M2_Act2

library(readr)
bd <- read.csv("Indicadores.csv")

bd1<-bd
bd1$Periodos <- as.Date(bd1$Periodos,"%Y/%m/%d")

summary(bd1)

##     Periodos            Inflacion      
##  Min.   :2000-01-01   Min.   :-1.0100  
##  1st Qu.:2005-10-01   1st Qu.: 0.2000  
##  Median :2011-07-01   Median : 0.4100  
##  Mean   :2011-07-02   Mean   : 0.3822  
##  3rd Qu.:2017-04-01   3rd Qu.: 0.5900  
##  Max.   :2023-01-01   Max.   : 1.7000

library(ggplot2)

ggplot(bd1,aes(x=Inflacion)) + geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

plot(density(bd1$Inflacion))

qqnorm(bd1$Inflacion)
qqline(bd1$Inflacion)

library("car")

## Loading required package: carData

qqPlot(bd1$Inflacion)

## [1] 244 205

shapiro.test(bd1$Inflacion)

## 
##  Shapiro-Wilk normality test
## 
## data:  bd1$Inflacion
## W = 0.97565, p-value = 0.0001144

library(nortest)

ad.test(bd1$Inflacion)

## 
##  Anderson-Darling normality test
## 
## data:  bd1$Inflacion
## A = 2.1124, p-value = 2.219e-05

cvm.test(bd1$Inflacion)

## 
##  Cramer-von Mises normality test
## 
## data:  bd1$Inflacion
## W = 0.35235, p-value = 8.464e-05

lillie.test(bd1$Inflacion)

## 
##  Lilliefors (Kolmogorov-Smirnov) normality test
## 
## data:  bd1$Inflacion
## D = 0.075203, p-value = 0.0006699

pearson.test(bd1$Inflacion)

## 
##  Pearson chi-square normality test
## 
## data:  bd1$Inflacion
## P = 37.906, p-value = 0.001561

sf.test(bd1$Inflacion)

## 
##  Shapiro-Francia normality test
## 
## data:  bd1$Inflacion
## W = 0.97271, p-value = 9.179e-05