x <- c(151,174,138,186,128,136,179,163,152,131)
x
y <- c(63,81,56,91,47,57,76,72,62,48)
y
r <- lm(y~x)
r
;
r
r
Summary(r)
summary(r)
x
y
r <- lm(y~x)
summary(r)
plot(x,y,main = "Linear Regression",col="blue")
plot(x,main = "Linear Regression",col="blue")
plot(x,y,main = "Linear Regression",col="blue")
plot(x,y)
plot(x)
plot(x,y,main = "Linear Regression",col="blue")
plot(x,y,main = "Linear Regression",col="blue")
plot(x,y,main = "Linear Regression",col="blue",type = "h")
plot(x,y,main = "Linear Regression",col="blue",type = "p")
plot(x,y,main = "Linear Regression",col="blue",type = "o")
plot(x,y,main = "Linear Regression",col="blue",type = "h")
plot(x,y,main = "Linear Regression",col="blue",type = "h")
plot(x,y,main = "Linear Regression",col="blue",type = "o")
plot(x,y,main = "Linear Regression",col="blue",type = "p")
attach(mtcars)
input <- mtcars[,c("mpg","disp","hp","wt")]
head(input)
plot(input)
i <- mtcars[,c("am","cyl","hp","wt")]
i
am.data=glm(formula = am ~ cyl + hp + wt,data= i,family = binomial)
summary(am.data)
i <- mtcars[,c("am","cyl","hp","wt")]
i <- mtcars[,c("am","cyl","hp","wt")]
i
summary(am.data)
i <- mtcars[,c("am","cyl","hp","wt")]
head(i)
am.data=glm(formula = am ~ cyl + hp + wt,data= i,family = binomial)
summary(am.data)
plot(i)
install.packages("survival")
library("survival")
head(pbc)
install.packages("DBI")
install.packages("binom")
dbinom(2,5,0.1)
sum(dbinom(0:2,5,0.1))
sum(dbinom(2:5,5,0.1))
x <- c(200,450,)
x <- c(200,450,300,1500,700,44)
x
median(x)
mean(x)
sd(x)
mode(x)
x^2
x
samples=sample(x,100*50,T)
samples=matrix(samples,100)
samplemean=apply(samples,1,mean)
length(samplemean)
hist(samplemean,col=rainbow(length(samplemean)))
hist(samplemean,col=rainbow(length(samplemean)),breaks = 5)
n=100
> xbar=64
> xbar=64 # sample mean
> xbar=64 #samplemean
> xbar
[1] 64
> sigma=3 #population sd
> alpha=0.05 # level if significance
> #h0:mu=67,ha:mu!=67
> #critical Z value method
> mu=67
> z=(xbar-mu)/(sigma/sort(n))
> z
[1] -100
> z=(xbar - mu) / (sigma / sort(n))
> # calculation critical values of Z
>
> > # calculation critical values of Z
> zc=qnorm(alpha/2)
> zc
[1] -1.959964
> zc=c(zc,-zc)
> zc
[1] -1.959964 1.959964
> #since calculated z values is beyond the critical z value hence.null hypothesis, rejected
> n=100
> xbar=39350
> sigma=3260
> alpha=0.05
> #H0:mu=40000,Ha:mu!=40000
> mu=40000
> z=(xbar-mu)/(sigma/sort(n))
> z
[1] -19.93865
> zc=qnorm(alpha/2)
> zc
[1] -1.959964
> xbar=40000
> mu=39250
> z=(xbar-mu)/(sigma/sort(n))
> z
[1] 23.00613
> sigma
[1] 3260
> n
[1] 100
> xbar
[1] 40000
> mu
[1] 39250
> z<-(xbar-mu)/(sigma/sort(n))
> z
[1] 23.00613
> zc=qnorm(alpha/2)
> zc=c(zc,-zc)
> zc
[1] -1.959964 1.959964
> z1<-(xbar-mu)/(sigma/sort(n))
> z1
[1] 23.00613
> zc
[1] -1.959964 1.959964
> z=(40000- 39350)/(3260/sqrt(100))
> z
[1] 1.993865
>
hist(samplemean,col=rainbow(length(samplemean)),breaks = 5)
z=(xbar-mu)/(sigma/sort(n))
xbar=40000
> mu=39250
xbar=40000 ;mu=39250
z=(xbar-mu)/(sigma/sort(n))
z=(40000- 39350)/(3260/sqrt(100))
z
zc=qnorm(alpha/2)
zc=c(zc,-zc);
zc
zc=qnorm(alpha/2)
zc=c(zc,-zc)
zc=qnorm(alpha/2)
zc=qnorm(alpha/2)
alpha=0.05
attach(mtcars)
input <- mtcars[,c("am","cyl","hp","wt")]
input
head(input)
attach(mtcars)
am.data <- glm(am~cly+hp+wt,data = input,family = binomial)
am.data <- glm(am~cly+hp+wt,data = input,family = "bionimal")
am.data <- glm(am~cly+hp+wt,data = input,family = "binomial")
am.data <- glm(am~cly+hp+wt,family = "binomial",data = input)
am.data <- glm(am~cyl+hp+wt,family = "binomial",data = input)
am.data <- glm(am~cyl+hp+wt,family = "binomial",data = input)
am.data
summary(am.data)
savehistory("C:/Users/Rakesh vadhel/Desktop/a.Rhistory")
