <2e-16 *** E 0.02437 0.02886 0.844 0.398 H 2.59739 0.03490 74.417 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 19829.2 on 111 degrees of freedom Residual deviance: 9522.6 on 109 degrees of freedom AIC: 9800.6 Number of Fisher Scoring iterations: 5 > detach() > > > > cleanEx(); ..nameEx <- "Ex.7.7" > > ### * Ex.7.7 > > flush(stderr()); flush(stdout()) > > ### Name: Ex.7.7 > ### Title: U.S. Population in thousands, for exercise 7.7 > ### Aliases: Ex.7.7 > ### Keywords: datasets > > ### ** Examples > > ##---- Should be DIRECTLY executable !! ---- > data(Ex.7.7) > with(Ex.7.7, plot(y ~ t)) > summary(Ex.7.7) y t Min. : 3929 Min. :1790 1st Qu.: 14968 1st Qu.:1835 Median : 50155 Median :1880 Mean : 69767 Mean :1880 3rd Qu.:114242 3rd Qu.:1925 Max. :203211 Max. :1970 > > > > cleanEx(); ..nameEx <- "Ex4.4" > > ### * Ex4.4 > > flush(stderr()); flush(stdout()) > > ### Name: Ex4.4 > ### Title: Data on Effects of Air Pollution on Interpersonal Attraction > ### Aliases: Ex4.4 > ### Keywords: datasets > > ### ** Examples > > data(Ex4.4) > summary(Ex4.4) Likert Odor Culture Min. : 1.000 Free:12 Dissimilar:12 1st Qu.: 2.000 Odor:12 Similar :12 Median : 3.500 Mean : 4.833 3rd Qu.: 7.250 Max. :10.000 > plot(Ex4.4) > > > > ### *