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Minnesota - CSCI - 8211
Computing the Types of the Relationships between Autonomous SystemsGiuseppe Di Battista, Maurizio Patrignani, and Maurizio PizzoniaDipartimento di Informatica e Automazione, Universit` di Roma Tre, Rome, Italy a Email: {gdb,patrigna,pizzonia}@dia.u
Minnesota - CSCI - 1902
/ Example 16/ A 2-dimensional array example / Builds an array of powerspublic class Array2d { public static void main(String[] args) { final int LENGTH = 10; / declaration of a symbolic constant final int WIDTH = 5; / ano
Minnesota - CSCI - 1902
/ Example 16.5/ Ragged arrays of 2 dimensions/ "rows" of 2-dimensional arrays need not be all the same length/ even though the base type (here int) must be the same for all/ the lowest level elements./ Here, each element of the first dimension
Minnesota - CSCI - 5271
Anti-Jamming: A StudyKarthikeyan Mahadevan, Sojeong Hong, John Dullum December 14, 2005AbstractAddressing jamming in wireless networks is important as the number of wireless networks is on the increase. In this paper, we present a new mechanism t
Minnesota - CSCI - 5980
YCheng,GMChurchProcIntConfIntellSystMol Biol,2000Biclustering:groupsgenesandconditions simultaneously. Selectgeneandconditionswithmorecoherent measurement Groupitemsbasedonasimilaritymeasuresthat dependsonabestdefinedsubsetofattributes. Allowrow
Minnesota - PHELP - 008
THE WILSON ADMINISTRATION IN LATIN AMERICAWilson wanted an orderly democratization in Latin America and continued economic opportunities for American businesses; when he couldn't get everything he wanted in the region, he made the maintenance of or
Minnesota - PHELP - 008
WORLD WAR I AS AN OPPORTUNITY FOR CHANGEAmerican traditions of non-involvement with European political affairs and free access to foreign markets came into conflict during World War I; initial American neutrality increasingly gave way to a pro-Allie
Minnesota - MOORE - 144
-.=.Center t o Study Human-Animal R e l a t i o n s h i p s and Environments Box 197 Mayo Bldg. 420 Delaware S t . SE Minneapolis, MN 55455 CEN/SHARE B u l l e t i n , No. 1 S u b j e c t : Pet Therapy(Other terms: Pet F a c i l i t a t e d T
Minnesota - MOORE - 144
:~.UNIVERSITY OF MINNESOTA NEWS SERVICE, S-68 NORRILL HALL E?IIWEAPOLIS, MINNESOTA 55455 AUGUST 29, 1975 NEWS PEOPLE: For further information contact BOB LEE, 373-7510 40 ' ' MEDICAL STUDENTS TO BE U RURAL PIIYSICIAN ASSOCIATES (FOR IMMEDIATE REL
Minnesota - STAT - 8311
Stat 8311 Estimating treatment means, unbalanced data> searle <- data.frame(soil = rep(c("s1", "s2"), c(7, 8), + var = c("v1", "v2", "v3")[c(1, 1, 1, 2, 2, 3, 3, 1, 1, + 1, 1, 2, 3, 3, 3)], y = c(6, 10, 11, 13, 15, 14, + 22, 12, 15, 19, 18, 31, 18,
Minnesota - STAT - 8051
Stat 8051, Fall 2007: Turkey dataThese data are from an experment to compare sources of an essential amino acid called methionine in turkey diets. Sixty pens of turkeys recieved a similar diet, supplemented with methionine from one of three sources
Minnesota - STAT - 8053
Mixed Effects Models for Fish GrowthSanford Weisberg, sandy@stat.umn.edu October 18, 2008Much like tree rings on trees, many fish preserve a record of their growth history in annular rings on fish scales and other bony parts. The number of rings pr
Minnesota - STAT - 8053
Stat 8053, Fall 2008: GLMMsFrom the lme4 package in R: Contagious bovine pleuropneumonia (CBPP) is a major disease of cattle in Africa, caused by a mycoplasma. This dataset describes the serological incidence of CBPP in zebu cattle during a follow-u
Minnesota - STAT - 5302
Physics Data Handout, Stat 5302> Output from Table Data. . . item. Data set = Physics, Data listing Col. 1 = Case-numbers Col. 2 = x Col. 3 = y Col. 4 = S -0 0.345 367 17 1 0.287 311 9 2 0.251 295 9 3 0.225 268 7 4 0.207 253 7 5 0.186 239 6 6 0.161
Minnesota - STAT - 5421
Stat 5421, Fall 2006: Blowdown data, part 4Here is another version of the blowdown data, this time using the species balsam r (BF), Aspen (A) black ash (BA). We consider the predictor D as well as SPP. > > > > + > > options(width = 68) loc <- "http:
Minnesota - STAT - 8053
Stat 8053, Fall 2008: Chapter 11 Cluster AnalysisThe rst example looks at economic data from 69 world cities in 2003, provided by the Union Bank of Switzerland. The variables are: BigMac = Minutes of labor to purchase a Big Mac; Bread = Minutes of l
Minnesota - STAT - 8051
Stat 8051, Fall 2007: Proportional Odds ModelsThe proportional odds model cannot be fit with the glm function. However, this model is so common that software for it is readily available, for example in proc logistic in SAS, in JMP, in special purpos
Minnesota - STAT - 8053
Stat 8053, Fall 2008: L1 and Quantile RegressionReference: R. Koenker (2005). Quantile Regression, Cambridge University Press. See also the vignette for the quantreg package in R on the class website.Sample and population quantilesGiven an distri
Minnesota - STAT - 8051
1Stat 8051, Fall 2007: Logistic RegressionLogistic regression is the forward problem of the study of the distribution of (y|x). Since y can only equal two values, there is value in study of the inverse problem of x|y through the conditional densi
Minnesota - STAT - 8051
Stat 8051, Fall 2007: Logistic RegressionOn July 4, 1999 a huge windstorm caused widespread devastation of trees in the Boundary Water Canoe Area Wilderness in northern Minnesota. A survey done later examined trees to provide data to model survival
Minnesota - STAT - 8053
Stat 8053, Fall 2008: Smoothing, Ch. 11Kernel smoothingKernel smoothing is essentially weighted local averaging. It balances bias and variability using a smoothing parameter that essentially controls how many points get high weight in the averaging
Minnesota - STAT - 8053
Stat 8053, Fall 2008: Chapter 9This follows the results in Chapter 9 of Hrdle and Simar on principal component analysis. The a first example is the banknote data discussed in this book, in Faraway, and in Weisberg (1985). > data(banknote, package =
Minnesota - STAT - 8051
Stat 8051, Fall 2007: More TransformationsBox-Cox transformation of the response> > > > > > > + library(alr3) library(MASS) data(wool) par(mfrow = c(1, 2) m1 <- lm(Cycles ~ ., wool) boxcox(m1) boxcox(Days + 1 ~ Eth * Sex * Age * Lrn, data = quine,
Minnesota - STAT - 8051
Stat 8051, Fall 2007: PrestigeThese data are the running example in the car package. The response is the prestige rating of 102 professions. Potential predictors are income, education and women, the fraction of the profession that is women. An addit
Minnesota - STAT - 5401
THE UNIVERSITY OF MINNESOTA Statistics 5401/8401 Solutions to Sample Midterm Examination The exhibits that were in a separate booklet are included here. Instead of tables of Bonferronized F-probability points, MacAnova output is used. Exhibit 1 (for
Minnesota - STAT - 5401
THE UNIVERSITY OF MINNESOTA Statistics 5401 Multi-group Profile Analysis Example This handout provides an analysis of some artificial data from Example 5.9 on p. 240 of Multivariate Statistical Methods, 3rd Edition by Donald F. Morrison, McGraw Hill
Minnesota - STAT - 5401
C ( SSSS hhhh TTTT AeTUUUU SSSS TTT UUUU eee UUUU SSSS iPpppp PPP eeee iii iiii QQQQ dSSSS(Ud3Qdd UUU QQQ RRRR iiii HHHH hhhh ffff gggg eeee eeee UUUU eeee ffff gggg TTTT ffff eeee dddd cccc # 7 B7@%(A$3 4(%'%&4% 3 B4 % A &%A D#%#7%(7A1@ #155(@7#4
Minnesota - STAT - 8401
L30data 100 5 LABELS) Artifical data generated from factor analytic model with) m = 2 factors, used in Lecture 30, 11/16/05)"%lf %lf %lf %lf %lf" 70.6 36.5 109.5 104.0 73.6 33.3 44.9 102.2 107.4 81.8 68.1 54.6 118.0 102.0 62
Minnesota - CH - 5021
# File ex12_030.txt from publisher's web site# Data for Ex. 12.30, p. 789 of IPS4# Col. 1: id = board number (1-24)# Col. 2: color (factor, 1=Blue, 2=green, 3=Lemon, 4=White)# Col. 3: insects = number of cerial leaf beetles trapped id color in
Minnesota - STAT - 5021
# Data on y = Nickel/Iron ratio in oat plants grown# in sand cultures for x = 4 days# Col. 1: days = Time (days) in sand cultures# Col. 2: ni_fe = Nickel/Iron ratio in oat plantsdays ni_fe 4 0.32 9 0.41 14 0.79 18 0.86 22
Minnesota - CH - 5021
# File ex12_016.txt from publisher's web site# Data for Ex. 12.16, p. 785 of IPS4 (Table 12.3)# Col. 1: id = subject number (1-160)# Col. 2: promotions = number of promotions# Col. 3: price = estimated price of supermarket product ($)# Use group
Minnesota - CH - 5021
# File ex13_012.txt from publisher's web site# Data for Ex. 13.12, p. 820 of IPS4# Col. 1: id = group number (1-6)# Col. 2: gender (factor, 1=Females, 2=Males)# Col. 3: major (factor, 1=CS, 2=EO, 3=O)# Col. 4: grades = mean high school math grad
Minnesota - CH - 5021
# File ex12_013.txt from publisher's web site# Data for Ex. 12.13, p. 784 of IPS4# Col. 1: id = case number (1-10)# Col. 2: time = days after baking# Col. 3: vitamina = Vitamin A content (mg/100 g)# Col. 4: vitamine = Vitamin E content (mg/100 g
Minnesota - CH - 5021
# File ex12_021.txt from publisher's web site# Data for Ex. 12.21, p. 786 of IPS4# Col. 1: id = infant number (1-45)# Col. 2: bftime (factor, 1=BF4, 2=BF5, 3=BF6, months)# Col. 3: energy = energy intake, (kcal/d) id bftime energy 1 BF4 499
Minnesota - CH - 5021
# File ex11_051.txt from publisher's web site# Data for Ex. 11.51, p. 743 of IPS4 (Table 10.1, p. 694)# Col. 1: id = woman number (1-60)# Col. 2: wages = proportional to weekly wages# Col. 3: los = length of service (months)# Col. 4: size = ban
Minnesota - STAT - 5931
cross$ export LD_LIBRARY_PATH=/APPS/ggobi/libcross$ R> library("Rggobi")>> data(mtcars)>> class(mtcars)[1] "data.frame"> names(mtcars) [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear"[11] "carb"> help(mtcars)> gg
Minnesota - STAT - 5303
Stat 5303Designing ExperimentsFall Semester 2007Time: Location: Textbook: Web page: Instructor:10:10 a.m. to 11:00 a.m. on Mondays, Wednesdays, and Fridays Ford 115 A First Course in Design and Analysis of Experiments by Oehlert http:/www.sta
Minnesota - STAT - 3022
zip fire theft age income race vol invol26 6.2 29 60.4 11744 10 5.3 040 9.5 44 76.5 9323 22.2 3.1 0.113 10.5 36 73.5 9948 19.6 4.8 1.257 7.7 37 66.9 10656 17.3 5.7 0.5
Minnesota - STAT - 3022
jnt1 jnt2 species191 131 conc185 134 conc200 137 conc173 127 conc171 118 conc160 118 conc188 134 conc186 129 conc174 131 conc163 115 conc190 143 conc174 131 conc
Minnesota - STAT - 3022
zinc code185 1189 1187 1181 1150 1176 1171 2174 2202 2171 2207 2125 2189 2179 2163 2174 2184 2186 2210 3139 3172 3198 3177 3
Minnesota - STAT - 3022
temp failure53 156 157 163 066 067 067 067 068 069 070 070 170 170 172 073 075 075 176 076 078 079 080 081
Minnesota - STAT - 3022
absent machine-841 -1375-436 -599-224 -129160 47597 7132-356 1116300 940288 987609 1927412 2150167 2573364 2620384 3596320 3619240 41951365 4395392 4512404 5088408
Minnesota - STAT - 3022
carbonat calcite21.3 20.523.6 23.226.7 27.026.8 28.727.0 27.927.1 27.927.2 29.027.3 27.727.5 27.627.6 29.628.0 28.028.0 29.228.2 27.529.3 28.429.4 28.529.4 29.329.4 30.029.5 31.0
Minnesota - STAT - 3022
absentee machines-841 -1375-436 -599-224 -129160 47597 7132-356 1116300 940288 987609 1927412 2150168 2573364 2620384 3596320 3619240 41951365 4395392 4512404 50884
Minnesota - STAT - 3022
planet order distance Mercury 1 3.87 Venus 2 7.23 Earth 3 10.00 Mars 4 15.24 Asteroids 5 29.00 Jupiter 6 52.03 Saturn 7 95.46 Uranus 8 192.00 Neptune 9 300.90 Pluto 10 395.00
Minnesota - STAT - 3022
brain liver time treat days sex weight loss tumor41081 1456164 .5 BD 10 F 239 5.9 22144286 1602171 .5 BD 10 F 225 4 246102926 1601936 .5 BD 10 F 224 -4.9
Minnesota - STAT - 3022
area species44218 10029371 1084244 453435 5332 165 111 7
Minnesota - STAT - 3022
context mode level m percent1 1 1 132 20.5 2 1 1 132 31.11 1 2 132 28.02 1 2 131 38.91 2 1 132 34.12 2 1 131 48.91 2 2 132 23.52 2 2 123 45.5
Minnesota - STAT - 3022
company treat score1 1 80.01 2 63.21 2 69.22 1 83.92 2 63.12 2 81.53 1 68.23 2 76.24 1 76.54 2 59.54 2 73.5
Minnesota - STAT - 3022
time voltage code5.79 26 11579.52 26 12323.7 26 168.85 28 2108.29 28 2110.29 28 2426.07 28 21067.6 28 27.74 30 317.05 30 320.46 30 321.02 30 322.66 30 343.4 30 347.3 30 3139.07 30 3144.12 30 3175.88 30 3194.9 30 30.27 32 40.4 32
Minnesota - STAT - 5931
> n <- 300> x <- runif(n)> y <- runif(n)> plot(x, y)> plot(x, y, asp = 1)> lines(c(0, 1, 1, 0, 0), c(0, 0, 1, 1, 0)> plot(x, y, asp = 1, type = "n", axes = FALSE, xlab = ", ylab = ")> lines(c(0, 1, 1, 0, 0), c(0, 0, 1, 1, 0)> points(x, y)>
Minnesota - CH - 5021
# File ex08_066.txt from publisher's web site# Data for Ex. 8.66, p. 603 of IPS4# Col. 1: text = text number (1-10)# Col. 2: girl = number of usages of "girl"# Col. 3: woman = number of usages of "woman"# Col. 4: boy = number of usages of "boy"
Minnesota - CH - 5021
# File ex10_044.txt from publisher's web site# Data for Ex. 10.44, p. 705 of IPS4# Col. 1: id = perch number (1-12)# Col. 2: weight = fish weigh (g)# Col. 3: len = fish length (cm)# Col. 4: width = fish width (cm)perch weight len width 1
Minnesota - CH - 5021
# File ta02_009.txt from publisher's web site# Data from Table 2.9, p. 161 of IPS4# Col. 1: child = child number (1 - 21)# Col. 2: age = age of child (months)# Col. 3: score = Gesell score of childchild age score 1 15 95 2 26 71
Minnesota - CH - 5021
# File ex10_004.txt from publisher's web site# Data for Ex. 10.4, p. 692 of IPS4# Col. 1: id = student number (1-16)# Col. 2: beers = number of cans of beer# Col. 3: bac = blood alcohol content id beers bac 1 5 0.100 2 2 0.030 3
Minnesota - CH - 5021
# File ex09_032.txt from publisher's web site# Data for Ex. 9.32, p. 647 of IPS4# Col. 1: responded (factor, 1=no, 2=yes)# Col. 2: letter (factor, 1=letter, 2=none)# Col. 3: count = number of physicians in cell# Note: cell number column dropped
Minnesota - CH - 5021
# File ex07_058.txt from publisher's web site# Data for Ex. 7.58, p. 544 of IPS4# Col. 1: apt = apartment number# Col. 2: rooms = number of rooms (1 or 2)# Col. 3: rent = rent ($/mo)apt rooms rent 1 2 595 2 2 500 3 2 580
Minnesota - CH - 5021
# File ex07_060.txt from publisher's web site# Data for Ex. 7.60, p. 545 of IPS4# Col. 1: loaf = loaf number (1-4)# Col. 2: days = days stored# Col. 3: vitaminc = vitamin C content (mg/100 g)loaf days vitaminc 1 0 47.62 2 0 49.79
Minnesota - CH - 5021
# File ex09_035.txt from publisher's web site# Data for Ex. 9.35, p. 648 of IPS4# Col. 1: current (factor 1=thislose, 2=thiswin)# Col. 2: next (factor (1=nextlose, 2=nextwin)# Col. 3: funds = number of stock funds# Note: cell number column dropp
Minnesota - CH - 5021
# File ex09_020.txt from publisher's web site# Data for Ex. 9.20, p. 643 of IPS4# Col. 1: wine (factor, 1=French, 2=Italian, 3=Other)# Col. 2: music (factor, 1=French, 2=Italian, 3=None)# Col. 3: count = number of purchases in cell# Note: cell n