SirmaXX

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title: "R Notebook"
output: html_notebook
---


# Chapter 10  Canonical correlation Analysis  (CH10 p 539) 

Chapter is related to Partitioning the covariance matrix (p73 ch2) .(ABbrevation :kısaltma )

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# Multivariate Statisttics 7.Week Lecture Notes

# Multivariate Multiple Regression Analysis

You can find this topic in the chapter 8 in the book.

1. Multiple Regression

$$  Y = X\beta +\varepsilon ~~\to \text{Regression Model} $$

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#y=ax+b^

  #y<-matrix(c(12,10,8,11,6,7,2,3,3),nrow=9,ncol=1)
#y

#x<-matrix(0:8,nrow=9,ncol=1)
#x

#b<-solve(t(x)%*%x)%*%t(x)%*%y

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#varyans kovaryans matrisi tanımlayalım
Sigma<-matrix(c(19,30,2,12,30,57,5,23,2,5,38,47,12,23,43,68),ncol = 4,byrow = True)
#loading matrix
lodMatrix<-matrix(c(4,1,7,2,-1,6,1,8),ncol = 2,byrow = TRUE)
#loading matrisinin transpozu
TransLodMatrix<-t(lodMatrix)
#matrislerin çarpımı
multiple<-lodMatrix %*%  TransLodMatrix
#sigma ile ll'  çarpımını çıkarıyoruz
Result=Sigma-multiple

SirmaXX

companies1 <- c("BİST100","USD","ALTIN","ISDMR1","ISDMR")
cash1<-c(17626,28650,38122,75699,62652)
cash1<-cash1[order(cash1)]
df1<-data.frame(companies1)


barplot(cash1, names.arg= companies1, 
        main = "Şirket değerleri",col=rainbow(3)
        ,xlab = "Şirket isimleri", ylab = "değeri")

SirmaXX

library(reshape2)
library(ggplot2)
#yapılacak işler/görevler
tasks <- c("Literatür taraması", "Veri toplanması", "Veri Analizi", "Rapor ve veri görselleştirmesi")
#görevlerimizi bir dataframe'a aktarıyoruz
dfr <- data.frame(
  name        = factor(tasks, levels = tasks),
  start.date  = as.Date(c("2010-08-24", "2010-10-31", "2010-11-01", "2011-02-14")),
  end.date    = as.Date(c("2010-10-31", "2010-12-14", "2011-02-28", "2011-04-30")),
  is.critical = c(TRUE, FALSE, FALSE, TRUE)

SirmaXX

public class Friends {
    private String Name;
    private Friends next;
}
public class Person extends Friends {
    private String Name;
    private Integer HitCount;
    public Friend FriendList;
    public Person(String n) {
        Name = n;

SirmaXX

#Kütüphaneleri ekleyelim
library(palmerpenguins)
library(ggplot2)
library(tidyverse)
library(corrplot)
library(GGally)
library(gapminder)
data(package = 'palmerpenguins')

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from peewee import *
db = SqliteDatabase('e_commerce.db')

# Basemodel dediğimiz şey şu en temel model olarak yazıyoruz.

class BaseModel(Model):
    class Meta:
        database = db

class Categories(BaseModel):

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number = (1,2,3,4,5,6,7,8,9,10)

def odd(number):
    if number % 2==0:
        return True
    else :
        return False

errorcount=1
domaincount=1