accessnash

def isPalindrome(a,b):
    s = str(a*b)
    if s == s[::-1]:
        return True
    
palinlist = []    
for i in range(999, 101, -1):
    for j in range(999, 101, -1):
        num = i*j
        if isPalindrome(i,j):

accessnash

pkg load statistics 
data = load('vlss.txt');
y = data(:,4);
n= length(y);
logtexp = data(:,3);
X = [ones(n,1) logtexp];
beta = inv(X'*X)*X'*y; %Beta coefficients
yhat = X*beta;
e = y - yhat;
k = size(X,2); 

accessnash

N= 5000;
n = 2;
lambda = 1;
mu = 3;
p = ones([n,1]);
x = zeros(n,1);
e = zeros(n,1);
y = zeros(n,1);
for i =1:N;
   x(:,i) = exprnd(lambda,n,1);

accessnash

data = load('carsdata2.txt');
y = data(:, 1)/1000; 
n = length(y); % Number of observations
predvar = data(:, 2:8); % Features
Reliability = predvar(:,1);
OwnerSat = predvar(:,2);
RoadScore = predvar(:, 3);
MPG = predvar(:, 4);
Zeroto60 = predvar(:,5);
NotReliable = (Reliability==4|Reliability==5);

accessnash

delta0 <- c(0,0,0,0)
xbar1 <- (c(2.287, 12.600, 0.347, 14.830))
xbar2 <- (c(2.404, 7.155, 0.524,  12.840))
S1 <- matrix(c(.459, .254, -.026, -.244, .254, 27.465, -.589, -.267, -.206, -.589, .030, .102, -.244, -.267, .102, 6.854), nc=4)
S2 <- matrix(c(.944, -.089, .002, -.719, -.089, 16.432, -.400, 19.044, .002, -.400, .024, -.094, -.719, 19.044, -.094, 61.854), nc =4)
n1 <- n2 <- 20
p <- length(delta0)
alpha <- 0.05
Spooled <- ((n1-1)*S1 + (n2-1)*S2 )/(n1+n2-2)
T2 <- (1/n1+1/n2)^{-1}*t(xbar1- xbar2 - delta0)%*%solve(Spooled)%*%(xbar1 - xbar2)

accessnash

S0 = 100.0;
X = 110.0;
H = 85.0;
R = 1;
sigma = 0.25;
r = 0.05;
T = 1.0;
M = 100;
dt=T/M; % Time step
mudt=(r-0.5*sigma^2)*dt;

accessnash

data1 <-read.table("C:/Users/DASA0/Desktop/Stat 524/wichern data/T6-5.dat", sep="")
data2 <-read.table("C:/Users/DASA0/Desktop/Stat 524/wichern data/T6-6.dat", sep="")
data1 <- as.data.frame(data1) 
data2 <- as.data.frame(data2)

xbar1 <- apply(data1, 2, mean)
xbar2 <- apply(data2, 2, mean)
t <- c(0, 1, 2, 3)
df <- data.frame(x=t, y=xbar1, type='Profile 1') 
df <- rbind(df, data.frame(x=t, y=xbar2, type='Profile 2'))

accessnash

numberparity <- read.csv("C:/Users/DASA0/Desktop/Stat 524/numberparity.csv", sep=",")
numberparity <- as.matrix(numberparity) 
C <- matrix(c(-1, -1, -1, 1, -1, 1, -1, 1, 1, 1, 1, -1), nc=4)
datanew <- numberparity%*%t(C)

onesampletest<- function(data=data, mu0=mu0, alpha=alpha){
  #computing Hotelling's T-square and the critical value
  xbar <- apply(data, 2, mean)
  S <- cov(data)
  

accessnash

delta0 <- c(0,0)
xbar1 <- (c(204.4, 556.6))
xbar2 <- (c(130.0, 355.0))
S1 <- matrix(c(13825.3, 23823.4, 23823.4, 73107.4), nc=2)
S2 <- matrix(c(8632.0, 19616.7, 19616.7, 55964.5), nc=2)
n1 <- 45
n2 <- 55
p <- 2
alpha <- 0.05
Spooled <- ((n1-1)*S1 + (n2-1)*S2 )/(n1+n2-2)

accessnash

perspdata <- read.csv("C:/Users/DASA0/Desktop/Stat 524/Perspiration.csv", sep=",")
perspdata <- as.data.frame(perspdata) 
names(perspdata) <- c("Sweat Rate", "Sodium", "Potassium")

xbar <- sapply(perspdata, mean) #calculate mean for each variable

x1 <- perspdata[,1, drop=TRUE]
x2 <- perspdata[,2, drop=TRUE]
x3 <- perspdata[,3, drop=TRUE]