Data Analysis

install.packages("tidyverse", repos="http://cran.us.r-project.org")

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install.packages("conflicted", repos="http://cran.us.r-project.org")

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library(tidyverse)

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library(conflicted)

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data(women)
head(women)

##   height weight
## 1     58    115
## 2     59    117
## 3     60    120
## 4     61    123
## 5     62    126
## 6     63    129

dim(women)

## [1] 15  2

ggplot(aes(x=height, y= weight), data = women) +
  geom_point(size=3, alpha=.5)+
    geom_smooth(method = lm, se=F)+
      theme_bw()+
        labs(title ="Weight explained by height in women",
          x="Height (Explanatory or Independent variable)",
             y="Weight (Outcome or Dependent variable)")

## `geom_smooth()` using formula = 'y ~ x'

###############################################################################
#The basics of linear relationship


#Idea behind liner model is : Something about change in X(Explanatory or Independent variable) variable will be explained by Y(Outcome or dependent variable)

#Close relationship might exist here, for example: As women get taller they aslo get heavier in weight.
#In this data sample there is liner relationship exist as one go-up another go-up.

#How much change in weight can be explained by change in height.